![NATURE BIOTECHNOLOGY VOLUME 35 NUMBER 10 OCTOBER 2017 897
23andMe wades further into drug discovery
Direct-to-consumer genetics testing com-
pany 23andMe is advancing its drug dis-
covery efforts with a $250 million financing
round announced in September. The
Mountain View, California–based firm
plans to use the funds for its own therapeu-
tics division aimed at mining the company’s
database for novel drug targets, in addition
to its existing consumer genomics business
and genetic research platform. At the same
time, the company has strengthened ongo-
ing partnerships with Pfizer and Roche, and
inked a new collaboration with Lundbeck—
all are keen to incorporate 23andMe’s human
genetics data cache into their discovery and
clinical programs.
It was over a decade ago that Icelandic
company deCODE Genetics pioneered
genetics-driven drug discovery. The
Reykjavik-based biotech’s DNA database of
140,000 Icelanders, which Amgen bought in
2012 (Nat. Biotechnol. 31, 87–88, 2013), was
set up to identify genes associated with dis-
ease. But whereas the bedrock of deCODE’s
platform was the health records stretching
back over a century, the value in 23andMe’s
platform lies instead in its database of more
than 2 million genotyped customers, and
the reams of phenotypic information par-
ticipants collect at home by online surveys
of mood, cognition and even food intake.
For Danish pharma Lundbeck, a partner-
ship signed in August with 23andMe and
think-tank Milken Institute will provide a
fresh look at major depressive disorder and
bipolar depression. The collaboration study-
ing 25,000 participants will link genomics
with complete cognitive tests and surveys
taken over nine months, providing an almost
continuous monitoring of participants’
symptoms. “Cognition is a key symptom in
depression,” says Niels Plath, vice president
for synaptic transmission at Copenhagen-
based Lundbeck. But the biological processes
leading to depression are poorly understood,
and the condition is difficult to classify as
it includes a broad population of patients.
“If we could use genetic profiling to sort
people into groups and link to biology, we
could identify new drug targets, novel path-
ways and protein networks. With 23andMe,
we can combine the genetic profiling with
symptomatic presentation,” says Plath. An
approach like this leapfrogs the traditional
paradigm of mouse models and cell-based
assays for drug discovery. “Our scientific
hypotheses must come from patient-derived
information,” says Plath. “It could be pheno-
type, it could be genetic.”
Drug maker Roche has been taking advan-
tage of 23andMe’s data cache for several years,
and its collaborations are yielding results. In
September, researchers from the Basel-based
pharma’s wholly owned Genentech subsid-
iary, in partnership with 23andMe and oth-
ers, published a paper showcasing 17 new
Parkinson’s disease risk loci that could be
potential targets for therapeutics (Nat. Genet.
http://dx.doi.org/10.1038/ng.3955, 2017).
A year earlier, in August 2016, scientists
at New York–based Pfizer, 23andMe and
Massachusetts General Hospital announced
that they had identified 15 genetic regions
linked to depression (Nat. Genet. 48, 1031–
1036, 2016). A 23andMe spokesperson this
week called that paper a “landmark,” because
it was the first study to uncover 17 variants
associated with major depressive disorder.
Ashley Winslow, who was corresponding
author on the 2016 Nature Genetics paper, and
who used to work at Pfizer, says, “Initially,
the focus was on using the database to either
confirm [or refute] the findings established
by traditional, clinical methods of ascertain-
ment.” It soon occurred to the investigators
that they could move beyond traditional
association studies and do discovery work in
indications that to date had “not been well
powered,” such as major depression, espe-
cially since some of 23andMe’s questionnaires
specifically asked if subjects had once been
clinically diagnosed.
“I think [the database is] of particular
interest for psychiatric disorders because
the medications just have such a poor track
record of not working,” says Winslow, now
senior director of translational research and
portfolio development at the University of
Pennsylvania’s Orphan Disease Center in
Philadelphia. “23andMe offered us a fresh
new look.”
Winslow thinks there is a “powerful
shift” under way in pharma as it recognizes
the benefits of rooting target discovery in
human-derived data. “You still have to do
the work-up through cell-line screening or
animals at some point, but the starting point
being human-derived data is hugely impor-
tant.”
Justin Petrone Tartu, EstoniaBeyond consumer genetics: 23andMe sells access to its database to drug companies.
KristofferTripplaar/AlamyStockPhoto
N E W S
©2017NatureAmerica,Inc.,partofSpringerNature.Allrightsreserved.](https://image.slidesharecdn.com/digitaltherapeutics-180401151009/75/slide-26-2048.jpg)
![DeepFace: Closing the Gap to Human-Level
Performance in FaceVerification
Taigman,Y. et al. (2014). DeepFace: Closing the Gap to Human-Level Performance in FaceVerification, CVPR’14.
Figure 2. Outline of the DeepFace architecture. A front-end of a single convolution-pooling-convolution filtering on the rectified input, followed by three
locally-connected layers and two fully-connected layers. Colors illustrate feature maps produced at each layer. The net includes more than 120 million
parameters, where more than 95% come from the local and fully connected layers.
very few parameters. These layers merely expand the input
into a set of simple local features.
The subsequent layers (L4, L5 and L6) are instead lo-
cally connected [13, 16], like a convolutional layer they ap-
ply a filter bank, but every location in the feature map learns
a different set of filters. Since different regions of an aligned
image have different local statistics, the spatial stationarity
The goal of training is to maximize the probability of
the correct class (face id). We achieve this by minimiz-
ing the cross-entropy loss for each training sample. If k
is the index of the true label for a given input, the loss is:
L = log pk. The loss is minimized over the parameters
by computing the gradient of L w.r.t. the parameters and
Human: 95% vs. DeepFace in Facebook: 97.35%
Recognition Accuracy for Labeled Faces in the Wild (LFW) dataset (13,233 images, 5,749 people)](https://image.slidesharecdn.com/digitaltherapeutics-180401151009/75/slide-51-2048.jpg)
![FaceNet:A Unified Embedding for Face
Recognition and Clustering
Schroff, F. et al. (2015). FaceNet:A Unified Embedding for Face Recognition and Clustering
Human: 95% vs. FaceNet of Google: 99.63%
Recognition Accuracy for Labeled Faces in the Wild (LFW) dataset (13,233 images, 5,749 people)
False accept
False reject
s. This shows all pairs of images that were
on LFW. Only eight of the 13 errors shown
he other four are mislabeled in LFW.
on Youtube Faces DB
ge similarity of all pairs of the first one
our face detector detects in each video.
False accept
False reject
Figure 6. LFW errors. This shows all pairs of images that were
incorrectly classified on LFW. Only eight of the 13 errors shown
here are actual errors the other four are mislabeled in LFW.
5.7. Performance on Youtube Faces DB
We use the average similarity of all pairs of the first one
hundred frames that our face detector detects in each video.
This gives us a classification accuracy of 95.12%±0.39.
Using the first one thousand frames results in 95.18%.
Compared to [17] 91.4% who also evaluate one hundred
frames per video we reduce the error rate by almost half.
DeepId2+ [15] achieved 93.2% and our method reduces this
error by 30%, comparable to our improvement on LFW.
5.8. Face Clustering
Our compact embedding lends itself to be used in order
to cluster a users personal photos into groups of people with
the same identity. The constraints in assignment imposed
by clustering faces, compared to the pure verification task,
lead to truly amazing results. Figure 7 shows one cluster in
a users personal photo collection, generated using agglom-
erative clustering. It is a clear showcase of the incredible
invariance to occlusion, lighting, pose and even age.
Figure 7. Face Clustering. Shown is an exemplar cluster for one
user. All these images in the users personal photo collection were
clustered together.
6. Summary
We provide a method to directly learn an embedding into
an Euclidean space for face verification. This sets it apart
from other methods [15, 17] who use the CNN bottleneck
layer, or require additional post-processing such as concate-
nation of multiple models and PCA, as well as SVM clas-
sification. Our end-to-end training both simplifies the setup
and shows that directly optimizing a loss relevant to the task
at hand improves performance.
Another strength of our model is that it only requires
False accept
False reject
Figure 6. LFW errors. This shows all pairs of images that were
incorrectly classified on LFW. Only eight of the 13 errors shown
here are actual errors the other four are mislabeled in LFW.
5.7. Performance on Youtube Faces DB
We use the average similarity of all pairs of the first one
hundred frames that our face detector detects in each video.
This gives us a classification accuracy of 95.12%±0.39.
Using the first one thousand frames results in 95.18%.
Compared to [17] 91.4% who also evaluate one hundred
frames per video we reduce the error rate by almost half.
DeepId2+ [15] achieved 93.2% and our method reduces this
error by 30%, comparable to our improvement on LFW.
5.8. Face Clustering
Our compact embedding lends itself to be used in order
to cluster a users personal photos into groups of people with
the same identity. The constraints in assignment imposed
by clustering faces, compared to the pure verification task,
Figure 7. Face Clustering. Shown is an exemplar cluster for one
user. All these images in the users personal photo collection were
clustered together.
6. Summary
We provide a method to directly learn an embedding into
an Euclidean space for face verification. This sets it apart
from other methods [15, 17] who use the CNN bottleneck
layer, or require additional post-processing such as concate-
nation of multiple models and PCA, as well as SVM clas-](https://image.slidesharecdn.com/digitaltherapeutics-180401151009/75/slide-52-2048.jpg)
![Targeting Ultimate Accuracy: Face
Recognition via Deep Embedding
Jingtuo Liu (2015) Targeting Ultimate Accuracy: Face Recognition via Deep Embedding
Human: 95% vs.Baidu: 99.77%
Recognition Accuracy for Labeled Faces in the Wild (LFW) dataset (13,233 images, 5,749 people)
3
Although several algorithms have achieved nearly perfect
accuracy in the 6000-pair verification task, a more practical
can achieve 95.8% identification rate, relatively reducing the
error rate by about 77%.
TABLE 3. COMPARISONS WITH OTHER METHODS ON SEVERAL EVALUATION TASKS
Score = -0.060 (pair #113) Score = -0.022 (pair #202) Score = -0.034 (pair #656)
Score = -0.031 (pair #1230) Score = -0.073 (pair #1862) Score = -0.091(pair #2499)
Score = -0.024 (pair #2551) Score = -0.036 (pair #2552) Score = -0.089 (pair #2610)
Method
Performance on tasks
Pair-wise
Accuracy(%)
Rank-1(%)
DIR(%) @
FAR =1%
Verification(%
)@ FAR=0.1%
Open-set
Identification(%
)@ Rank =
1,FAR = 0.1%
IDL Ensemble
Model
99.77 98.03 95.8 99.41 92.09
IDL Single Model 99.68 97.60 94.12 99.11 89.08
FaceNet[12] 99.63 NA NA NA NA
DeepID3[9] 99.53 96.00 81.40 NA NA
Face++[2] 99.50 NA NA NA NA
Facebook[15] 98.37 82.5 61.9 NA NA
Learning from
Scratch[4]
97.73 NA NA 80.26 28.90
HighDimLBP[10] 95.17 NA NA
41.66(reported
in [4])
18.07(reported
in [4])
• 6,000쌍의 얼굴 사진 중에 바이두의 인공지능은 불과 14쌍만을 잘못 판단
• 알고 보니 이 14쌍 중의 5쌍의 사진은 오히려 정답에 오류가 있었고,
실제로는 인공지능이 정확 (red box)](https://image.slidesharecdn.com/digitaltherapeutics-180401151009/75/slide-53-2048.jpg)
![AtomNet: A Deep Convolutional Neural Network for
Bioactivity Prediction in Structure-based Drug
Discovery
Izhar Wallach
Atomwise, Inc.
izhar@atomwise.com
Michael Dzamba
Atomwise, Inc.
misko@atomwise.com
Abraham Heifets
Atomwise, Inc.
abe@atomwise.com
Abstract
Deep convolutional neural networks comprise a subclass of deep neural networks
(DNN) with a constrained architecture that leverages the spatial and temporal
structure of the domain they model. Convolutional networks achieve the best pre-
dictive performance in areas such as speech and image recognition by hierarchi-
cally composing simple local features into complex models. Although DNNs have
been used in drug discovery for QSAR and ligand-based bioactivity predictions,
none of these models have benefited from this powerful convolutional architec-
ture. This paper introduces AtomNet, the first structure-based, deep convolutional
neural network designed to predict the bioactivity of small molecules for drug dis-
covery applications. We demonstrate how to apply the convolutional concepts of
feature locality and hierarchical composition to the modeling of bioactivity and
chemical interactions. In further contrast to existing DNN techniques, we show
that AtomNet’s application of local convolutional filters to structural target infor-
mation successfully predicts new active molecules for targets with no previously
known modulators. Finally, we show that AtomNet outperforms previous docking
approaches on a diverse set of benchmarks by a large margin, achieving an AUC
greater than 0.9 on 57.8% of the targets in the DUDE benchmark.
1 Introduction
Fundamentally, biological systems operate through the physical interaction of molecules. The ability
to determine when molecular binding occurs is therefore critical for the discovery of new medicines
and for furthering of our understanding of biology. Unfortunately, despite thirty years of compu-
tational efforts, computer tools remain too inaccurate for routine binding prediction, and physical
experiments remain the state of the art for binding determination. The ability to accurately pre-
dict molecular binding would reduce the time-to-discovery of new treatments, help eliminate toxic
molecules early in development, and guide medicinal chemistry efforts [1, 2].
In this paper, we introduce a new predictive architecture, AtomNet, to help address these challenges.
AtomNet is novel in two regards: AtomNet is the first deep convolutional neural network for molec-
ular binding affinity prediction. It is also the first deep learning system that incorporates structural
information about the target to make its predictions.
Deep convolutional neural networks (DCNN) are currently the best performing predictive models
for speech and vision [3, 4, 5, 6]. DCNN is a class of deep neural network that constrains its model
architecture to leverage the spatial and temporal structure of its domain. For example, a low-level
image feature, such as an edge, can be described within a small spatially-proximate patch of pixels.
Such a feature detector can share evidence across the entire receptive field by “tying the weights”
of the detector neurons, as the recognition of the edge does not depend on where it is found within
1
arXiv:1510.02855v1[cs.LG]10Oct2015](https://image.slidesharecdn.com/digitaltherapeutics-180401151009/75/slide-75-2048.jpg)
![AtomNet: A Deep Convolutional Neural Network for
Bioactivity Prediction in Structure-based Drug
Discovery
Izhar Wallach
Atomwise, Inc.
izhar@atomwise.com
Michael Dzamba
Atomwise, Inc.
misko@atomwise.com
Abraham Heifets
Atomwise, Inc.
abe@atomwise.com
Abstract
Deep convolutional neural networks comprise a subclass of deep neural networks
(DNN) with a constrained architecture that leverages the spatial and temporal
structure of the domain they model. Convolutional networks achieve the best pre-
dictive performance in areas such as speech and image recognition by hierarchi-
cally composing simple local features into complex models. Although DNNs have
been used in drug discovery for QSAR and ligand-based bioactivity predictions,
none of these models have benefited from this powerful convolutional architec-
ture. This paper introduces AtomNet, the first structure-based, deep convolutional
neural network designed to predict the bioactivity of small molecules for drug dis-
covery applications. We demonstrate how to apply the convolutional concepts of
feature locality and hierarchical composition to the modeling of bioactivity and
chemical interactions. In further contrast to existing DNN techniques, we show
that AtomNet’s application of local convolutional filters to structural target infor-
mation successfully predicts new active molecules for targets with no previously
known modulators. Finally, we show that AtomNet outperforms previous docking
approaches on a diverse set of benchmarks by a large margin, achieving an AUC
greater than 0.9 on 57.8% of the targets in the DUDE benchmark.
1 Introduction
Fundamentally, biological systems operate through the physical interaction of molecules. The ability
to determine when molecular binding occurs is therefore critical for the discovery of new medicines
and for furthering of our understanding of biology. Unfortunately, despite thirty years of compu-
tational efforts, computer tools remain too inaccurate for routine binding prediction, and physical
experiments remain the state of the art for binding determination. The ability to accurately pre-
dict molecular binding would reduce the time-to-discovery of new treatments, help eliminate toxic
molecules early in development, and guide medicinal chemistry efforts [1, 2].
In this paper, we introduce a new predictive architecture, AtomNet, to help address these challenges.
AtomNet is novel in two regards: AtomNet is the first deep convolutional neural network for molec-
ular binding affinity prediction. It is also the first deep learning system that incorporates structural
information about the target to make its predictions.
Deep convolutional neural networks (DCNN) are currently the best performing predictive models
for speech and vision [3, 4, 5, 6]. DCNN is a class of deep neural network that constrains its model
architecture to leverage the spatial and temporal structure of its domain. For example, a low-level
image feature, such as an edge, can be described within a small spatially-proximate patch of pixels.
Such a feature detector can share evidence across the entire receptive field by “tying the weights”
of the detector neurons, as the recognition of the edge does not depend on where it is found within
1
arXiv:1510.02855v1[cs.LG]10Oct2015
Smina 123 35 5 0 0
Table 3: The number of targets on which AtomNet and Smina exceed given adjusted-logAUC thresh-
olds. For example, on the CHEMBL-20 PMD set, AtomNet achieves an adjusted-logAUC of 0.3
or better for 27 targets (out of 50 possible targets). ChEMBL-20 PMD contains 50 targets, DUDE-
30 contains 30 targets, DUDE-102 contains 102 targets, and ChEMBL-20 inactives contains 149
targets.
To overcome these limitations we take an indirect approach. Instead of directly visualizing filters
in order to understand their specialization, we apply filters to input data and examine the location
where they maximally fire. Using this technique we were able to map filters to chemical functions.
For example, Figure 5 illustrate the 3D locations at which a particular filter from our first convo-
lutional layer fires. Visual inspection of the locations at which that filter is active reveals that this
filter specializes as a sulfonyl/sulfonamide detector. This demonstrates the ability of the model to
learn complex chemical features from simpler ones. In this case, the filter has inferred a meaningful
spatial arrangement of input atom types without any chemical prior knowledge.
Figure 5: Sulfonyl/sulfonamide detection with autonomously trained convolutional filters.
8
• 이미 알려진 단백질-리간드 3차원 결합 구조를 딥러닝(CNN)으로 학습
• 화학 결합 등에 대한 계산 없이도, 단백질-리간드 결합 여부를 계산
• 기존의 구조기반 예측 등 대비, 딥러닝으로 더 정확히 예측하였음](https://image.slidesharecdn.com/digitaltherapeutics-180401151009/75/slide-76-2048.jpg)
![AtomNet: A Deep Convolutional Neural Network for
Bioactivity Prediction in Structure-based Drug
Discovery
Izhar Wallach
Atomwise, Inc.
izhar@atomwise.com
Michael Dzamba
Atomwise, Inc.
misko@atomwise.com
Abraham Heifets
Atomwise, Inc.
abe@atomwise.com
Abstract
Deep convolutional neural networks comprise a subclass of deep neural networks
(DNN) with a constrained architecture that leverages the spatial and temporal
structure of the domain they model. Convolutional networks achieve the best pre-
dictive performance in areas such as speech and image recognition by hierarchi-
cally composing simple local features into complex models. Although DNNs have
been used in drug discovery for QSAR and ligand-based bioactivity predictions,
none of these models have benefited from this powerful convolutional architec-
ture. This paper introduces AtomNet, the first structure-based, deep convolutional
neural network designed to predict the bioactivity of small molecules for drug dis-
covery applications. We demonstrate how to apply the convolutional concepts of
feature locality and hierarchical composition to the modeling of bioactivity and
chemical interactions. In further contrast to existing DNN techniques, we show
that AtomNet’s application of local convolutional filters to structural target infor-
mation successfully predicts new active molecules for targets with no previously
known modulators. Finally, we show that AtomNet outperforms previous docking
approaches on a diverse set of benchmarks by a large margin, achieving an AUC
greater than 0.9 on 57.8% of the targets in the DUDE benchmark.
1 Introduction
Fundamentally, biological systems operate through the physical interaction of molecules. The ability
to determine when molecular binding occurs is therefore critical for the discovery of new medicines
and for furthering of our understanding of biology. Unfortunately, despite thirty years of compu-
tational efforts, computer tools remain too inaccurate for routine binding prediction, and physical
experiments remain the state of the art for binding determination. The ability to accurately pre-
dict molecular binding would reduce the time-to-discovery of new treatments, help eliminate toxic
molecules early in development, and guide medicinal chemistry efforts [1, 2].
In this paper, we introduce a new predictive architecture, AtomNet, to help address these challenges.
AtomNet is novel in two regards: AtomNet is the first deep convolutional neural network for molec-
ular binding affinity prediction. It is also the first deep learning system that incorporates structural
information about the target to make its predictions.
Deep convolutional neural networks (DCNN) are currently the best performing predictive models
for speech and vision [3, 4, 5, 6]. DCNN is a class of deep neural network that constrains its model
architecture to leverage the spatial and temporal structure of its domain. For example, a low-level
image feature, such as an edge, can be described within a small spatially-proximate patch of pixels.
Such a feature detector can share evidence across the entire receptive field by “tying the weights”
of the detector neurons, as the recognition of the edge does not depend on where it is found within
1
arXiv:1510.02855v1[cs.LG]10Oct2015
• 이미 알려진 단백질-리간드 3차원 결합 구조를 딥러닝(CNN)으로 학습
• 화학 결합 등에 대한 계산 없이도, 단백질-리간드 결합 여부를 계산
• 기존의 구조기반 예측 등 대비, 딥러닝으로 더 정확히 예측하였음](https://image.slidesharecdn.com/digitaltherapeutics-180401151009/75/slide-77-2048.jpg)
![RESEARCH ARTICLE
A pilot study to determine the feasibility of
enhancing cognitive abilities in children with
sensory processing dysfunction
Joaquin A. Anguera1,2☯
*, Anne N. Brandes-Aitken1☯
, Ashley D. Antovich1
, Camarin
E. Rolle1
, Shivani S. Desai1
, Elysa J. Marco1,2,3
1 Department of Neurology, University of California, San Francisco, United States of America, 2 Department
of Psychiatry, University of California, San Francisco, United States of America, 3 Department of Pediatrics,
University of California, San Francisco, United States of America
☯ These authors contributed equally to this work.
* joaquin.anguera@ucsf.edu
Abstract
Children with Sensory Processing Dysfunction (SPD) experience incoming information in
atypical, distracting ways. Qualitative challenges with attention have been reported in these
children, but such difficulties have not been quantified using either behavioral or functional
neuroimaging methods. Furthermore, the efficacy of evidence-based cognitive control inter-
ventions aimed at enhancing attention in this group has not been tested. Here we present
work aimed at characterizing and enhancing attentional abilities for children with SPD. A
sample of 38 SPD and 25 typically developing children were tested on behavioral, neural,
and parental measures of attention before and after a 4-week iPad-based at-home cognitive
remediation program. At baseline, 54% of children with SPD met or exceeded criteria on a
parent report measure for inattention/hyperactivity. Significant deficits involving sustained
attention, selective attention and goal management were observed only in the subset of
SPD children with parent-reported inattention. This subset of children also showed reduced
midline frontal theta activity, an electroencephalographic measure of attention. Following
the cognitive intervention, only the SPD children with inattention/hyperactivity showed both
improvements in midline frontal theta activity and on a parental report of inattention. Notably,
33% of these individuals no longer met the clinical cut-off for inattention, with the parent-
reported improvements persisting for 9 months. These findings support the benefit of a
targeted attention intervention for a subset of children with SPD, while simultaneously
highlighting the importance of having a multifaceted assessment for individuals with neuro-
developmental conditions to optimally personalize treatment.
Introduction
Five percent of all children suffer from Sensory Processing Dysfunction (SPD)[1], with these
individuals exhibiting exaggerated aversive, withdrawal, or seeking behaviors associated with
sensory inputs [2]. These sensory processing differences can have significant and lifelong con-
sequences for learning and social abilities, and are often shared by children who meet
PLOS ONE | https://doi.org/10.1371/journal.pone.0172616 April 5, 2017 1 / 19
a1111111111
a1111111111
a1111111111
a1111111111
a1111111111
OPEN ACCESS
Citation: Anguera JA, Brandes-Aitken AN, Antovich
AD, Rolle CE, Desai SS, Marco EJ (2017) A pilot
study to determine the feasibility of enhancing
cognitive abilities in children with sensory
processing dysfunction. PLoS ONE 12(4):
e0172616. https://doi.org/10.1371/journal.
pone.0172616
Editor: Jacobus P. van Wouwe, TNO,
NETHERLANDS
Received: October 5, 2016
Accepted: February 1, 2017
Published: April 5, 2017
Copyright: © 2017 Anguera et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: All relevant data are
within the paper and its Supporting Information
files.
Funding: This work was supported by the
Mickelson-Brody Family Foundation, the Wallace
Research Foundation, the James Gates Family
Foundation, the Kawaja-Holcombe Family
Foundation (EJM), and the SNAP 2015 Crowd
funding effort.
•감각처리장애(SPD)를 가진 소아 환자 중 ADHD를 가진 20명에 대해서 실험
•4주 동안 (주당 5일, 25분)Project EVO 게임을 하게 한 결과,
•20명 중 7명이 큰 개선을 보여서 더 이상 ADHD의 범주에 들지 않게 됨
•사용 후 적어도 9개월 동안 효과가 지속되었음
Fig 4. Transfer effect on behavioral and parent report measures. Pre and post (A) response time (B) and resp
revealing within group change. Error bars indicate standard error of the mean. Within group main effects of session
= p .05, ** =.p .01. Sun symbols indicate statistically significant instances where SPD+IA post-training performa
TDC group prior to training. (C) Vanderbilt parent report inattention change bar plot (calculated by pre-post margina
significant group x session interaction. Error bars indicate standard error of the mean. All group x session interactio
stars (* = p .05, ** =.p .01) on bar graph.
https://doi.org/10.1371/journal.pone.0172616.g004
PLOS ONE | https://doi.org/10.1371/journal.pone.0172616 April 5, 2017](https://image.slidesharecdn.com/digitaltherapeutics-180401151009/75/slide-220-2048.jpg)
Uploaded byYoon Sup Choi
의료의 미래, 디지털 헬스케어: 신약개발을 중심으로
- The traditional business model of personal genomics companies sees individuals pay to sequence their genomes and receive analysis results, while the companies keep the genomic data and sell it to pharmaceutical companies. However, this model has limitations in addressing high sequencing costs for individuals, lack of individual control over their data, and lack of incentives. - The proposed Nebula model uses blockchain technology to connect individuals directly with data buyers, eliminating personal genomics companies as middlemen. This is intended to reduce sequencing costs for individuals, give them control over their genomic data and how it is used, and provide incentives. - The model aims to satisfy both individuals, by addressing the above issues, and data buyers' needs around data availability, acquisition, and
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의료의 미래, 디지털 헬스케어: 신약개발을 중심으로
- 1. Professor, SAHIST, SungkyunkwanUniversity Director, Digital Healthcare Institute Yoon Sup Choi, Ph.D. 디지털 헬스케어, 의료의 미래 신약 개발을 중심으로
- 2. “It's in Apple'sDNA that technology alone is not enough. It's technology married with liberal arts.”
- 3. The Convergence ofIT, BT and Medicine
- 6. Inevitable Tsunami ofChange
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- 8. •2017년은 역대 디지털헬스케어 스타트업 펀딩 중 최대의 해. •투자횟수와 개별 투자의 규모도 역대 최고 수준을 기록 •$100m 을 넘는 mega deal 도 8건이 있었으며, •이에 따라 기업가치 $1b이 넘는 유니콘 기업들이 상당수 생겨남. https://rockhealth.com/reports/2017-year-end-funding-report-the-end-of-the-beginning-of-digital-health/
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- 10. •최근 3년 동안Merck, J&J, GSK 등의 제약사들의 디지털 헬스케어 분야 투자 급증 •2015-2016년 총 22건의 deal (=2010-2014년의 5년간 투자 건수와 동일) •Merck 가 가장 활발: 2009년부터 Global Health Innovation Fund 를 통해 24건 투자 ($5-7M) •GSK 의 경우 2014년부터 6건 (via VC arm, SR One): including Propeller Health
- 11. AnalysisTarget Discovery AnalysisLeadDiscovery Clinical Trial Post Market Surveillance Digital Healthcare in Drug Development
- 12. AnalysisTarget Discovery AnalysisLeadDiscovery Clinical Trial Post Market Surveillance Digital Healthcare in Drug Development •개인 유전 정보 분석 •블록체인 기반 유전체 거래 플랫폼
- 14. Results within 6-8weeksA little spit is all it takes! DTC Genetic TestingDirect-To-Consumer
- 15. 120 Disease Risk 21Drug Response 49 Carrier Status 57Traits $99
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- 20. Inherited Conditions 혈색소증은 유전적원인으로 철에 대한 체내 대사에 이상이 생겨 음식을 통해 섭취한 철이 너무 많이 흡수되는 질환입니다. 너무 많이 흡수된 철 은 우리 몸의 여러 장기, 특히 간, 심장 및 췌장에 과다하게 축적되며 이 들 장기를 손상시킴으로써 간질환, 심장질환 및 악성종양을 유발합니다.
- 21. Traits 음주 후 얼굴이붉어지는가 쓴 맛을 감지할 수 있나 귀지 유형 눈 색깔 곱슬머리 여부 유당 분해 능력 말라리아 저항성 대머리가 될 가능성 근육 퍼포먼스 혈액형 노로바이러스 저항성 HIV 저항성 흡연 중독 가능성
- 22. genetic factor vs.environmental factor
- 23. 1,200,000 1,000,000 900,000 850,000 650,000 500,000 400,000 300,000 250,000 180,000 100,000 2007-11 2011-06 2011-10 2012-04 2012-10 2013-04 2013-06 2013-09 2013-12 2014-10 2015-02 2015-05 2015-06 2016-02 0 Customer growth of23andMe 2017-04 2,000,000 Digital Healthcare Institute Director,Yoon Sup Choi, PhD yoonsup.choi@gmail.com
- 24. https://www.23andme.com/slideshow/research/ 고객의 자발적인 참여에의한 유전학 연구 깍지를 끼면 어느 쪽 엄지가 위로 오는가? 아침형 인간? 저녁형 인간? 빛에 노출되었을 때 재채기를 하는가? 근육의 퍼포먼스 쓴 맛 인식 능력 음주 후 얼굴이 붉어지나? 유당 분해 효소 결핍? 고객의 81%가 10개 이상의 질문에 자발적 답변 매주 1 million 개의 data point 축적 The More Data, The Higher Accuracy!
- 25. January 13, 2015January6, 2015 Data Business
- 26. NATURE BIOTECHNOLOGY VOLUME35 NUMBER 10 OCTOBER 2017 897 23andMe wades further into drug discovery Direct-to-consumer genetics testing com- pany 23andMe is advancing its drug dis- covery efforts with a $250 million financing round announced in September. The Mountain View, California–based firm plans to use the funds for its own therapeu- tics division aimed at mining the company’s database for novel drug targets, in addition to its existing consumer genomics business and genetic research platform. At the same time, the company has strengthened ongo- ing partnerships with Pfizer and Roche, and inked a new collaboration with Lundbeck— all are keen to incorporate 23andMe’s human genetics data cache into their discovery and clinical programs. It was over a decade ago that Icelandic company deCODE Genetics pioneered genetics-driven drug discovery. The Reykjavik-based biotech’s DNA database of 140,000 Icelanders, which Amgen bought in 2012 (Nat. Biotechnol. 31, 87–88, 2013), was set up to identify genes associated with dis- ease. But whereas the bedrock of deCODE’s platform was the health records stretching back over a century, the value in 23andMe’s platform lies instead in its database of more than 2 million genotyped customers, and the reams of phenotypic information par- ticipants collect at home by online surveys of mood, cognition and even food intake. For Danish pharma Lundbeck, a partner- ship signed in August with 23andMe and think-tank Milken Institute will provide a fresh look at major depressive disorder and bipolar depression. The collaboration study- ing 25,000 participants will link genomics with complete cognitive tests and surveys taken over nine months, providing an almost continuous monitoring of participants’ symptoms. “Cognition is a key symptom in depression,” says Niels Plath, vice president for synaptic transmission at Copenhagen- based Lundbeck. But the biological processes leading to depression are poorly understood, and the condition is difficult to classify as it includes a broad population of patients. “If we could use genetic profiling to sort people into groups and link to biology, we could identify new drug targets, novel path- ways and protein networks. With 23andMe, we can combine the genetic profiling with symptomatic presentation,” says Plath. An approach like this leapfrogs the traditional paradigm of mouse models and cell-based assays for drug discovery. “Our scientific hypotheses must come from patient-derived information,” says Plath. “It could be pheno- type, it could be genetic.” Drug maker Roche has been taking advan- tage of 23andMe’s data cache for several years, and its collaborations are yielding results. In September, researchers from the Basel-based pharma’s wholly owned Genentech subsid- iary, in partnership with 23andMe and oth- ers, published a paper showcasing 17 new Parkinson’s disease risk loci that could be potential targets for therapeutics (Nat. Genet. http://dx.doi.org/10.1038/ng.3955, 2017). A year earlier, in August 2016, scientists at New York–based Pfizer, 23andMe and Massachusetts General Hospital announced that they had identified 15 genetic regions linked to depression (Nat. Genet. 48, 1031– 1036, 2016). A 23andMe spokesperson this week called that paper a “landmark,” because it was the first study to uncover 17 variants associated with major depressive disorder. Ashley Winslow, who was corresponding author on the 2016 Nature Genetics paper, and who used to work at Pfizer, says, “Initially, the focus was on using the database to either confirm [or refute] the findings established by traditional, clinical methods of ascertain- ment.” It soon occurred to the investigators that they could move beyond traditional association studies and do discovery work in indications that to date had “not been well powered,” such as major depression, espe- cially since some of 23andMe’s questionnaires specifically asked if subjects had once been clinically diagnosed. “I think [the database is] of particular interest for psychiatric disorders because the medications just have such a poor track record of not working,” says Winslow, now senior director of translational research and portfolio development at the University of Pennsylvania’s Orphan Disease Center in Philadelphia. “23andMe offered us a fresh new look.” Winslow thinks there is a “powerful shift” under way in pharma as it recognizes the benefits of rooting target discovery in human-derived data. “You still have to do the work-up through cell-line screening or animals at some point, but the starting point being human-derived data is hugely impor- tant.” Justin Petrone Tartu, EstoniaBeyond consumer genetics: 23andMe sells access to its database to drug companies. KristofferTripplaar/AlamyStockPhoto N E W S ©2017NatureAmerica,Inc.,partofSpringerNature.Allrightsreserved.
- 27. Human genomes arebeing sequenced at an ever-increasing rate. The 1000 Genomes Project has aggregated hundreds of genomes; The Cancer Genome Atlas (TGCA) has gathered several thousand; and the Exome Aggregation Consortium (ExAC) has sequenced more than 60,000 exomes. Dotted lines show three possible future growth curves. DNA SEQUENCING SOARS 2001 2005 2010 2015 2020 2025 100 103 106 109 Human Genome Project Cumulativenumberofhumangenomes 1000 Genomes TCGA ExAC Current amount 1st personal genome Recorded growth Projection Double every 7 months (historical growth rate) Double every 12 months (Illumina estimate) Double every 18 months (Moore's law) Michael Einsetein, Nature, 2015
- 28.
- 29. More DNA MoreMeaning 더 많은 의미를 파악하기 위해서는 더 많은 DNA가 필요 더 많이 시퀀싱하도록 유도하려면 더 많은 가치를 줘야함 Dilemma in Sequencing
- 30. opportunities, we conductedtwo surveys. First, we surveyed people with diverse backgrounds and determined factors that deter them from sequencing their genomes. Second, we interviewed researchers at many pharma and biotech companies and identified challenges that they face when working with genomic data. Figure 3. Survey results (sample size = 402). 4.1. Individuals Only 2% of people who participated in our survey have genotyped or sequenced their Dilemma in Sequencing •시퀀싱을 하지 않는 이유: 너무 비싸서 & 프라이버시 문제 (데이터에 대한 권한) •시퀀싱에 지불 의사가 크지 않다: 대다수가 250불 이하 (=원가 이하)
- 31. Blockchain-enabled genomic data sharingand analysis platform Dennis Grishin Kamal Obbad
- 32. The traditional businessmodel of direct-to-consumer personal genomics companies is illustrated in Figure 4. People pay to sequence or genotype their genomes and receive analysis results. Personal genomics companies keep the genomic data and sell it to pharma and biotech companies that use the data for research and development. This model addresses none of the challenges detailed in the previous sections. Figure 4. Traditional business model of personal genomics companies. The Nebula model, shown in FIgure 5, eliminates personal genomics companies as middlemen between data owners and data buyers. Instead, data owners can acquire their personal genomic data from Nebula sequencing facilities or other sources, join the Nebula blockchain-based, peer-to-peer network and directly connect with data buyers. As detailed in the following sections, this model reduces effective sequencing costs and enhances protection of personal genomic data. It also satisfies the needs of data buyers in regards to data availability, data acquisition logistics and resources needed for genomic big data. 11 •시퀀싱 비용: 사용자가 일단 시퀀싱 비용을 지불해야 한다. •데이터 소유권: 어느 제약사에 얼마에 판매할지는 사용자 본인이 아닌, 중간 밴더가 결정한다. •프라이버시: 사용자의 데이터가 판매된 이후 어떻게 사용되는지 알 수 없다. •인센티브: 사용자는 이 판매에 대한 재정적인 보상을 받지 못한다. 서열 생산 및 상호 거래 촉진에 한계
- 33. The traditional businessmodel of direct-to-consumer personal genomics companies is illustrated in Figure 4. People pay to sequence or genotype their genomes and receive analysis results. Personal genomics companies keep the genomic data and sell it to pharma and biotech companies that use the data for research and development. This model addresses none of the challenges detailed in the previous sections. Figure 4. Traditional business model of personal genomics companies. The Nebula model, shown in FIgure 5, eliminates personal genomics companies as middlemen between data owners and data buyers. Instead, data owners can acquire their personal genomic data from Nebula sequencing facilities or other sources, join the Nebula blockchain-based, peer-to-peer network and directly connect with data buyers. As detailed in the following sections, this model reduces effective sequencing costs and enhances protection of personal genomic data. It also satisfies the needs of data buyers in regards to data availability, data acquisition logistics and resources needed for genomic big data. 11 Figure 5. The Nebula model. 5.1.1. Lower sequencing costs Nebula reduces effective sequencing costs in two ways. First, individuals who have not yet sequenced their personal genomes can join the Nebula network and participate in paid
- 34. Figure 5. TheNebula model. 5.1.1. Lower sequencing costs Nebula reduces effective sequencing costs in two ways. First, individuals who have not yet sequenced their personal genomes can join the Nebula network and participate in paid
- 35. Figure 5. TheNebula model. 5.1.1. Lower sequencing costs Nebula reduces effective sequencing costs in two ways. First, individuals who have not yet sequenced their personal genomes can join the Nebula network and participate in paid
- 36. Figure 5. TheNebula model. 5.1.1. Lower sequencing costs Nebula reduces effective sequencing costs in two ways. First, individuals who have not yet sequenced their personal genomes can join the Nebula network and participate in paid surveys. Thereby data buyers can identify individuals with phenotypes of interest, such as particular medical conditions, and offer to subsidize their genome sequencing costs. As sequencing technology advances and sequencing costs decrease, buyers will be increasingly able to fully pay for personal genome sequencing of many people. Second, individuals who acquired their personal genomic data from Nebula sequencing facilities or other personal genomics companies, can join the Nebula network and profit from selling access to their data. Lowering sequencing costs will incentivize more people to sequence their genomes and result in growth of genomic data that will fuel medical research. 블록체인 기반의 유전체 데이터 플랫폼 •시퀀싱 비용: 사용자의 시퀀싱 비용 지불 없이 일단 시퀀싱을 수행 •데이터 소유권: 어느 제약사에 얼마에 판매할지는 사용자 본인이 결정 •프라이버시: 블록체인 기반으로 데이터의 위변조 및 활용 결과 추적 •인센티브: 네뷸라 토큰 기반으로 사용자에게 재정적 인센티브 제공
- 37. 블록체인 기반의 유전체데이터 플랫폼 Nebula tokens will be the currency of the Nebula network. The growth of the Nebula network will set in motion a circular flow of Nebula tokens as illustrated in Figure 6B. Individuals will buy personal genome sequencing at Nebula sequencing facilities and pay with Nebula tokens, data buyers will use Nebula tokens to purchase access to genomic and phenotypic data, and Nebula Genomics will sell Nebula tokens to data buyers for fiat money. Figure 6. (A) Growth of the Nebula network. (B) Circular flow of Nebula tokens. 7. Personal genomics companies in comparison •모든 데이터의 트랜젝션은 프라이빗 토큰 (네뷸라 토큰)을 기반으로 이루어짐 •탈중앙화 방식으로 시퀀싱 비용, 프라이버시 및 인센티브 문제를 해결할 수 있으므로, •결국 시퀀싱 분야의 닭과 달걀의 문제를 해결 가능
- 39. AnalysisTarget Discovery AnalysisLeadDiscovery Clinical Trial Post Market Surveillance Digital Healthcare in Drug Development •딥러닝 기반의 lead discovery •인공지능+제약사
- 40. No choice butto bring AI into the medicine
- 42. 12 Olga Russakovsky*et al. Fig. 4 Random selection of images in ILSVRC detection validation set. The images in the top 4 rows were taken from ILSVRC2012 single-object localization validation set, and the images in the bottom 4 rows were collected from Flickr using scene-level queries. tage of all the positive examples available. The second is images collected from Flickr specifically for the de- http://arxiv.org/pdf/1409.0575.pdf
- 43. • Main competition •객체 분류 (Classification): 그림 속의 객체를 분류 • 객체 위치 (localization): 그림 속 ‘하나’의 객체를 분류하고 위치를 파악 • 객체 인식 (object detection): 그림 속 ‘모든’ 객체를 분류하고 위치 파악 16 Olga Russakovsky* et al. Fig. 7 Tasks in ILSVRC. The first column shows the ground truth labeling on an example image, and the next three show three sample outputs with the corresponding evaluation score. http://arxiv.org/pdf/1409.0575.pdf
- 44. Performance of winningentries in the ILSVRC2010-2015 competitions in each of the three tasks http://image-net.org/challenges/LSVRC/2015/results#loc Single-object localization Localizationerror 0 10 20 30 40 50 2011 2012 2013 2014 2015 Object detection Averageprecision 0.0 17.5 35.0 52.5 70.0 2013 2014 2015 Image classification Classificationerror 0 10 20 30 2010 2011 2012 2013 2014 2015
- 46. Kaiming He, XiangyuZhang, Shaoqing Ren, Jian Sun, “Deep Residual Learning for Image Recognition”, 2015 How deep is deep?
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- 51. DeepFace: Closing theGap to Human-Level Performance in FaceVerification Taigman,Y. et al. (2014). DeepFace: Closing the Gap to Human-Level Performance in FaceVerification, CVPR’14. Figure 2. Outline of the DeepFace architecture. A front-end of a single convolution-pooling-convolution filtering on the rectified input, followed by three locally-connected layers and two fully-connected layers. Colors illustrate feature maps produced at each layer. The net includes more than 120 million parameters, where more than 95% come from the local and fully connected layers. very few parameters. These layers merely expand the input into a set of simple local features. The subsequent layers (L4, L5 and L6) are instead lo- cally connected [13, 16], like a convolutional layer they ap- ply a filter bank, but every location in the feature map learns a different set of filters. Since different regions of an aligned image have different local statistics, the spatial stationarity The goal of training is to maximize the probability of the correct class (face id). We achieve this by minimiz- ing the cross-entropy loss for each training sample. If k is the index of the true label for a given input, the loss is: L = log pk. The loss is minimized over the parameters by computing the gradient of L w.r.t. the parameters and Human: 95% vs. DeepFace in Facebook: 97.35% Recognition Accuracy for Labeled Faces in the Wild (LFW) dataset (13,233 images, 5,749 people)
- 52. FaceNet:A Unified Embeddingfor Face Recognition and Clustering Schroff, F. et al. (2015). FaceNet:A Unified Embedding for Face Recognition and Clustering Human: 95% vs. FaceNet of Google: 99.63% Recognition Accuracy for Labeled Faces in the Wild (LFW) dataset (13,233 images, 5,749 people) False accept False reject s. This shows all pairs of images that were on LFW. Only eight of the 13 errors shown he other four are mislabeled in LFW. on Youtube Faces DB ge similarity of all pairs of the first one our face detector detects in each video. False accept False reject Figure 6. LFW errors. This shows all pairs of images that were incorrectly classified on LFW. Only eight of the 13 errors shown here are actual errors the other four are mislabeled in LFW. 5.7. Performance on Youtube Faces DB We use the average similarity of all pairs of the first one hundred frames that our face detector detects in each video. This gives us a classification accuracy of 95.12%±0.39. Using the first one thousand frames results in 95.18%. Compared to [17] 91.4% who also evaluate one hundred frames per video we reduce the error rate by almost half. DeepId2+ [15] achieved 93.2% and our method reduces this error by 30%, comparable to our improvement on LFW. 5.8. Face Clustering Our compact embedding lends itself to be used in order to cluster a users personal photos into groups of people with the same identity. The constraints in assignment imposed by clustering faces, compared to the pure verification task, lead to truly amazing results. Figure 7 shows one cluster in a users personal photo collection, generated using agglom- erative clustering. It is a clear showcase of the incredible invariance to occlusion, lighting, pose and even age. Figure 7. Face Clustering. Shown is an exemplar cluster for one user. All these images in the users personal photo collection were clustered together. 6. Summary We provide a method to directly learn an embedding into an Euclidean space for face verification. This sets it apart from other methods [15, 17] who use the CNN bottleneck layer, or require additional post-processing such as concate- nation of multiple models and PCA, as well as SVM clas- sification. Our end-to-end training both simplifies the setup and shows that directly optimizing a loss relevant to the task at hand improves performance. Another strength of our model is that it only requires False accept False reject Figure 6. LFW errors. This shows all pairs of images that were incorrectly classified on LFW. Only eight of the 13 errors shown here are actual errors the other four are mislabeled in LFW. 5.7. Performance on Youtube Faces DB We use the average similarity of all pairs of the first one hundred frames that our face detector detects in each video. This gives us a classification accuracy of 95.12%±0.39. Using the first one thousand frames results in 95.18%. Compared to [17] 91.4% who also evaluate one hundred frames per video we reduce the error rate by almost half. DeepId2+ [15] achieved 93.2% and our method reduces this error by 30%, comparable to our improvement on LFW. 5.8. Face Clustering Our compact embedding lends itself to be used in order to cluster a users personal photos into groups of people with the same identity. The constraints in assignment imposed by clustering faces, compared to the pure verification task, Figure 7. Face Clustering. Shown is an exemplar cluster for one user. All these images in the users personal photo collection were clustered together. 6. Summary We provide a method to directly learn an embedding into an Euclidean space for face verification. This sets it apart from other methods [15, 17] who use the CNN bottleneck layer, or require additional post-processing such as concate- nation of multiple models and PCA, as well as SVM clas-
- 53. Targeting Ultimate Accuracy:Face Recognition via Deep Embedding Jingtuo Liu (2015) Targeting Ultimate Accuracy: Face Recognition via Deep Embedding Human: 95% vs.Baidu: 99.77% Recognition Accuracy for Labeled Faces in the Wild (LFW) dataset (13,233 images, 5,749 people) 3 Although several algorithms have achieved nearly perfect accuracy in the 6000-pair verification task, a more practical can achieve 95.8% identification rate, relatively reducing the error rate by about 77%. TABLE 3. COMPARISONS WITH OTHER METHODS ON SEVERAL EVALUATION TASKS Score = -0.060 (pair #113) Score = -0.022 (pair #202) Score = -0.034 (pair #656) Score = -0.031 (pair #1230) Score = -0.073 (pair #1862) Score = -0.091(pair #2499) Score = -0.024 (pair #2551) Score = -0.036 (pair #2552) Score = -0.089 (pair #2610) Method Performance on tasks Pair-wise Accuracy(%) Rank-1(%) DIR(%) @ FAR =1% Verification(% )@ FAR=0.1% Open-set Identification(% )@ Rank = 1,FAR = 0.1% IDL Ensemble Model 99.77 98.03 95.8 99.41 92.09 IDL Single Model 99.68 97.60 94.12 99.11 89.08 FaceNet[12] 99.63 NA NA NA NA DeepID3[9] 99.53 96.00 81.40 NA NA Face++[2] 99.50 NA NA NA NA Facebook[15] 98.37 82.5 61.9 NA NA Learning from Scratch[4] 97.73 NA NA 80.26 28.90 HighDimLBP[10] 95.17 NA NA 41.66(reported in [4]) 18.07(reported in [4]) • 6,000쌍의 얼굴 사진 중에 바이두의 인공지능은 불과 14쌍만을 잘못 판단 • 알고 보니 이 14쌍 중의 5쌍의 사진은 오히려 정답에 오류가 있었고, 실제로는 인공지능이 정확 (red box)
- 54. Show and Tell: ANeural Image Caption Generator Vinyals, O. et al. (2015). Show and Tell:A Neural Image Caption Generator, arXiv:1411.4555 v om Samy Bengio Google bengio@google.com Dumitru Erhan Google dumitru@google.com s a cts his re- m- ed he de- nts A group of people shopping at an outdoor market. ! There are many vegetables at the fruit stand. Vision! Deep CNN Language ! Generating! RNN Figure 1. NIC, our model, is based end-to-end on a neural net- work consisting of a vision CNN followed by a language gener-
- 55. Show and Tell: ANeural Image Caption Generator Vinyals, O. et al. (2015). Show and Tell:A Neural Image Caption Generator, arXiv:1411.4555 Figure 5. A selection of evaluation results, grouped by human rating.
- 56.
- 57. Bone Age Assessment •M: 28 Classes • F: 20 Classes • Method: G.P. • Top3-95.28% (F) • Top3-81.55% (M)
- 59. 40 50 60 70 80 인공지능 의사 A의사 B 40 50 60 70 80 의사 A + 인공지능 의사 B + 인공지능 69.5% 63% 49.5% 72.5% 57.5% 정확도(%) 영상의학과 펠로우 (소아영상 세부전공) 영상의학과 2년차 전공의 인공지능 vs 의사 인공지능 + 의사 AJR Am J Roentgenol. 2017 Dec;209(6):1374-1380. • 총 환자의 수: 200명 • 의사A: 소아영상 세부전공한 영상의학 전문의 (500례 이상의 판독 경험) • 의사B: 영상의학과 2년차 전공의 (판독법 하루 교육 이수 + 20례 판독) • 레퍼런스: 경험 많은 소아영상의학과 전문의 2명(18년, 4년 경력)의 컨센서스 • 인공지능: VUNO의 골연령 판독 딥러닝 골연령 판독에 인간 의사와 인공지능의 시너지 효과 Digital Healthcare Institute Director,Yoon Sup Choi, PhD yoonsup.choi@gmail.com
- 60. 총 판독 시간(m) 0 50 100 150 200 w/o AI w/ AI 0 50 100 150 200 w/o AI w/ AI 188m 154m 180m 108m saving 40% of time saving 18% of time 의사 A 의사 B 골연령 판독에서 인공지능을 활용하면 판독 시간의 절감도 가능 • 총 환자의 수: 200명 • 의사A: 소아영상 세부전공한 영상의학 전문의 (500례 이상의 판독 경험) • 의사B: 영상의학과 2년차 전공의 (판독법 하루 교육 이수 + 20례 판독) • 레퍼런스: 경험 많은 소아영상의학과 전문의 2명(18년, 4년 경력)의 컨센서스 • 인공지능: VUNO의 골연령 판독 딥러닝 AJR Am J Roentgenol. 2017 Dec;209(6):1374-1380. Digital Healthcare Institute Director,Yoon Sup Choi, PhD yoonsup.choi@gmail.com
- 61. Detection of DiabeticRetinopathy
- 62. 당뇨성 망막병증 • 당뇨병의대표적 합병증: 당뇨병력이 30년 이상 환자 90% 발병 • 안과 전문의들이 안저(안구의 안쪽)를 사진으로 찍어서 판독 • 망막 내 미세혈관 생성, 출혈, 삼출물 정도를 파악하여 진단
- 63. Training Set /Test Set • CNN으로 후향적으로 128,175개의 안저 이미지 학습 • 미국의 안과전문의 54명이 3-7회 판독한 데이터 • 우수한 안과전문의들 7-8명의 판독 결과와 인공지능의 판독 결과 비교 • EyePACS-1 (9,963 개), Messidor-2 (1,748 개)a) Fullscreen mode b) Hit reset to reload this image. This will reset all of the grading. c) Comment box for other pathologies you see eFigure 2. Screenshot of the Second Screen of the Grading Tool, Which Asks Graders to Assess the Image for DR, DME and Other Notable Conditions or Findings
- 64. • EyePACS-1 과 Messidor-2의 AUC = 0.991, 0.990 • 7-8명의 안과 전문의와 sensitivity, specificity 가 동일한 수준 • F-score: 0.95 (vs. 인간 의사는 0.91) Additional sensitivity analyses were conducted for sev- eralsubcategories:(1)detectingmoderateorworsediabeticreti- effects of data set size on algorithm performance were exam- ined and shown to plateau at around 60 000 images (or ap- Figure 2. Validation Set Performance for Referable Diabetic Retinopathy 100 80 60 40 20 0 0 70 80 85 95 90 75 0 5 10 15 20 25 30 100806040 Sensitivity,% 1 – Specificity, % 20 EyePACS-1: AUC, 99.1%; 95% CI, 98.8%-99.3%A 100 High-sensitivity operating point High-specificity operating point 100 80 60 40 20 0 0 70 80 85 95 90 75 0 5 10 15 20 25 30 100806040 Sensitivity,% 1 – Specificity, % 20 Messidor-2: AUC, 99.0%; 95% CI, 98.6%-99.5%B 100 High-specificity operating point High-sensitivity operating point Performance of the algorithm (black curve) and ophthalmologists (colored circles) for the presence of referable diabetic retinopathy (moderate or worse diabetic retinopathy or referable diabetic macular edema) on A, EyePACS-1 (8788 fully gradable images) and B, Messidor-2 (1745 fully gradable images). The black diamonds on the graph correspond to the sensitivity and specificity of the algorithm at the high-sensitivity and high-specificity operating points. In A, for the high-sensitivity operating point, specificity was 93.4% (95% CI, 92.8%-94.0%) and sensitivity was 97.5% (95% CI, 95.8%-98.7%); for the high-specificity operating point, specificity was 98.1% (95% CI, 97.8%-98.5%) and sensitivity was 90.3% (95% CI, 87.5%-92.7%). In B, for the high-sensitivity operating point, specificity was 93.9% (95% CI, 92.4%-95.3%) and sensitivity was 96.1% (95% CI, 92.4%-98.3%); for the high-specificity operating point, specificity was 98.5% (95% CI, 97.7%-99.1%) and sensitivity was 87.0% (95% CI, 81.1%-91.0%). There were 8 ophthalmologists who graded EyePACS-1 and 7 ophthalmologists who graded Messidor-2. AUC indicates area under the receiver operating characteristic curve. Research Original Investigation Accuracy of a Deep Learning Algorithm for Detection of Diabetic Retinopathy Results
- 65.
- 66.
- 67. 0 0 MO N T H 2 0 1 7 | V O L 0 0 0 | N A T U R E | 1 LETTER doi:10.1038/nature21056 Dermatologist-level classification of skin cancer with deep neural networks Andre Esteva1 *, Brett Kuprel1 *, Roberto A. Novoa2,3 , Justin Ko2 , Susan M. Swetter2,4 , Helen M. Blau5 & Sebastian Thrun6 Skin cancer, the most common human malignancy1–3 , is primarily diagnosed visually, beginning with an initial clinical screening and followed potentially by dermoscopic analysis, a biopsy and histopathological examination. Automated classification of skin lesions using images is a challenging task owing to the fine-grained variability in the appearance of skin lesions. Deep convolutional neural networks (CNNs)4,5 show potential for general and highly variable tasks across many fine-grained object categories6–11 . Here we demonstrate classification of skin lesions using a single CNN, trained end-to-end from images directly, using only pixels and disease labels as inputs. We train a CNN using a dataset of 129,450 clinical images—two orders of magnitude larger than previous datasets12 —consisting of 2,032 different diseases. We test its performance against 21 board-certified dermatologists on biopsy-proven clinical images with two critical binary classification use cases: keratinocyte carcinomas versus benign seborrheic keratoses; and malignant melanomas versus benign nevi. The first case represents the identification of the most common cancers, the second represents the identification of the deadliest skin cancer. The CNN achieves performance on par with all tested experts across both tasks, demonstrating an artificial intelligence capable of classifying skin cancer with a level of competence comparable to dermatologists. Outfitted with deep neural networks, mobile devices can potentially extend the reach of dermatologists outside of the clinic. It is projected that 6.3 billion smartphone subscriptions will exist by the year 2021 (ref. 13) and can therefore potentially provide low-cost universal access to vital diagnostic care. There are 5.4 million new cases of skin cancer in the United States2 every year. One in five Americans will be diagnosed with a cutaneous malignancy in their lifetime. Although melanomas represent fewer than 5% of all skin cancers in the United States, they account for approxi- mately 75% of all skin-cancer-related deaths, and are responsible for over 10,000 deaths annually in the United States alone. Early detection is critical, as the estimated 5-year survival rate for melanoma drops from over 99% if detected in its earliest stages to about 14% if detected in its latest stages. We developed a computational method which may allow medical practitioners and patients to proactively track skin lesions and detect cancer earlier. By creating a novel disease taxonomy, and a disease-partitioning algorithm that maps individual diseases into training classes, we are able to build a deep learning system for auto- mated dermatology. Previous work in dermatological computer-aided classification12,14,15 has lacked the generalization capability of medical practitioners owing to insufficient data and a focus on standardized tasks such as dermoscopy16–18 and histological image classification19–22 . Dermoscopy images are acquired via a specialized instrument and histological images are acquired via invasive biopsy and microscopy; whereby both modalities yield highly standardized images. Photographic images (for example, smartphone images) exhibit variability in factors such as zoom, angle and lighting, making classification substantially more challenging23,24 . We overcome this challenge by using a data- driven approach—1.41 million pre-training and training images make classification robust to photographic variability. Many previous techniques require extensive preprocessing, lesion segmentation and extraction of domain-specific visual features before classification. By contrast, our system requires no hand-crafted features; it is trained end-to-end directly from image labels and raw pixels, with a single network for both photographic and dermoscopic images. The existing body of work uses small datasets of typically less than a thousand images of skin lesions16,18,19 , which, as a result, do not generalize well to new images. We demonstrate generalizable classification with a new dermatologist-labelled dataset of 129,450 clinical images, including 3,374 dermoscopy images. Deep learning algorithms, powered by advances in computation and very large datasets25 , have recently been shown to exceed human performance in visual tasks such as playing Atari games26 , strategic board games like Go27 and object recognition6 . In this paper we outline the development of a CNN that matches the performance of dermatologists at three key diagnostic tasks: melanoma classification, melanoma classification using dermoscopy and carcinoma classification. We restrict the comparisons to image-based classification. We utilize a GoogleNet Inception v3 CNN architecture9 that was pre- trained on approximately 1.28 million images (1,000 object categories) from the 2014 ImageNet Large Scale Visual Recognition Challenge6 , and train it on our dataset using transfer learning28 . Figure 1 shows the working system. The CNN is trained using 757 disease classes. Our dataset is composed of dermatologist-labelled images organized in a tree-structured taxonomy of 2,032 diseases, in which the individual diseases form the leaf nodes. The images come from 18 different clinician-curated, open-access online repositories, as well as from clinical data from Stanford University Medical Center. Figure 2a shows a subset of the full taxonomy, which has been organized clinically and visually by medical experts. We split our dataset into 127,463 training and validation images and 1,942 biopsy-labelled test images. To take advantage of fine-grained information contained within the taxonomy structure, we develop an algorithm (Extended Data Table 1) to partition diseases into fine-grained training classes (for example, amelanotic melanoma and acrolentiginous melanoma). During inference, the CNN outputs a probability distribution over these fine classes. To recover the probabilities for coarser-level classes of interest (for example, melanoma) we sum the probabilities of their descendants (see Methods and Extended Data Fig. 1 for more details). We validate the effectiveness of the algorithm in two ways, using nine-fold cross-validation. First, we validate the algorithm using a three-class disease partition—the first-level nodes of the taxonomy, which represent benign lesions, malignant lesions and non-neoplastic 1 Department of Electrical Engineering, Stanford University, Stanford, California, USA. 2 Department of Dermatology, Stanford University, Stanford, California, USA. 3 Department of Pathology, Stanford University, Stanford, California, USA. 4 Dermatology Service, Veterans Affairs Palo Alto Health Care System, Palo Alto, California, USA. 5 Baxter Laboratory for Stem Cell Biology, Department of Microbiology and Immunology, Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, California, USA. 6 Department of Computer Science, Stanford University, Stanford, California, USA. *These authors contributed equally to this work. © 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.
- 68. LETTERH his task, theCNN achieves 72.1±0.9% (mean±s.d.) overall he average of individual inference class accuracies) and two gists attain 65.56% and 66.0% accuracy on a subset of the set. Second, we validate the algorithm using a nine-class rtition—the second-level nodes—so that the diseases of have similar medical treatment plans. The CNN achieves two trials, one using standard images and the other using images, which reflect the two steps that a dermatologist m to obtain a clinical impression. The same CNN is used for a Figure 2b shows a few example images, demonstrating th distinguishing between malignant and benign lesions, whic visual features. Our comparison metrics are sensitivity an Acral-lentiginous melanoma Amelanotic melanoma Lentigo melanoma … Blue nevus Halo nevus Mongolian spot … Training classes (757)Deep convolutional neural network (Inception v3) Inference classes (varies by task) 92% malignant melanocytic lesion 8% benign melanocytic lesion Skin lesion image Convolution AvgPool MaxPool Concat Dropout Fully connected Softmax Deep CNN layout. Our classification technique is a Data flow is from left to right: an image of a skin lesion e, melanoma) is sequentially warped into a probability over clinical classes of skin disease using Google Inception hitecture pretrained on the ImageNet dataset (1.28 million 1,000 generic object classes) and fine-tuned on our own 29,450 skin lesions comprising 2,032 different diseases. ning classes are defined using a novel taxonomy of skin disease oning algorithm that maps diseases into training classes (for example, acrolentiginous melanoma, amelanotic melano melanoma). Inference classes are more general and are comp or more training classes (for example, malignant melanocytic class of melanomas). The probability of an inference class is c summing the probabilities of the training classes according to structure (see Methods). Inception v3 CNN architecture repr from https://research.googleblog.com/2016/03/train-your-ow classifier-with.html GoogleNet Inception v3 • 129,450개의 피부과 병변 이미지 데이터를 자체 제작 • 미국의 피부과 전문의 18명이 데이터 curation • CNN (Inception v3)으로 이미지를 학습 • 피부과 전문의들 21명과 인공지능의 판독 결과 비교 • 표피세포 암 (keratinocyte carcinoma)과 지루각화증(benign seborrheic keratosis)의 구분 • 악성 흑색종과 양성 병변 구분 (표준 이미지 데이터 기반) • 악성 흑색종과 양성 병변 구분 (더마토스코프로 찍은 이미지 기반)
- 69. Skin cancer classificationperformance of the CNN and dermatologists. LETT a b 0 1 Sensitivity 0 1 Specificity Melanoma: 130 images 0 1 Sensitivity 0 1 Specificity Melanoma: 225 images Algorithm: AUC = 0.96 0 1 Sensitivity 0 1 Specificity Melanoma: 111 dermoscopy images 0 1 Sensitivity 0 1 Specificity Carcinoma: 707 images Algorithm: AUC = 0.96 0 1 Sensitivity 0 1 Specificity Melanoma: 1,010 dermoscopy images Algorithm: AUC = 0.94 0 1 Sensitivity 0 1 Specificity Carcinoma: 135 images Algorithm: AUC = 0.96 Dermatologists (25) Average dermatologist Algorithm: AUC = 0.94 Dermatologists (22) Average dermatologist Algorithm: AUC = 0.91 Dermatologists (21) Average dermatologist cancer classification performance of the CNN and 21명 중에 인공지능보다 정확성이 떨어지는 피부과 전문의들이 상당수 있었음 피부과 전문의들의 평균 성적도 인공지능보다 좋지 않았음
- 70. Skin Cancer ImageClassification (TensorFlow Dev Summit 2017) Skin cancer classification performance of the CNN and dermatologists. https://www.youtube.com/watch?v=toK1OSLep3s&t=419s
- 71. WSJ, 2017 June •다국적 제약사는 인공지능 기술을 신약 개발에 활용하기 위해 다양한 시도 • 최근 인공지능에서는 과거의 virtual screening, docking 등과는 다른 방식을 이용
- 72. https://research.googleblog.com/2017/12/deepvariant-highly-accurate-genomes.html DeepVariant: Highly AccurateGenomes With Deep Neural Networks •2016년 PrecisionFDA의 SNP 퍼포먼스 부문에서 Verily 가 우승 •이 알고리즘이 개선되어 DeepVariant 라는 이름으로 공개 •Read의 alignment를 위해서 그 자체를 ‘이미지’로 인식하여 CNN으로 학습
- 73. targets. To overcome theselimitations we take an indirect approach. Instead of directly visualizing filters in order to understand their specialization, we apply filters to input data and examine the location where they maximally fire. Using this technique we were able to map filters to chemical functions. For example, Figure 5 illustrate the 3D locations at which a particular filter from our first convo- lutional layer fires. Visual inspection of the locations at which that filter is active reveals that this filter specializes as a sulfonyl/sulfonamide detector. This demonstrates the ability of the model to learn complex chemical features from simpler ones. In this case, the filter has inferred a meaningful spatial arrangement of input atom types without any chemical prior knowledge. Figure 5: Sulfonyl/sulfonamide detection with autonomously trained convolutional filters. 8 Protein-Compound Complex Structure Binding, or non-binding?
- 75. AtomNet: A DeepConvolutional Neural Network for Bioactivity Prediction in Structure-based Drug Discovery Izhar Wallach Atomwise, Inc. izhar@atomwise.com Michael Dzamba Atomwise, Inc. misko@atomwise.com Abraham Heifets Atomwise, Inc. abe@atomwise.com Abstract Deep convolutional neural networks comprise a subclass of deep neural networks (DNN) with a constrained architecture that leverages the spatial and temporal structure of the domain they model. Convolutional networks achieve the best pre- dictive performance in areas such as speech and image recognition by hierarchi- cally composing simple local features into complex models. Although DNNs have been used in drug discovery for QSAR and ligand-based bioactivity predictions, none of these models have benefited from this powerful convolutional architec- ture. This paper introduces AtomNet, the first structure-based, deep convolutional neural network designed to predict the bioactivity of small molecules for drug dis- covery applications. We demonstrate how to apply the convolutional concepts of feature locality and hierarchical composition to the modeling of bioactivity and chemical interactions. In further contrast to existing DNN techniques, we show that AtomNet’s application of local convolutional filters to structural target infor- mation successfully predicts new active molecules for targets with no previously known modulators. Finally, we show that AtomNet outperforms previous docking approaches on a diverse set of benchmarks by a large margin, achieving an AUC greater than 0.9 on 57.8% of the targets in the DUDE benchmark. 1 Introduction Fundamentally, biological systems operate through the physical interaction of molecules. The ability to determine when molecular binding occurs is therefore critical for the discovery of new medicines and for furthering of our understanding of biology. Unfortunately, despite thirty years of compu- tational efforts, computer tools remain too inaccurate for routine binding prediction, and physical experiments remain the state of the art for binding determination. The ability to accurately pre- dict molecular binding would reduce the time-to-discovery of new treatments, help eliminate toxic molecules early in development, and guide medicinal chemistry efforts [1, 2]. In this paper, we introduce a new predictive architecture, AtomNet, to help address these challenges. AtomNet is novel in two regards: AtomNet is the first deep convolutional neural network for molec- ular binding affinity prediction. It is also the first deep learning system that incorporates structural information about the target to make its predictions. Deep convolutional neural networks (DCNN) are currently the best performing predictive models for speech and vision [3, 4, 5, 6]. DCNN is a class of deep neural network that constrains its model architecture to leverage the spatial and temporal structure of its domain. For example, a low-level image feature, such as an edge, can be described within a small spatially-proximate patch of pixels. Such a feature detector can share evidence across the entire receptive field by “tying the weights” of the detector neurons, as the recognition of the edge does not depend on where it is found within 1 arXiv:1510.02855v1[cs.LG]10Oct2015
- 76. AtomNet: A DeepConvolutional Neural Network for Bioactivity Prediction in Structure-based Drug Discovery Izhar Wallach Atomwise, Inc. izhar@atomwise.com Michael Dzamba Atomwise, Inc. misko@atomwise.com Abraham Heifets Atomwise, Inc. abe@atomwise.com Abstract Deep convolutional neural networks comprise a subclass of deep neural networks (DNN) with a constrained architecture that leverages the spatial and temporal structure of the domain they model. Convolutional networks achieve the best pre- dictive performance in areas such as speech and image recognition by hierarchi- cally composing simple local features into complex models. Although DNNs have been used in drug discovery for QSAR and ligand-based bioactivity predictions, none of these models have benefited from this powerful convolutional architec- ture. This paper introduces AtomNet, the first structure-based, deep convolutional neural network designed to predict the bioactivity of small molecules for drug dis- covery applications. We demonstrate how to apply the convolutional concepts of feature locality and hierarchical composition to the modeling of bioactivity and chemical interactions. In further contrast to existing DNN techniques, we show that AtomNet’s application of local convolutional filters to structural target infor- mation successfully predicts new active molecules for targets with no previously known modulators. Finally, we show that AtomNet outperforms previous docking approaches on a diverse set of benchmarks by a large margin, achieving an AUC greater than 0.9 on 57.8% of the targets in the DUDE benchmark. 1 Introduction Fundamentally, biological systems operate through the physical interaction of molecules. The ability to determine when molecular binding occurs is therefore critical for the discovery of new medicines and for furthering of our understanding of biology. Unfortunately, despite thirty years of compu- tational efforts, computer tools remain too inaccurate for routine binding prediction, and physical experiments remain the state of the art for binding determination. The ability to accurately pre- dict molecular binding would reduce the time-to-discovery of new treatments, help eliminate toxic molecules early in development, and guide medicinal chemistry efforts [1, 2]. In this paper, we introduce a new predictive architecture, AtomNet, to help address these challenges. AtomNet is novel in two regards: AtomNet is the first deep convolutional neural network for molec- ular binding affinity prediction. It is also the first deep learning system that incorporates structural information about the target to make its predictions. Deep convolutional neural networks (DCNN) are currently the best performing predictive models for speech and vision [3, 4, 5, 6]. DCNN is a class of deep neural network that constrains its model architecture to leverage the spatial and temporal structure of its domain. For example, a low-level image feature, such as an edge, can be described within a small spatially-proximate patch of pixels. Such a feature detector can share evidence across the entire receptive field by “tying the weights” of the detector neurons, as the recognition of the edge does not depend on where it is found within 1 arXiv:1510.02855v1[cs.LG]10Oct2015 Smina 123 35 5 0 0 Table 3: The number of targets on which AtomNet and Smina exceed given adjusted-logAUC thresh- olds. For example, on the CHEMBL-20 PMD set, AtomNet achieves an adjusted-logAUC of 0.3 or better for 27 targets (out of 50 possible targets). ChEMBL-20 PMD contains 50 targets, DUDE- 30 contains 30 targets, DUDE-102 contains 102 targets, and ChEMBL-20 inactives contains 149 targets. To overcome these limitations we take an indirect approach. Instead of directly visualizing filters in order to understand their specialization, we apply filters to input data and examine the location where they maximally fire. Using this technique we were able to map filters to chemical functions. For example, Figure 5 illustrate the 3D locations at which a particular filter from our first convo- lutional layer fires. Visual inspection of the locations at which that filter is active reveals that this filter specializes as a sulfonyl/sulfonamide detector. This demonstrates the ability of the model to learn complex chemical features from simpler ones. In this case, the filter has inferred a meaningful spatial arrangement of input atom types without any chemical prior knowledge. Figure 5: Sulfonyl/sulfonamide detection with autonomously trained convolutional filters. 8 • 이미 알려진 단백질-리간드 3차원 결합 구조를 딥러닝(CNN)으로 학습 • 화학 결합 등에 대한 계산 없이도, 단백질-리간드 결합 여부를 계산 • 기존의 구조기반 예측 등 대비, 딥러닝으로 더 정확히 예측하였음
- 77. AtomNet: A DeepConvolutional Neural Network for Bioactivity Prediction in Structure-based Drug Discovery Izhar Wallach Atomwise, Inc. izhar@atomwise.com Michael Dzamba Atomwise, Inc. misko@atomwise.com Abraham Heifets Atomwise, Inc. abe@atomwise.com Abstract Deep convolutional neural networks comprise a subclass of deep neural networks (DNN) with a constrained architecture that leverages the spatial and temporal structure of the domain they model. Convolutional networks achieve the best pre- dictive performance in areas such as speech and image recognition by hierarchi- cally composing simple local features into complex models. Although DNNs have been used in drug discovery for QSAR and ligand-based bioactivity predictions, none of these models have benefited from this powerful convolutional architec- ture. This paper introduces AtomNet, the first structure-based, deep convolutional neural network designed to predict the bioactivity of small molecules for drug dis- covery applications. We demonstrate how to apply the convolutional concepts of feature locality and hierarchical composition to the modeling of bioactivity and chemical interactions. In further contrast to existing DNN techniques, we show that AtomNet’s application of local convolutional filters to structural target infor- mation successfully predicts new active molecules for targets with no previously known modulators. Finally, we show that AtomNet outperforms previous docking approaches on a diverse set of benchmarks by a large margin, achieving an AUC greater than 0.9 on 57.8% of the targets in the DUDE benchmark. 1 Introduction Fundamentally, biological systems operate through the physical interaction of molecules. The ability to determine when molecular binding occurs is therefore critical for the discovery of new medicines and for furthering of our understanding of biology. Unfortunately, despite thirty years of compu- tational efforts, computer tools remain too inaccurate for routine binding prediction, and physical experiments remain the state of the art for binding determination. The ability to accurately pre- dict molecular binding would reduce the time-to-discovery of new treatments, help eliminate toxic molecules early in development, and guide medicinal chemistry efforts [1, 2]. In this paper, we introduce a new predictive architecture, AtomNet, to help address these challenges. AtomNet is novel in two regards: AtomNet is the first deep convolutional neural network for molec- ular binding affinity prediction. It is also the first deep learning system that incorporates structural information about the target to make its predictions. Deep convolutional neural networks (DCNN) are currently the best performing predictive models for speech and vision [3, 4, 5, 6]. DCNN is a class of deep neural network that constrains its model architecture to leverage the spatial and temporal structure of its domain. For example, a low-level image feature, such as an edge, can be described within a small spatially-proximate patch of pixels. Such a feature detector can share evidence across the entire receptive field by “tying the weights” of the detector neurons, as the recognition of the edge does not depend on where it is found within 1 arXiv:1510.02855v1[cs.LG]10Oct2015 • 이미 알려진 단백질-리간드 3차원 결합 구조를 딥러닝(CNN)으로 학습 • 화학 결합 등에 대한 계산 없이도, 단백질-리간드 결합 여부를 계산 • 기존의 구조기반 예측 등 대비, 딥러닝으로 더 정확히 예측하였음
- 79. 604 VOLUME 35NUMBER 7 JULY 2017 NATURE BIOTECHNOLOGY AI-powered drug discovery captures pharma interest Adrug-huntingdealinkedlastmonth,between Numerate,ofSanBruno,California,andTakeda PharmaceuticaltouseNumerate’sartificialintel- ligence (AI) suite to discover small-molecule therapies for oncology, gastroenterology and central nervous system disorders, is the latest in a growing number of research alliances involv- ing AI-powered computational drug develop- ment firms. Also last month, GNS Healthcare of Cambridge, Massachusetts announced a deal with Roche subsidiary Genentech of South San Francisco, California to use GNS’s AI platform to better understand what affects the efficacy of knowntherapiesinoncology.InMay,Exscientia of Dundee, Scotland, signed a deal with Paris- based Sanofi that includes up to €250 ($280) million in milestone payments. Exscientia will provide the compound design and Sanofi the chemical synthesis of new drugs for diabetes and cardiovascular disease. The trend indicates thatthepharmaindustry’slong-runningskepti- cism about AI is softening into genuine interest, driven by AI’s promise to address the industry’s principal pain point: clinical failure rates. The industry’s willingness to consider AI approaches reflects the reality that drug discov- eryislaborious,timeconsumingandnotpartic- ularly effective. A two-decade-long downward trend in clinical success rates has only recently improved (Nat. Rev. Drug Disc. 15, 379–380, 2016). Still, today, only about one in ten drugs thatenterphase1clinicaltrialsreachespatients. Half those failures are due to a lack of efficacy, says Jackie Hunter, CEO of BenevolentBio, a division of BenevolentAI of London. “That tells you we’re not picking the right targets,” she says. “Even a 5 or 10% reduction in efficacy failure would be amazing.” Hunter’s views on AI in drug discovery are featured in Ernst & Young’s BiotechnologyReport2017releasedlastmonth. Companies that have been watching AI from the sidelines are now jumping in. The best- known machine-learning model for drug dis- covery is perhaps IBM’s Watson. IBM signed a deal in December 2016 with Pfizer to aid the pharma giant’s immuno-oncology drug discov- eryefforts,addingtoastringofpreviousdealsin the biopharma space (Nat.Biotechnol.33,1219– 1220, 2015). IBM’s Watson hunts for drugs by sorting through vast amounts of textual data to provide quick analyses, and tests hypotheses by sorting through massive amounts of laboratory data, clinical reports and scientific publications. BenevolentAI takes a similar approach with algorithms that mine the research literature and proprietary research databases. The explosion of biomedical data has driven much of industry’s interest in AI (Table 1). The confluence of ever-increasing computational horsepower and the proliferation of large data sets has prompted scientists to seek learning algorithms that can help them navigate such massive volumes of information. A lot of the excitement about AI in drug discovery has spilled over from other fields. Machine vision, which allows, among other things, self-driving cars, and language process- ing have given rise to sophisticated multilevel artificial neural networks known as deep- learning algorithms that can be used to model biological processes from assay data as well as textual data. In the past people didn’t have enough data to properly train deep-learning algorithms, says Mark Gerstein, a biomedical informat- ics professor at Yale University in New Haven, Connecticut.Nowresearchershavebeenableto build massive databases and harness them with these algorithms, he says. “I think that excite- ment is justified.” Numerate is one of a growing number of AI companies founded to take advantage of that dataonslaughtasappliedtodrugdiscovery.“We apply AI to chemical design at every stage,” says Guido Lanza, Numerate’s CEO. It will provide Tokyo-basedTakedawithcandidatesforclinical trials by virtual compound screenings against targets, designing and optimizing compounds, andmodelingabsorption,distribution,metabo- lism and excretion, and toxicity. The agreement includes undisclosed milestone payments and royalties. Academic laboratories are also embracing AI tools. In April, Atomwise of San Francisco launched its Artificial Intelligence Molecular Screen awards program, which will deliver 72 potentially therapeutic compounds to as many as 100 university research labs at no charge. Atomwise is a University of Toronto spinout that in 2015 secured an alliance with Merck of Kenilworth, New Jersey. For this new endeavor, it will screen 10 million molecules using its AtomNet platform to provide each lab with 72 compounds aimed at a specific target of the laboratory’s choosing. The Japanese government launched in 2016 a research consortium centered on using Japan’s K supercomputer to ramp up drug discovery efficiency across dozens of local companies and institutions. Among those involved are Takeda and tech giants Fujitsu of Tokyo, Japan, and NEC, also of Tokyo, as well as Kyoto University Hospital and Riken, Japan’s National Research and Development Institute, which will provide clinical data. Deep learning is starting to gain acolytes in the drug discovery space. KTSDESIGN/SciencePhotoLibrary N E W S©2017NatureAmerica,Inc.,partofSpringerNature.Allrightsreserved.
- 80. 604 VOLUME 35NUMBER 7 JULY 2017 NATURE BIOTECHNOLOGY AI-powered drug discovery captures pharma interest Adrug-huntingdealinkedlastmonth,between Numerate,ofSanBruno,California,andTakeda PharmaceuticaltouseNumerate’sartificialintel- ligence (AI) suite to discover small-molecule therapies for oncology, gastroenterology and central nervous system disorders, is the latest in a growing number of research alliances involv- ing AI-powered computational drug develop- ment firms. Also last month, GNS Healthcare of Cambridge, Massachusetts announced a deal with Roche subsidiary Genentech of South San Francisco, California to use GNS’s AI platform to better understand what affects the efficacy of knowntherapiesinoncology.InMay,Exscientia of Dundee, Scotland, signed a deal with Paris- based Sanofi that includes up to €250 ($280) million in milestone payments. Exscientia will provide the compound design and Sanofi the chemical synthesis of new drugs for diabetes and cardiovascular disease. The trend indicates thatthepharmaindustry’slong-runningskepti- cism about AI is softening into genuine interest, driven by AI’s promise to address the industry’s principal pain point: clinical failure rates. The industry’s willingness to consider AI approaches reflects the reality that drug discov- eryislaborious,timeconsumingandnotpartic- ularly effective. A two-decade-long downward trend in clinical success rates has only recently improved (Nat. Rev. Drug Disc. 15, 379–380, 2016). Still, today, only about one in ten drugs thatenterphase1clinicaltrialsreachespatients. Half those failures are due to a lack of efficacy, says Jackie Hunter, CEO of BenevolentBio, a division of BenevolentAI of London. “That tells you we’re not picking the right targets,” she says. “Even a 5 or 10% reduction in efficacy failure would be amazing.” Hunter’s views on AI in drug discovery are featured in Ernst & Young’s BiotechnologyReport2017releasedlastmonth. Companies that have been watching AI from the sidelines are now jumping in. The best- known machine-learning model for drug dis- covery is perhaps IBM’s Watson. IBM signed a deal in December 2016 with Pfizer to aid the pharma giant’s immuno-oncology drug discov- eryefforts,addingtoastringofpreviousdealsin the biopharma space (Nat.Biotechnol.33,1219– 1220, 2015). IBM’s Watson hunts for drugs by sorting through vast amounts of textual data to provide quick analyses, and tests hypotheses by sorting through massive amounts of laboratory data, clinical reports and scientific publications. BenevolentAI takes a similar approach with algorithms that mine the research literature and proprietary research databases. The explosion of biomedical data has driven much of industry’s interest in AI (Table 1). The confluence of ever-increasing computational horsepower and the proliferation of large data sets has prompted scientists to seek learning algorithms that can help them navigate such massive volumes of information. A lot of the excitement about AI in drug discovery has spilled over from other fields. Machine vision, which allows, among other things, self-driving cars, and language process- ing have given rise to sophisticated multilevel artificial neural networks known as deep- learning algorithms that can be used to model biological processes from assay data as well as textual data. In the past people didn’t have enough data to properly train deep-learning algorithms, says Mark Gerstein, a biomedical informat- ics professor at Yale University in New Haven, Connecticut.Nowresearchershavebeenableto build massive databases and harness them with these algorithms, he says. “I think that excite- ment is justified.” Numerate is one of a growing number of AI companies founded to take advantage of that dataonslaughtasappliedtodrugdiscovery.“We apply AI to chemical design at every stage,” says Guido Lanza, Numerate’s CEO. It will provide Tokyo-basedTakedawithcandidatesforclinical trials by virtual compound screenings against targets, designing and optimizing compounds, andmodelingabsorption,distribution,metabo- lism and excretion, and toxicity. The agreement includes undisclosed milestone payments and royalties. Academic laboratories are also embracing AI tools. In April, Atomwise of San Francisco launched its Artificial Intelligence Molecular Screen awards program, which will deliver 72 potentially therapeutic compounds to as many as 100 university research labs at no charge. Atomwise is a University of Toronto spinout that in 2015 secured an alliance with Merck of Kenilworth, New Jersey. For this new endeavor, it will screen 10 million molecules using its AtomNet platform to provide each lab with 72 compounds aimed at a specific target of the laboratory’s choosing. The Japanese government launched in 2016 a research consortium centered on using Japan’s K supercomputer to ramp up drug discovery efficiency across dozens of local companies and institutions. Among those involved are Takeda and tech giants Fujitsu of Tokyo, Japan, and NEC, also of Tokyo, as well as Kyoto University Hospital and Riken, Japan’s National Research and Development Institute, which will provide clinical data. Deep learning is starting to gain acolytes in the drug discovery space. KTSDESIGN/SciencePhotoLibrary N E W S©2017NatureAmerica,Inc.,partofSpringerNature.Allrightsreserved. Genomics data analytics startup WuXi NextCode Genomics of Shanghai; Cambridge, Massachusetts; and Reykjavík, Iceland, collab- orated with researchers at Yale University on a study that used the company’s deep-learning algorithm to identify a key mechanism in blood vessel growth. The result could aid drug discovery efforts aimed at inhibiting blood vessel growth in tumors (Nature doi:10.1038/ nature22322, 2017). IntheUS,duringtheObamaadministration, industry and academia joined forces to apply AI to accelerate drug discovery as part of the CancerMoonshotinitiative(Nat.Biotechnol.34, 119, 2016). The Accelerating Therapeutics for Opportunities in Medicine (ATOM), launched in January 2016, marries computational and experimental approaches, with Brentford, UK-based GlaxoSmithKline, participating with Lawrence Livermore National Laboratory in Livermore, California, and the US National Cancer Institute. The computational portion of the process, which includes deep-learning and other AI algorithms, will be tested in the first two years. In the third year, “we hope to start on day one with a disease hypothesis and on day 365 to deliver a drug candidate,” says MarthaHead,GlaxoSmithKline’shead,insights from data. Table 1 Selected collaborations in the AI-drug discovery space AI company/ location Technology Announced partner/ location Indication(s) Deal date Atomwise Deep-learning screening from molecular structure data Merck Malaria 2015 BenevolentAI Deep-learning and natural language processing of research literature Janssen Pharmaceutica (Johnson & Johnson), Beerse, Belgium Multiple November 8, 2016 Berg, Framingham, Massachusetts Deep-learning screening of biomarkers from patient data None Multiple N/A Exscientia Bispecific compounds via Bayesian models of ligand activity from drug discovery data Sanofi Metabolic diseases May 9, 2017 GNS Healthcare Bayesian probabilistic inference for investigating efficacy Genentech Oncology June 19, 2017 Insilico Medicine Deep-learning screening from drug and disease databases None Age-related diseases N/A Numerate Deep learning from pheno- typic data Takeda Oncology, gastro- enterology and central nervous system disorders June 12, 2017 Recursion, Salt Lake City, Utah Cellular phenotyping via image analysis Sanofi Rare genetic diseases April 25, 2016 twoXAR, Palo Alto, California Deep-learning screening from literature and assay data Santen Pharmaceuticals, Osaka, Japan Glaucoma February 23, 2017 N/A, none announced. Source: companies’ websites. N E W S
- 81. •현재 하루에 10m개의 compound 를 스크리닝 가능 •실험보다 10,000배, Ultra HTS 보다 100배 빠름 •Toxicity, side effects, mechanism of action, efficacy 등의 규명을 위해서도 사용 •머크를 포함한 10개의 제약사, 하버드 등 40개 연구 기관과 프로젝트 진행 중 •대상 질병: Alzheimer's disease, bacterial infections, antibiotics, nephrology, ophthalmology, immuno-oncology, metabolic and childhood liver diseases 등
- 82. Standigm ® Standard + NextParadigm Giant’s shoulder Artificial Intelligence Gangnam, Seoul, Founded in May 2015 www.standigm.com
- 83. Standigm AI fordrug repositioning New indication prediction Prediction interpretation Target protein prioritization Compound | Disease Compound | Pathways | Disease Compound | Binding Targets on Pathways | Disease LINCS L1000 The deep learning algorithm trained with millions of drug- perturbed gene expression responses on various cell lines The massive biological knowledge graph database integrated automatically from various drug- disease-target resources The drug structure embedded machine learning algorithm for binding affinity prediction
- 85. Outcomes Standigm generated tensof drug candidates for diverse diseases. The candidates have been experimentally validated with our collaboration partners. Cancer with CrystalGenomics, Inc. toward lead optimization (2 hits out of 10 initial candidates) Parkinson’s disease with Ajou University (College of Pharmacy) under validating with animal model (1 hit out of 7 initial candidates) Autism with Korea Institute of Science and Technology under validating with animal model (10 initial candidates) Fatty liver disease (In-house project) validated with gut-liver on a chip (7 hits out of 7 initial candidates) Mitochondrial diseases (In-house project) establishing experimental plans with domain experts (3 initial candidates) Small projects with a Japanese pharmaceutical company
- 86. Collaboration New indication prediction Prediction interpretation Target protein prioritization Standigm basicallyaims at exclusive partnership with our collaborators. Basic pipeline *Additional customized modules can be developed to pursue the best results upon discussion The total service fee depends on: • The number of compounds • Range of the selected disease area • Marketability of the selected disease area The rate of up-front depends on: • Ownership of the developed product • Ownership of the produced information during collaboration (Exclusive for collaborator or joint ownership) * L1000 profiling service fee by Genometry is not included.
- 87. AnalysisTarget Discovery AnalysisLeadDiscovery Clinical Trial Post Market Surveillance Digital Healthcare in Drug Development •환자 모집 •데이터 측정: 센서&웨어러블 •디지털 표현형 •복약 순응도
- 89. •복잡한 의료 데이터의분석 및 insight 도출 •영상 의료/병리 데이터의 분석/판독 •연속 데이터의 모니터링 및 예방/예측 인공지능의 의료 활용
- 95. Annals of Oncology(2016) 27 (suppl_9): ix179-ix180. 10.1093/annonc/mdw601 Validation study to assess performance of IBM cognitive computing system Watson for oncology with Manipal multidisciplinary tumour board for 1000 consecutive cases: An Indian experience • MMDT(Manipal multidisciplinary tumour board) treatment recommendation and data of 1000 cases of 4 different cancers breast (638), colon (126), rectum (124) and lung (112) which were treated in last 3 years was collected. • Of the treatment recommendations given by MMDT, WFO provided 50% in REC, 28% in FC, 17% in NREC • Nearly 80% of the recommendations were in WFO REC and FC group • 5% of the treatment provided by MMDT was not available with WFO • The degree of concordance varied depending on the type of cancer • WFO-REC was high in Rectum (85%) and least in Lung (17.8%) • high with TNBC (67.9%); HER2 negative (35%) • WFO took a median of 40 sec to capture, analyze and give the treatment. (vs MMDT took the median time of 15 min)
- 96. WFO in ASCO2017 • Early experience with IBM WFO cognitive computing system for lung and colorectal cancer treatment (마니팔 병원) • 지난 3년간: lung cancer(112), colon cancer(126), rectum cancer(124) • lung cancer: localized 88.9%, meta 97.9% • colon cancer: localized 85.5%, meta 76.6% • rectum cancer: localized 96.8%, meta 80.6% Performance of WFO in India 2017 ASCO annual Meeting, J Clin Oncol 35, 2017 (suppl; abstr 8527)
- 97. Empowering the OncologyCommunity for Cancer Care Genomics Oncology Clinical Trial Matching Watson Health’s oncology clients span more than 35 hospital systems “Empowering the Oncology Community for Cancer Care” Andrew Norden, KOTRA Conference, March 2017, “The Future of Health is Cognitive”
- 98. IBM Watson Health Watsonfor Clinical Trial Matching (CTM) 18 1. According to the National Comprehensive Cancer Network (NCCN) 2. http://csdd.tufts.edu/files/uploads/02_-_jan_15,_2013_-_recruitment-retention.pdf© 2015 International Business Machines Corporation Searching across eligibility criteria of clinical trials is time consuming and labor intensive Current Challenges Fewer than 5% of adult cancer patients participate in clinical trials1 37% of sites fail to meet minimum enrollment targets. 11% of sites fail to enroll a single patient 2 The Watson solution • Uses structured and unstructured patient data to quickly check eligibility across relevant clinical trials • Provides eligible trial considerations ranked by relevance • Increases speed to qualify patients Clinical Investigators (Opportunity) • Trials to Patient: Perform feasibility analysis for a trial • Identify sites with most potential for patient enrollment • Optimize inclusion/exclusion criteria in protocols Faster, more efficient recruitment strategies, better designed protocols Point of Care (Offering) • Patient to Trials: Quickly find the right trial that a patient might be eligible for amongst 100s of open trials available Improve patient care quality, consistency, increased efficiencyIBM Confidential
- 99. •총 16주간 HOG(Highlands Oncology Group)의 폐암과 유방암 환자 2,620명을 대상 •90명의 환자를 3개의 노바티스 유방암 임상 프로토콜에 따라 선별 •임상 시험 코디네이터: 1시간 50분 •Watson CTM: 24분 (78% 시간 단축) •Watson CTM은 임상 시험 기준에 해당되지 않는 환자 94%를 자동으로 스크리닝
- 100. •메이요 클리닉의 유방암신약 임상시험에 등록자의 수가 80% 증가하였다는 결과 발표
- 101. AnalysisTarget Discovery AnalysisLeadDiscovery Clinical Trial Post Market Surveillance Digital Healthcare in Drug Development •환자 모집 •데이터 측정: 센서&웨어러블 •디지털 표현형 •복약 순응도
- 102.
- 104.
- 105. https://clinicaltrials.gov/ct2/results?term=fitbit&Search=Search •의료기기가 아님에도 Fitbit은 이미 임상 연구에 폭넓게 사용되고 있음 •Fitbit 이 장려하지 않았음에도, 임상 연구자들이 자발적으로 사용 •Fitbit 을 이용한 임상 연구 수는 계속 증가하는 추세 (16.3(80), 16.8(113), 17.7(173))
- 107. •Fitbit이 임상연구에 활용되는것은 크게 두 가지 경우 •Fitbit 자체가 intervention이 되어서 활동량이나 치료 효과를 증진시킬 수 있는지 여부 •연구 참여자의 활동량을 모니터링 하기 위한 수단 •1. Fitbit으로 환자의 활동량을 증가시키기 위한 연구들 •Fitbit이 소아 비만 환자의 활동량을 증가시키는지 여부를 연구 •Fitbit이 위소매절제술을 받은 환자들의 활동량을 증가시키는지 여부 •Fitbit이 젊은 낭성 섬유증 (cystic fibrosis) 환자의 활동량을 증가시키는지 여부 •Fitbit이 암 환자의 신체 활동량을 증가시키기 위한 동기부여가 되는지 여부 •2. Fitbit으로 임상 연구에 참여하는 환자의 활동량을 모니터링 •항암 치료를 받은 환자들의 건강과 예후를 평가하는데 fitbit을 사용 •현금이 자녀/부모의 활동량을 증가시키는지 파악하기 위해 fitbit을 사용 •Brain tumor 환자의 삶의 질 측정을 위해 다른 survey 결과와 함께 fitbit을 사용 •말초동맥 질환(Peripheral Artery Disease) 환자의 활동량을 평가하기 위해
- 108. •체중 감량이 유방암재발에 미치는 영향을 연구 •유방암 환자들 중 20%는 재발, 대부분이 전이성 유방암 •과체중은 유방암의 위험을 높인다고 알려져 왔으며, •비만은 초기 유방암 환자의 예후를 좋지 않게 만드는 것도 알려짐 •하지만, 체중 감량과 유방암 재발 위험도의 상관관계 연구는 아직 없음 •3,200 명의 과체중, 초기 비만 유방암 환자들이 2년간 참여 •결과에 따라 전세계 유방암 환자의 표준 치료에 체중 감량이 포함될 가능성 •Fitbit 이 체중 감량 프로그램에 대한 지원 •Fitbit Charge HR: 운동량, 칼로리 소모, 심박수 측정 •Fitbit Aria Wi-Fi Smart Scale: 스마트 체중계 •FitStar: 개인 맞춤형 동영상 운동 코칭 서비스 2016. 4. 27.
- 110.
- 111. •Biogen Idec, 다발성경화증 환자의 모니터링에 Fitbit을 사용 •고가의 약 효과성을 검증하여 보험 약가 유지 목적 •정교한 측정으로 MS 전조 증상의 조기 발견 가능? Dec 23, 2014
- 112.
- 117. (“FREE VERTICAL MOMENTSAND TRANSVERSE FORCES IN HUMAN WALKING AND THEIR ROLE IN RELATION TO ARM-SWING”, YU LI*, WEIJIE WANG, ROBIN H. CROMPTON AND MICHAEL M. GUNTHER) (“SYNTHESIS OF NATURAL ARM SWING MOTION IN HUMAN BIPEDAL WALKING”, JAEHEUNG PARK) ︎ Right Arm Left Foot Left Arm Right Foot “보행 시 팔의 움직임은 몸의 역학적 균형을 맞추기 위한 자동적인 행동 으로, 반대쪽 발의 움직임을 관찰할 수 있는 지표” 보행 종류에 따른 신체 운동 궤도의 변화 발의 모양 팔의 스윙 궤도 일반 보행 팔자 걸음 구부린 걸음 직토 워크에서 수집하는 데이터 종류 설명 비고 충격량 발에 전해지는 충격량 분석 Impact Score 보행 주기 보행의 주기 분석 Interval Score 보폭 단위 보행 시의 거리 Stride(향후 보행 분석 고도화용) 팔의 3차원 궤도 걸음에 따른 팔의 움직임 궤도 팔의 Accel,Gyro Data 취합 보행 자세 상기 자료를 분석한 보행 자세 분류 총 8가지 종류로 구분 비대칭 지수 신체 부위별(어깨, 허리, 골반) 비대칭 점수 제공 1주일 1회 반대쪽 손 착용을 통한 데이터 취득 필요 걸음걸이 템플릿 보행시 발생하는 특이점들을 추출하여 개인별 템플릿 저장 생체 인증 기능용 with the courtesy of ZIKTO, Inc
- 118. Smart Band detectingseizure
- 119. https://www.empatica.com/science Monitoring the AutonomicNervous System “Sympathetic activation increases when you experience excitement or stress whether physical, emotional, or cognitive.The skin is the only organ that is purely innervated by the sympathetic nervous system.” https://www.empatica.com/science
- 120. from the talkof Professor Rosalind W. Picard @ Univ of Michigan 2015
- 121.
- 122.
- 124.
- 126.
- 127.
- 129. • 아이폰의 센서로측정한 자신의 의료/건강 데이터를 플랫폼에 공유 가능 • 가속도계, 마이크, 자이로스코프, GPS 센서 등을 이용 • 걸음, 운동량, 기억력, 목소리 떨림 등등 • 기존의 의학연구의 문제를 해결: 충분한 의료 데이터의 확보 • 연구 참여자 등록에 물리적, 시간적 장벽을 제거 (1번/3개월 ➞ 1번/1초) • 대중의 의료 연구 참여 장려: 연구 참여자의 수 증가 • 발표 후 24시간 내에 수만명의 연구 참여자들이 지원 • 사용자 본인의 동의 하에 진행 ResearchKit
- 130. •초기 버전으로, 5가지질환에 대한 앱 5개를 소개 ResearchKit
- 131.
- 132.
- 133.
- 134. http://www.roche.com/media/store/roche_stories/roche-stories-2015-08-10.htm pRED app totrack Parkinson’s symptoms in drug trial
- 135. Autism and BeyondEpiWatchMole Mapper measuring facial expressions of young patients having autism measuring morphological changes of moles measuring behavioral data of epilepsy patients
- 136. •스탠퍼드의 심혈관 질환연구 앱, myHeart • 발표 하루만에 11,000 명의 참가자가 등록 • 스탠퍼드의 해당 연구 책임자 앨런 영, “기존의 방식으로는 11,000명 참가자는 미국 전역의 50개 병원에서 1년간 모집해야 한다”
- 137. •파킨슨 병 연구앱, mPower • 발표 하루만에 5,589 명의 참가자가 등록 • 기존에 6000만불을 들여 5년 동안 모집한 환자의 수는 단 800명
- 138. the manifestations ofdisease by providing a more comprehensive and nuanced view of the experience of illness. Through the lens of the digital phenotype, an individual’s interaction The digital phenotype Sachin H Jain, Brian W Powers, Jared B Hawkins & John S Brownstein In the coming years, patient phenotypes captured to enhance health and wellness will extend to human interactions with digital technology. In 1982, the evolutionary biologist Richard Dawkins introduced the concept of the “extended phenotype”1, the idea that pheno- types should not be limited just to biological processes, such as protein biosynthesis or tissue growth, but extended to include all effects that a gene has on its environment inside or outside ofthebodyoftheindividualorganism.Dawkins stressed that many delineations of phenotypes are arbitrary. Animals and humans can modify their environments, and these modifications andassociatedbehaviorsareexpressionsofone’s genome and, thus, part of their extended phe- notype. In the animal kingdom, he cites damn buildingbybeaversasanexampleofthebeaver’s extended phenotype1. Aspersonaltechnologybecomesincreasingly embedded in human lives, we think there is an important extension of Dawkins’s theory—the notion of a ‘digital phenotype’. Can aspects of ourinterfacewithtechnologybesomehowdiag- nosticand/orprognosticforcertainconditions? Can one’s clinical data be linked and analyzed together with online activity and behavior data to create a unified, nuanced view of human dis- ease?Here,wedescribetheconceptofthedigital phenotype. Although several disparate studies have touched on this notion, the framework for medicine has yet to be described. We attempt to define digital phenotype and further describe the opportunities and challenges in incorporat- ing these data into healthcare. Jan. 2013 0.000 0.002 0.004 Density 0.006 July 2013 Jan. 2014 July 2014 User 1 User 2 User 3 User 4 User 5 User 6 User 7 Date Figure 1 Timeline of insomnia-related tweets from representative individuals. Density distributions (probability density functions) are shown for seven individual users over a two-year period. Density on the y axis highlights periods of relative activity for each user. A representative tweet from each user is shown as an example. npg©2015NatureAmerica,Inc.Allrightsreserved. http://www.nature.com/nbt/journal/v33/n5/full/nbt.3223.html
- 139.
- 140.
- 141.
- 142.
- 144. Digital Phenotype: Your smartphoneknows if you are depressed Ginger.io
- 145. Ginger.io •문자를 얼마나 자주하는지 •통화를 얼마나 오래하는지 •누구와 통화를 하는지 •얼마나 거리를 많이 이동했는지 •얼마나 많이 움직였는지 • UCSF, McLean Hospital: 정신질환 연구 • Novant Health: 당뇨병, 산후 우울증 연구 • UCSF, Duke: 수술 후 회복 모니터링
- 146. Digital Phenotype: Your smartphoneknows if you are depressed J Med Internet Res. 2015 Jul 15;17(7):e175. The correlation analysis between the features and the PHQ-9 scores revealed that 6 of the 10 features were significantly correlated to the scores: • strong correlation: circadian movement, normalized entropy, location variance • correlation: phone usage features, usage duration and usage frequency
- 147. Digital Phenotype: Your smartphoneknows if you are depressed J Med Internet Res. 2015 Jul 15;17(7):e175. Comparison of location and usage feature statistics between participants with no symptoms of depression (blue) and the ones with (red). (ENT, entropy; ENTN, normalized entropy; LV, location variance; HS, home stay;TT, transition time;TD, total distance; CM, circadian movement; NC, number of clusters; UF, usage frequency; UD, usage duration). Figure 4. Comparison of location and usage feature statistics between participants with no symptoms of depression (blue) and the ones with (red). Feature values are scaled between 0 and 1 for easier comparison. Boxes extend between 25th and 75th percentiles, and whiskers show the range. Horizontal solid lines inside the boxes are medians. One, two, and three asterisks show significant differences at P<.05, P<.01, and P<.001 levels, respectively (ENT, entropy; ENTN, normalized entropy; LV, location variance; HS, home stay; TT, transition time; TD, total distance; CM, circadian movement; NC, number of clusters; UF, usage frequency; UD, usage duration). Figure 5. Coefficients of correlation between location features. One, two, and three asterisks indicate significant correlation levels at P<.05, P<.01, and P<.001, respectively (ENT, entropy; ENTN, normalized entropy; LV, location variance; HS, home stay; TT, transition time; TD, total distance; CM, circadian movement; NC, number of clusters). Saeb et alJOURNAL OF MEDICAL INTERNET RESEARCH the variability of the time the participant spent at the location clusters what extent the participants’ sequence of locations followed a circadian rhythm. home stay
- 148. Reece & Danforth,“Instagram photos reveal predictive markers of depression” (2016) higher Hue (bluer) lower Saturation (grayer) lower Brightness (darker) 인스타그램으로 당신이 우울한지 알 수 있을까?
- 149. Digital Phenotype: Your Instagramknows if you are depressed Rao (MVR) (24) . Results Both Alldata and Prediagnosis models were decisively superior to a null model . Alldata predictors were significant with 99% probability.57.5;(KAll = 1 K 49.8) Pre = 1 7 Prediagnosis and Alldata confidence levels were largely identical, with two exceptions: Prediagnosis Brightness decreased to 90% confidence, and Prediagnosis posting frequency dropped to 30% confidence, suggesting a null predictive value in the latter case. Increased hue, along with decreased brightness and saturation, predicted depression. This means that photos posted by depressed individuals tended to be bluer, darker, and grayer (see Fig. 2). The more comments Instagram posts received, the more likely they were posted by depressed participants, but the opposite was true for likes received. In the Alldata model, higher posting frequency was also associated with depression. Depressed participants were more likely to post photos with faces, but had a lower average face count per photograph than healthy participants. Finally, depressed participants were less likely to apply Instagram filters to their posted photos. Fig. 2. Magnitude and direction of regression coefficients in Alldata (N=24,713) and Prediagnosis (N=18,513) models. Xaxis values represent the adjustment in odds of an observation belonging to depressed individuals, per Reece & Danforth, “Instagram photos reveal predictive markers of depression” (2016) Fig. 1. Comparison of HSV values. Right photograph has higher Hue (bluer), lower Saturation (grayer), and lower Brightness (darker) than left photograph. Instagram photos posted by depressed individuals had HSV values shifted towards those in the right photograph, compared with photos posted by healthy individuals. Units of observation In determining the best time span for this analysis, we encountered a difficult question: When and for how long does depression occur? A diagnosis of depression does not indicate the persistence of a depressive state for every moment of every day, and to conduct analysis using an individual’s entire posting history as a single unit of observation is therefore rather specious. At the other extreme, to take each individual photograph as units of observation runs the risk of being too granular. DeChoudhury et al. (5) looked at all of a given user’s posts in a single day, and aggregated those data into perperson, perday units of observation. We adopted this precedent of “userdays” as a unit of analysis . 5 Statistical framework We used Bayesian logistic regression with uninformative priors to determine the strength of individual predictors. Two separate models were trained. The Alldata model used all collected data to address Hypothesis 1. The Prediagnosis model used all data collected from higher Hue (bluer) lower Saturation (grayer) lower Brightness (darker)
- 150. Digital Phenotype: Your Instagramknows if you are depressed Reece & Danforth, “Instagram photos reveal predictive markers of depression” (2016) . In particular, depressedχ2 07.84, p .17e 64;( All = 9 = 9 − 1 13.80, p .87e 44)χ2Pre = 8 = 2 − 1 participants were less likely than healthy participants to use any filters at all. When depressed participants did employ filters, they most disproportionately favored the “Inkwell” filter, which converts color photographs to blackandwhite images. Conversely, healthy participants most disproportionately favored the Valencia filter, which lightens the tint of photos. Examples of filtered photographs are provided in SI Appendix VIII. Fig. 3. Instagram filter usage among depressed and healthy participants. Bars indicate difference between observed and expected usage frequencies, based on a Chisquared analysis of independence. Blue bars indicate disproportionate use of a filter by depressed compared to healthy participants, orange bars indicate the reverse.
- 151. Digital Phenotype: Your Instagramknows if you are depressed Reece & Danforth, “Instagram photos reveal predictive markers of depression” (2016) VIII. Instagram filter examples Fig. S8. Examples of Inkwell and Valencia Instagram filters. Inkwell converts color photos to blackandwhite, Valencia lightens tint. Depressed participants most favored Inkwell compared to healthy participants, Healthy participants
- 152. AnalysisTarget Discovery AnalysisLeadDiscovery Clinical Trial Post Market Surveillance Digital Healthcare in Drug Development •환자 모집 •데이터 측정: 센서&웨어러블 •디지털 표현형 •복약 순응도
- 153. Ingestible Sensor, ProteusDigital Health
- 154. Ingestible Sensor, ProteusDigital Health
- 156. IEEE Trans BiomedEng. 2014 Jul An Ingestible Sensor for Measuring Medication Adherence d again on imal was ysis were s detected, risk of ed with a his can be s during can be on, placed filling, or an edible monstrated cases, the nts of the ve release ity, visual a suitable The 0.9% of devices that went undetected represent contributions from all components of the system. For the sensor, the most likely contribution is due to physiological corner cases, where a combination of stomach environment and receiver-sensor orientation may result in a small proportion of devices (no greater than 0.9%) being missed. Table IV- Exposure and performance in clinical trials 412 subjects 20,993 ingestions Maximum daily ingestion: 34 Maximum use days: 90 days 99.1% Detection accuracy 100% Correct identification 0% False positives No SAEs / UADEs related to system Trials were conducted in the following patient populations. The number of patients in each study is indicated in parentheses: Healthy Volunteers (296), Cardiovascular disease (53), Tuberculosis (30), Psychiatry (28). SAE = Serious Adverse Event; UADE = Unanticipated Adverse Device Effect) Exposure and performance in clinical trials
- 157. Jan 12, 2015 Clinicaltrial researchers using Oracle’s software will now be able to track patients’ medication adherence with Proteus’s technology. - Measuring participant adherence to drug protocols - Identifying the optimum dosing regimen for recommended use
- 158. Sep 10, 2015 Proteusand Otsuka have submitted a sensor-embedded version of the antidepressant Abilify for FDA approval.
- 159.
- 160. Nov 13, 2017 •2017년11월 FDA는 Abilify MyCite의 시판 허가 •처방 전 환자의 동의가 필요 •환자의 사생활 침해 우려 의견도 있음 •주치의와 보호자까지 최대 4명이 복약 정보 수령 가능
- 161. Nov 13, 2017 •2017년11월 FDA는 Abilify MyCite의 시판 허가 •처방 전 환자의 동의가 필요 •환자의 사생활 침해 우려 의견도 있음 •주치의와 보호자까지 최대 4명이 복약 정보 수령 가능
- 162. AnalysisTarget Discovery AnalysisLeadDiscovery Clinical Trial Post Market Surveillance Digital Healthcare in Drug Development •SNS 기반의 PMS •블록체인 기반의 PMS
- 163. ‘Facebook for Patients’,PatientsLikeMe.com
- 164. ‘Facebook for Patients’,PatientsLikeMe.com Stephen Heywood Benjamin Heywood James Heywood Jeff Cole • In 2004, three MIT engineers established the service for their own brother who was suffered from ALS. • Until 2011, only patients of 22 chronic disease, including ALS, HIV, Parkinson’s.
- 165.
- 166. • Age/sex • Medicationhistory • E-mail When joining in PatientsLikeMe
- 167. Users can findand friends with patients like them, based on disease, stage, age, sex ... Finding Patients Like Me!
- 168. Patines can keeptheir medical journals in the ‘Wall’, recording conditions, treatments, symptoms… (They don’t have to lie, because it’s totally anonymous)
- 169. Medications he/she took ‘RealWorld’ Feedback from the Patients • How long he/she took the medication • Purpose for which he/she took the medication • Dose of the medication • Efficacy / side-effect of the medication
- 170.
- 171. X 10,000 personal journalpersonal journal personal journal personal journal personal journal personal journal personal journal personal journal Big Medical Data
- 172. Business Model ofPatientsLikeMe Sell the real world data of anonymous patients To pharmaceutical or insurace companies
- 173. 110,000+ adverse eventreports, on 1,000 different medications
- 174. •PatientsLikeMe의 모든 데이터를 Genentech과 5년간 공유하기로 계약 •과거에도 Sanofi Aventis, Merck 와 임상시험 환자 모집 등을 제휴
- 175. “FDA will assessthe platform’s feasibility as a way to generate adverse event reports, which the FDA uses to regulate drugs after their release into the market.” 2015.6.15
- 176. Lexapro (escitalopram) selective serotoninreuptake inhibitor (SSRI)
- 179. The main sideeffect reported by PatientsLikeMe users on selective serotonin reuptake inhibitor (SSRI) Lexapro (escitalopram) was “Decreased sex drive (libido),” at 24% (n = 149), whereas the clinical trial data on Lexapro report 3% (n = 715) Nat Biotech 2009 Brownstein et al. http://www.nature.com/nbt/journal/v27/n10/full/nbt1009-888.html#close
- 181. “In the presentstudy, we found that daily doses of lithium, leading to plasma levels ranging from 0.4 to 0.8 mEq/liter, delay disease progression in human patients affected by ALS.” “Lithium Delays Progression of Amyotrophic Lateral Sclerosis (PNAS, 2007)”
- 182. “Accelerated clinical discoveryusing self-reported patient data collected online and a patient-matching algorithm (Nat. Biotech., 2011)” “Here we describe an analysis of data reported on the website PatientsLikeMe by patients with amyotrophic lateral sclerosis (ALS) who experimented with lithium carbonate treatment. ... At 12 months after treatment, we found no effect of lithium on disease progression.”
- 183. 44명의 환자들을 대상으로(대조군 등으로 나눈 후) 16명의 환자들에게만 Lithium 을 투여 PatientsLikeMe에 등록된, 4,318 명의 ALS 환자들 중, 348명이 Lithium을 복용 그 중, 일정 기준을 충족하는 총 149명의 환자들을 분석 ALS는 매우 희귀하여 환자 수가 아주 적은 질환 온라인 SNS 서비스를 통해 자발적으로 데이터를 제공한 ALS 환자가, 전통적 임상 연구에 참여한 환자보다 9배 더 많았다!
- 184. Paul Wicks (Research Directorof PatientsLikeMe) “I can push a button and survey 200 ALS patients and get results in two weeks”
- 185. A trusted sourceof real-world information, providing over 30 peer-reviewed research studies.
- 186. Pharmaceutical companies arerecruiting patients for clinical trials thorough PatientsLikeMe •Pharmaceutical companies spend huge money and time to recruit patients to clinical trials. •The companies can utilize PatientsLikeMe platform to access patients pool. •Currently 40,000+ clinical trials recruit participants through PatientsLikeMe. http://www.patientslikeme.com/clinical_trials
- 187. 블록체인 기반의 탈중앙화된환자 커뮤니티를 구축하는 휴먼스케이프 차별성 : 보상체계 정보 생산의 주체인 환자들과 검증의 주체인 의료 전문가들에게 보상이 분배되어 본인의 지적 생산물에 대한 합당한 가치를 인정받습니다. 커뮤니티에 작성한 정보는 다른 환자나 의료 전문가들의 투표를 통해 그 가치를 평가받고, 합당한 보상을 받게 됩니다. 환자가 작성한 개인건강기록은 비식별화되어 블록체인에 기록, 거래됩니다. •PatientsLikeMe 등 기존의 환자 커뮤니티의 문제 •환자의 ‘자발적 참여’로 증상, 복약, 부작용 등의 데이터를 제공하므로 동기가 낮음 •플랫폼이 이 데이터를 제약사에 판매해도 환자는 정작 재정적 인센티브를 받지 못함 •블록체인 기반의 환자 커뮤니티: 데이터를 제공하고 커뮤니티에 기여하는 환자 및 의료진에게 인센티브 부여 가능 •글로벌 시장을 타겟으로, 연내 ICO 진행 예정 (Hum 토큰)
- 188. 관련 서비스의 한계 2004년설립된 PatientsLikeMe는 주로 난치병 환자들이 자신의 증상에 관한 정보를 공유하는 온라인 커뮤니티이다. 현재까지 약 60만 명의 환자들이 이 커뮤니티에 가입되어 있다. • 14년 간 가입자 수 60만 명 • 2018년 현재 활성 사용자 수 17,000여 명 • 자발적 참여의 한계 • 재주는 곰, 돈은 왕서방 환자들이 생산한 정보를 플랫폼사인 PLM이 보험사, 제약사 등에 판매, 수익을 얻는 구조. 중증 질환이 아닐수록 정보를 습득하거나 공유할 유인을 감소시켜 커뮤니티 참여율을 낮추게 되므로 다양한 범위의 건강 정보 수집에 어려움을 가져온다.
- 189. 차별성 : 보상체계 정보생산의 주체인 환자들과 검증의 주체인 의료 전문가들에게 보상이 분배되어 본인의 지적 생산물에 대한 합당한 가치를 인정받습니다. 커뮤니티에 작성한 정보는 다른 환자나 의료 전문가들의 투표를 통해 그 가치를 평가받고, 합당한 보상을 받게 됩니다. 환자가 작성한 개인건강기록은 비식별화되어 블록체인에 기록, 거래됩니다.
- 190.
- 191.
- 192.
- 193. •Digiceutical = digital+ pharmaceutical •"chemical 과 protein에 이어서 digital drug 이 세번째 종류의 신약이 될 것이다” •digital drug 은 크게 두 가지 종류 •기존의 약을 아예 대체 •기존 약을 강화(augment)
- 194. PTSD (외상 후스트레스 장애)
- 195. • PTSD는 전쟁,고문, 자연재해, 범죄, 테러 등의 심각한 사건을 경험한 후, 사 건 이후에도 그 사건에 공포감을 느끼고 트라우마를 느끼는 질환 • 환자들은 악몽을 꾸거나, 특정 장면이 영화의 회상 장면(Flashback)처 럼 재현되는 등의 증상을 가지게 되며, 사고와 연관된 자극을 회피 • 이러한 변화에 따라서 일상 사회 생활에도 어려움을 겪거나, 우울증, 분 노 장애 등을 동반하는 경우 많음 • 이라크전 참전 군인의 15.6-17.1%, 아프가니스탄 전에 참전 군인의 11.2% 가 PTSD 를 겪음 (NEJM, 2004) PTSD (외상 후 스트레스 장애)
- 196.
- 197. •PTSD 치료를 위해가장 효과적인 치료로 증명된 원리 •환자가 트라우마를 갖고 있는 상황과 기억에 지속적으로 노출시켜 스트레스와 회피 행동을 감소시키는 치료 방식 •트라우마에 대한 기억을 반복해서 떠올리게 되는데, 이러한 과정을 거치며 특정 기억과 반응의 연결고리를 약화 시킴 Prolonged Exposure Therapy (지속 노출 치료)
- 199. 지속 노출 치료의한계 • 환자들이 트라우마를 떠올리는 것에 거부감을 느끼거나, 효과적으로 상상하지 못함 • 사실 그 자체가 PTSD 의 증상의 하나 • 환자가 트라우마에 대한 기억을 생생하게 시각화하지 못하면 치료 효과 감소 어떻게 환자에게 실감나는 상황을 시각화 해줄 것인가
- 200.
- 201. VirtualVietnam •VR은 PTSD의 치료를위해 1990년대부터 활용 •최초의 시도: 버추얼 베트남 (1997) • 정글을 헤치고 나가는 시나리오 / 군용 헬리곱터가 날아가는 시나리오 • 그래픽 수준, 구현 효과 및 시나리오 등이 제한적 • 전통적 심리 치료에 효과 없던 환자 전원이 유의미한 개선 효과 “영상 속에서 베트남 사람들과 탱크를 보았어요”
- 202.
- 203.
- 204.
- 206. scores at baseline,post treatment and 3-month follow-up are in Fig group, mean Beck Anxiety Inventory scores significantly decrea (9.5) to 11.9 (13.6), (t=3.37, df=19, p < .003) and mean PHQ-9 decreased 49% from 13.3 (5.4) to 7.1 (6.7), (t=3.68, df=19, p < 0.00 Figure 4. PTSD Checklist scores across treatment Figure 5. BAI and PH The average number of sessions for this sample was just under successful treatment completers had documented mild and mode injuries, which suggest that this form of exposure can be useful PTSD Checklist scores across treatment • 연구 결과 20명의 환자들은 전반적으로 유의미한 개선을 보임 • 환자들 전체의 PCL-M 수치가 평균 54.4에서 35.6으로 감소 • 20명 중 16명은 치료 직후에 더 이상 PTSD 를 가지지 않은 것으로 나타남 • 치료가 끝난지 3개월 후에 환자들의 상태는 유지 http://www.ncbi.nlm.nih.gov/pubmed/19377167
- 207. reatment and 3-monthfollow-up are in Figure 4. For this same iety Inventory scores significantly decreased 33% from 18.6 =3.37, df=19, p < .003) and mean PHQ-9 (depression) scores 3 (5.4) to 7.1 (6.7), (t=3.68, df=19, p < 0.002) (see Figure 5). ores across treatment Figure 5. BAI and PHQ-Depression scores r of sessions for this sample was just under 11. Also, two of the mpleters had documented mild and moderate traumatic brain that this form of exposure can be usefully applied with this BAI and PHQ-Depression scores • 벡 불안 지수는 평균 18.6에서 11.9로 33% 감소 • PHQ-9 우울증 지수 역시 13.3에서 7.1로 49% 감소 • 경미한 외상성 뇌손상 (traumatic brain injury) 환자 2명에도 유의미한 효과 http://www.ncbi.nlm.nih.gov/pubmed/19377167
- 208. • Puretech Health •‘새로운 개념의 제약회사’를 추구하는 회사 • 기존의 신약 뿐만 아니라, 게임, 앱 등을 이용한 Digital Therapeutics 를 개발 • Digital Therapeutics는 최근 미국 FDA의 de novo 승인을 받기도 함
- 211. • Puretech Health •신약 파이프라인 중에는 일반적인 small molecule 등도 있지만, • Akili: ADHD, 우울증, 알츠하이머 등을 위한 인지 능력 개선 목적의 게임 (Project EVO) • Sonde: Voice biomarker 를 이용한 우울증 등 mental health의 진단 및 모니터링 목적
- 212. • Puretech Health •신약 파이프라인 중에는 일반적인 small molecule 등도 있지만, • Akili: ADHD, 우울증, 알츠하이머 등을 위한 인지 능력 개선 목적의 게임 (Project EVO) • Sonde: Voice biomarker 를 이용한 우울증 등 mental health의 진단 및 모니터링 목적
- 213. • Puretech Health •신약 파이프라인 중에는 일반적인 small molecule 등도 있지만, • Akili: ADHD, 우울증, 알츠하이머 등을 위한 인지 능력 개선 목적의 게임 (Project EVO) • Sonde: Voice biomarker 를 이용한 우울증 등 mental health의 진단 및 모니터링 목적
- 215. LETTER doi:10.1038/nature12486 Video gametraining enhances cognitive control in older adults J. A. Anguera1,2,3 , J. Boccanfuso1,3 , J. L. Rintoul1,3 , O. Al-Hashimi1,2,3 , F. Faraji1,3 , J. Janowich1,3 , E. Kong1,3 , Y. Larraburo1,3 , C. Rolle1,3 , E. Johnston1 & A. Gazzaley1,2,3,4 Cognitivecontrolisdefinedbyasetofneuralprocessesthatallowusto interact with our complex environment in a goal-directed manner1 . Humans regularly challenge these control processes when attempting to simultaneously accomplish multiple goals (multitasking), generat- ing interference as the result of fundamental information processing limitations2 . It is clear that multitasking behaviour has become ubi- quitous in today’s technologically dense world3 , and substantial evid- ence has accrued regarding multitasking difficulties and cognitive control deficits in our ageing population4 . Here we show that multi- tasking performance, as assessed with a custom-designed three- dimensional video game (NeuroRacer), exhibits a linear age-related decline from 20 to 79 years of age. By playing an adaptive version of NeuroRacer in multitasking training mode, older adults (60 to 85 years old) reduced multitasking costs compared to both an active control group and a no-contact control group, attaining levels beyond those achieved by untrained 20-year-old participants, with gains persisting for 6 months. Furthermore, age-related deficits in neural signatures of cognitive control, as measured with electroencephalo- graphy,wereremediated by multitasking training (enhanced midline frontal theta power and frontal–posterior theta coherence). Critically, thistrainingresultedinperformancebenefitsthatextendedtountrained cognitive control abilities (enhanced sustained attention and working memory), with an increase in midline frontal theta power predicting the training-induced boost in sustained attention and preservation of multitasking improvement 6 months later. These findings high- light the robust plasticity of the prefrontal cognitive control system in the ageing brain, and provide the first evidence, to our knowledge, ofhowacustom-designedvideogamecanbeusedtoassesscognitive abilities across the lifespan, evaluate underlying neural mechanisms, and serve as a powerful tool for cognitive enhancement. In a first experiment, we evaluated multitasking performance across the adult lifespan. A total of 174 participants spanning six decades of life (ages 20–79; ,30 individuals per decade) played a diagnostic version of NeuroRacertomeasuretheirperceptualdiscriminationability(‘signtask’) withandwithoutaconcurrentvisuomotortrackingtask(‘drivingtask’;see Supplementary Information for details of NeuroRacer). Performance was evaluated using two distinct game conditions: ‘sign only’ (respond as rapidly as possible to the appearance of a sign only when a green circle was present); and ‘sign and drive’ (simultaneously perform the sign task while maintaining a car in the centre of a winding road using a joystick (that is, ‘drive’; see Fig. 1a)). Perceptual discrimination performance was evaluatedusingthesignaldetectionmetricofdiscriminability(d9).A‘cost’ index was used to assess multitasking performance by calculating the percentage change in d9 from ‘sign only’ to ‘sign and drive’, such that greater cost (that is, a more negative percentage cost) indicates increased interference when simultaneously engaging in the two tasks (see Methods Summary). Prior to the assessment of multitasking costs, an adaptive staircase algorithm was used to determine the difficulty levels of the game at which each participant performed the perceptual discrimination and visuomotor tracking tasks in isolation at ,80% accuracy. These levels were then used to set the parameters of the component tasks in the multitasking condition, so that each individual played the game at a customizedchallengelevel.Thisensuredthatcomparisonswouldinform differences in the ability to multitask, and not merely reflect disparities in component skills (see Methods, Supplementary Figs 1 and 2, and Sup- plementary Information for more details). Multitasking performance diminished significantly across the adult lifespan in a linear fashion (that is, increasing cost, see Fig. 2a and Sup- plementaryTable1),withtheonlysignificantdifferenceincostbetween adjacent decades being the increase from the twenties (226.7% cost) to the thirties (238.6% cost). This deterioration in multitasking perform- ance is consistent with the pattern of performance decline across the lifespan observed for fluid cognitive abilities, such as reasoning5 and working memory6 . Thus, using NeuroRacer as a performance assess- ment tool, we replicated previously evidenced age-related multitasking deficits7,8 , and revealed that multitasking performance declines linearly as we advance in age beyond our twenties. In a second experiment, we explored whether older adults who trained by playing NeuroRacer in multitasking mode would exhibit improve- mentsintheirmultitaskingperformanceonthegame9,10 (thatis,diminished NeuroRacer costs). Critically, we also assessed whether this training 1 Department of Neurology, University of California, San Francisco, California 94158, USA. 2 Department of Physiology, University of California, San Francisco, California 94158, USA. 3 Center for Integrative Neuroscience, University of California, San Francisco, California 94158, USA. 4 Department of Psychiatry, University of California, San Francisco, California 94158, USA. 1 month MultitaskingSingle taskNo-contact control Initial visit NeuroRacer EEG and cognitive testing Drive only Sign only Sign and drive and 1 hour × 3 times per week × 1 month or Single task Multitask 6+ months Training intervention NeuroRacer or a b + + Figure 1 | NeuroRacer experimental conditions and training design. a, Screen shot captured during each experimental condition. b, Visualization of training design and measures collected at each time point. 5 S E P T E M B E R 2 0 1 3 | V O L 5 0 1 | N A T U R E | 9 7 Macmillan Publishers Limited. All rights reserved©2013
- 216. Video game trainingenhances cognitive control in older adults https://www.youtube.com/watch?v=1xPX8F_wl0c
- 217. transferred to enhancementsin their cognitive control abilities11 beyond those attained by participants who trained on the component tasks in isolation. In designing the multitasking training version of NeuroRacer, during game play as a key mechanistic feature of the tr In addition, although cost reduction was observed o group, equivalent improvement in component task sk byboth STTandMTT(seeSupplementary Figs 4 and that enhancedmultitaskingabilitywas notsolelyther component skills, but a function of learning to res generated by the two tasks when performed concurr the d9 cost improvement following training was not th trade-off, as driving performance costs also diminish group from pre- to post-training (see Supplementa Notably in the MTT group, the multitasking pe remained stable 6 months after training without boo 6 months, 221.9% cost). Interestingly, the MTT grou cost improved significantly beyond the cost level attai 20 year olds who played a single session of NeuroRac experiment 3; P , 0.001). Next, we assessed if training with NeuroRacer le enhancementsofcognitivecontrolabilitiesthatareknow in ageing (for example, sustained attention, divided a memory; see Supplementary Table 2)12 . We hypoth immersed in a challenging, adaptive, high-interferen for a prolonged period of time (that is, MTT) would cognitive performance on untrained tasks that also dem control. Consistent with our hypothesis, significant interactions and subsequent follow-up analyses eviden training improvements in both working memory (de task with and without distraction7 ; Fig. 3a, b) and su † –100% –90% –80% –70% –60% –50% –40% –30% –20% –10% Multitaskingcost(d′) † * ba 1 month later 6 months later Experiment 1: lifespan Experiment 2: training Single task training No-contact control Multitasking training 0% 20s 30s 40s 50s 60s 70s Initial Figure 2 | NeuroRacer multitasking costs. a, Costs across the lifespan (n 5 174) increased (that is, a more negative percentage) in a linear fashion when participants were grouped by decade (F(1,5) 5 135.7, P , 0.00001) or analysed individually (F(1,173) 5 42.8, r 5 0.45, P , 0.00001; see Supplementary Fig. 3), with significant increases in cost observed for all age groups versus the 20-year-old group (P , 0.05 for each decade comparison). b, Costs before training, 1 month post-training, and 6 months post-training showed a session X group interaction (F(4,72) 5 7.17, P , 0.0001, Cohen’s d 5 1.10), with follow-up analyses supporting a differential benefit for the MTT group (Cohen’s d for MTT vs STT 5 1.02; MTT vs NCC5 1.20). {P , 0.05 within group improvement from pre to post, *P , 0.05 between groups (n 5 46). Error bars represent s.e.m. –100 0 100 200 Pre–post WM task with distractions (RT) RTdifference(ms) † * a –100 0 100 200 Pre–p without d RTdifference(ms) † b RESEARCH LETTER Video game training enhances cognitive control in older adults z • 게임을 통한 고령층의 인지 능력 (멀티태스킹 능력) 개선 효과가 있음을 증명 • 60-85세 참가자 46명을 4주간 뉴로레이서를 통해서 훈련 • 그 결과 훈련 받지 않은 20대보다 더 잘 하게 되었으며, • 연습을 하지 않고 6개월이 지나도, 능력은 그대로 남아 있었다. Nature 501, 97–101 (2013)
- 218. Video game trainingenhances cognitive control in older adults (vigilance; test of variables of attention (T group (Fig. 3c; see Supplementary Table several statistical trendssuggestive of impro ance on other cognitive controltasks (dual- and changedetectiontask;see analysisofco in Supplementary Table 2). Note that alth and sustained attention improvements w rapid responses to test probes, neither im alternative version of the TOVA) nor accu cant group differences, revealing that traini of a speed/accuracy trade-off. Importantl ments were specific to working memory a cesses, and not theresult ofgeneralized incr as no group X session interactions were fou tasks (a stimulus detection task and the dig see Supplementary Table 2). Finally, only significant correlation between multitaski withNeuroRacer)andimprovementsonan task (delayed-recognition with distraction (Fig. 3d). These important ‘transfer of benefits’ sug lying mechanism of cognitive control was c MTT with NeuroRacer. To assess this furth basis of training effects by quantifying even tions (ERSP) and long-range phase coheren of each sign presented during NeuroRacer Wespecificallyassessedmidlinefrontalthe EEG measure of cognitive control (for exam tained attention15 and interference resolutio prefrontal cortex. In addition, we analysed between frontal and posterior brain region measure also associated with cognitive con memory14 and sustained attention15 ). Se power and coherence each revealed signifi b Long-range theta coherence Older adult post-training PLV (% coherence) 1 5 10 * ) Initial Older adults Younger adults † Midline frontal theta Power(dB) Initial * a Older adults Younger adults Older adult post-training Single task training Multitasking training No-contact control 3.40 3.05 2.70 2.35 1.65 1.30 0.95 0.60 0.25 –0.10 –0.45 –0.80 –1.15 –1.50 2.00 Nature 501, 97–101 (2013) • 인지 능력의 개선은 brain activity 로도 동일하게 관찰되었다. • 노년층 실험군에서 기술이 향상될수록 cognitive control을 관장하는 prefrontal cortex 의 activity가 높아지는 것이 관찰되었다.
- 219. OPEN ORIGINAL ARTICLE Characterizing cognitivecontrol abilities in children with 16p11.2 deletion using adaptive ‘video game’ technology: a pilot study JA Anguera1,2 , AN Brandes-Aitken1 , CE Rolle1 , SN Skinner1 , SS Desai1 , JD Bower3 , WE Martucci3 , WK Chung4 , EH Sherr1,5 and EJ Marco1,2,5 Assessing cognitive abilities in children is challenging for two primary reasons: lack of testing engagement can lead to low testing sensitivity and inherent performance variability. Here we sought to explore whether an engaging, adaptive digital cognitive platform built to look and feel like a video game would reliably measure attention-based abilities in children with and without neurodevelopmental disabilities related to a known genetic condition, 16p11.2 deletion. We assessed 20 children with 16p11.2 deletion, a genetic variation implicated in attention deficit/hyperactivity disorder and autism, as well as 16 siblings without the deletion and 75 neurotypical age-matched children. Deletion carriers showed significantly slower response times and greater response variability when compared with all non-carriers; by comparison, traditional non-adaptive selective attention assessments were unable to discriminate group differences. This phenotypic characterization highlights the potential power of administering tools that integrate adaptive psychophysical mechanics into video-game-style mechanics to achieve robust, reliable measurements. Translational Psychiatry (2016) 6, e893; doi:10.1038/tp.2016.178; published online 20 September 2016 INTRODUCTION Cognition is typically associated with measures of intelligence (for example, intellectual quotient (IQ)1 ), and is a reflection of one’s ability to perform higher-level processes by engaging specific mechanisms associated with learning, memory and reasoning. Such acts require the engagement of a specific subset of cognitive resources called cognitive control abilities,2–5 which engage the underlying neural mechanisms associated with atten- tion, working memory and goal-management faculties.6 These abilities are often assessed with validated pencil-and-paper approaches or, now more commonly with these same paradigms deployed on either desktop or laptop computers. These approaches are often less than ideal when assessing pediatric populations, as children have highly varied degree of testing engagement, leading to low test sensitivity.7–9 This is especially concerning when characterizing clinical populations, as increased performance variability in these groups often exceeds the range of testing sensitivity,7–9 limiting the ability to characterize cognitive deficits in certain populations. A proper assessment of cognitive control abilities in children is especially important, as these abilities allow children to interact with their complex environment in a goal-directed manner,10 are predictive of academic performance11 and are correlated with overall quality of life.12 For pediatric clinical populations, this characterization is especially critical as they are often assessed in an indirect fashion through intelligence quotients, parent report questionnaires13 and/or behavioral challenges,14 each of which fail to properly characterize these abilities in a direct manner. One approach to make testing more robust and user-friendly is to present material in an optimally engaging manner, a strategy particularly beneficial when assessing children. The rise of digital health technologies facilitates the ability to administer these types of tests on tablet-based technologies (that is, iPad) in a game-like manner.15 For instance, Dundar and Akcayir16 assessed tablet- based reading compared with book reading in school-aged children, and discovered that students preferred tablet-based reading, reporting it to be more enjoyable. Another approach used to optimize the testing experience involves the integration of adaptive staircase algorithms, as the incorporation of such appro- aches lead to more reliable assessments that can be completed in a timely manner. This approach, rooted in psychophysical research,17 has been a powerful way to ensure that individuals perform at their ability level on a given task, mitigating the possi- bility of floor/ceiling effects. With respect to assessing individual abilities, the incorporation of adaptive mechanics acts as a normalizing agent for each individual in accordance with their underlying cognitive abilities,18 facilitating fair comparisons between groups (for example, neurotypical and study populations). Adaptive mechanics in a consumer-style video game experi- ence could potentially assist in the challenge of interrogating cognitive abilities in a pediatric patient population. This synergistic approach would seemingly raise one’s level of engagement by making the testing experience more enjoyable and with greater sensitivity to individual differences, a key aspect typically missing in both clinical and research settings when testing these populations. Video game approaches have previously been utilized in clinical adult populations (for example, stroke,19,20 1 Department of Neurology, University of California, San Francisco, San Francisco, CA, USA; 2 Department of Psychiatry, University of California, San Francisco, San Francisco, CA, USA; 3 Akili Interactive Labs, Boston, MA, USA; 4 Department of Pediatrics, Columbia University Medical Center, New York, NY, USA and 5 Department of Pediatrics, University of California, San Francisco, San Francisco, CA, USA. Correspondence: JA Anguera or EJ Marco, University of California, San Francisco, Mission Bay – Sandler Neurosciences Center, UCSF MC 0444, 675 Nelson Rising Lane, Room 502, San Francisco, CA 94158, USA. E-mail: joaquin.anguera@ucsf.edu or elysa.marco@ucsf.edu Received 6 March 2016; revised 13 July 2016; accepted 18 July 2016 Citation: Transl Psychiatry (2016) 6, e893; doi:10.1038/tp.2016.178 www.nature.com/tp Figure 2. Project: EVO selective attention performance. (a) EVO single- and multi-tasking response time performance f non-affected siblings and non-affected control groups). (b) EVO multi-tasking RT. (c) Visual search task performance Characterizing cognitive control abilities in child JA Anguera et al •Project EVO (게임)을 통해서, •아동 집중력 장애(attention disorder) 관련 특정 유전형 carrier 를 골라낼 수 있음 •게임에서의 Response Time을 기준으로 carrier vs. non-carrier 간 유의미한 차이
- 220. RESEARCH ARTICLE A pilotstudy to determine the feasibility of enhancing cognitive abilities in children with sensory processing dysfunction Joaquin A. Anguera1,2☯ *, Anne N. Brandes-Aitken1☯ , Ashley D. Antovich1 , Camarin E. Rolle1 , Shivani S. Desai1 , Elysa J. Marco1,2,3 1 Department of Neurology, University of California, San Francisco, United States of America, 2 Department of Psychiatry, University of California, San Francisco, United States of America, 3 Department of Pediatrics, University of California, San Francisco, United States of America ☯ These authors contributed equally to this work. * joaquin.anguera@ucsf.edu Abstract Children with Sensory Processing Dysfunction (SPD) experience incoming information in atypical, distracting ways. Qualitative challenges with attention have been reported in these children, but such difficulties have not been quantified using either behavioral or functional neuroimaging methods. Furthermore, the efficacy of evidence-based cognitive control inter- ventions aimed at enhancing attention in this group has not been tested. Here we present work aimed at characterizing and enhancing attentional abilities for children with SPD. A sample of 38 SPD and 25 typically developing children were tested on behavioral, neural, and parental measures of attention before and after a 4-week iPad-based at-home cognitive remediation program. At baseline, 54% of children with SPD met or exceeded criteria on a parent report measure for inattention/hyperactivity. Significant deficits involving sustained attention, selective attention and goal management were observed only in the subset of SPD children with parent-reported inattention. This subset of children also showed reduced midline frontal theta activity, an electroencephalographic measure of attention. Following the cognitive intervention, only the SPD children with inattention/hyperactivity showed both improvements in midline frontal theta activity and on a parental report of inattention. Notably, 33% of these individuals no longer met the clinical cut-off for inattention, with the parent- reported improvements persisting for 9 months. These findings support the benefit of a targeted attention intervention for a subset of children with SPD, while simultaneously highlighting the importance of having a multifaceted assessment for individuals with neuro- developmental conditions to optimally personalize treatment. Introduction Five percent of all children suffer from Sensory Processing Dysfunction (SPD)[1], with these individuals exhibiting exaggerated aversive, withdrawal, or seeking behaviors associated with sensory inputs [2]. These sensory processing differences can have significant and lifelong con- sequences for learning and social abilities, and are often shared by children who meet PLOS ONE | https://doi.org/10.1371/journal.pone.0172616 April 5, 2017 1 / 19 a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Anguera JA, Brandes-Aitken AN, Antovich AD, Rolle CE, Desai SS, Marco EJ (2017) A pilot study to determine the feasibility of enhancing cognitive abilities in children with sensory processing dysfunction. PLoS ONE 12(4): e0172616. https://doi.org/10.1371/journal. pone.0172616 Editor: Jacobus P. van Wouwe, TNO, NETHERLANDS Received: October 5, 2016 Accepted: February 1, 2017 Published: April 5, 2017 Copyright: © 2017 Anguera et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All relevant data are within the paper and its Supporting Information files. Funding: This work was supported by the Mickelson-Brody Family Foundation, the Wallace Research Foundation, the James Gates Family Foundation, the Kawaja-Holcombe Family Foundation (EJM), and the SNAP 2015 Crowd funding effort. •감각처리장애(SPD)를 가진 소아 환자 중 ADHD를 가진 20명에 대해서 실험 •4주 동안 (주당 5일, 25분)Project EVO 게임을 하게 한 결과, •20명 중 7명이 큰 개선을 보여서 더 이상 ADHD의 범주에 들지 않게 됨 •사용 후 적어도 9개월 동안 효과가 지속되었음 Fig 4. Transfer effect on behavioral and parent report measures. Pre and post (A) response time (B) and resp revealing within group change. Error bars indicate standard error of the mean. Within group main effects of session = p .05, ** =.p .01. Sun symbols indicate statistically significant instances where SPD+IA post-training performa TDC group prior to training. (C) Vanderbilt parent report inattention change bar plot (calculated by pre-post margina significant group x session interaction. Error bars indicate standard error of the mean. All group x session interactio stars (* = p .05, ** =.p .01) on bar graph. https://doi.org/10.1371/journal.pone.0172616.g004 PLOS ONE | https://doi.org/10.1371/journal.pone.0172616 April 5, 2017
- 221. •ADHD에 대해서는 대규모RCT phase III 임상 시험 진행 중이며, FDA 의료기기 인허가 목표 •8-12살 환자(n=330), 치료 효과 없는 비디오게임을 control group으로 •primary endpoint: TOVA •의사의 처방을 받는 ADHD 치료용 게임 + 보험사의 커버 목표
- 222. • Woebot, 정신상담 챗봇 스타트업 • 스탠퍼드의 mental health 전문가들이 시작한 우울증 치료 (인지행동치료) 목적의 챗봇 • Andrew Ng 교수는 이사회장으로 참여
- 223. • Woebot, 정신상담 챗봇 • 실제 상담사들이 하듯이, 대화형으로 설명하고 사용자의 정신 건강 상태를 체크 • 대부분 설문과 다를 것이 없지만 (정해진 답 중에 하나 선택), UI 상의 혁신이라고 볼 수 있음 • 아직까지는 아주 정교한 NLP를 사용하고 있지는 않음 (세션 당 한 번 정도)
- 224. • Woebot, 정신상담 챗봇 • 실제 상담사들이 하듯이, 대화형으로 설명하고 사용자의 정신 건강 상태를 체크 • 대부분 설문과 다를 것이 없지만 (정해진 답 중에 하나 선택), UI 상의 혁신이라고 볼 수 있음 • 아직까지는 아주 정교한 NLP를 사용하고 있지는 않음 (세션 당 한 번 정도)
- 225. • Woebot, 정신상담 챗봇 • 실제 상담사들이 하듯이, 대화형으로 설명하고 사용자의 정신 건강 상태를 체크 • 대부분 설문과 다를 것이 없지만 (정해진 답 중에 하나 선택), UI 상의 혁신이라고 볼 수 있음 • 아직까지는 아주 정교한 NLP를 사용하고 있지는 않음 (세션 당 한 번 정도)
- 226. Original Paper Delivering CognitiveBehavior Therapy to Young Adults With Symptoms of Depression and Anxiety Using a Fully Automated Conversational Agent (Woebot): A Randomized Controlled Trial Kathleen Kara Fitzpatrick1* , PhD; Alison Darcy2* , PhD; Molly Vierhile1 , BA 1 Stanford School of Medicine, Department of Psychiatry and Behavioral Sciences, Stanford, CA, United States 2 Woebot Labs Inc., San Francisco, CA, United States * these authors contributed equally Corresponding Author: Alison Darcy, PhD Woebot Labs Inc. 55 Fair Avenue San Francisco, CA, 94110 United States Email: alison@woebot.io Abstract Background: Web-based cognitive-behavioral therapeutic (CBT) apps have demonstrated efficacy but are characterized by poor adherence. Conversational agents may offer a convenient, engaging way of getting support at any time. Objective: The objective of the study was to determine the feasibility, acceptability, and preliminary efficacy of a fully automated conversational agent to deliver a self-help program for college students who self-identify as having symptoms of anxiety and depression. Methods: In an unblinded trial, 70 individuals age 18-28 years were recruited online from a university community social media site and were randomized to receive either 2 weeks (up to 20 sessions) of self-help content derived from CBT principles in a conversational format with a text-based conversational agent (Woebot) (n=34) or were directed to the National Institute of Mental Health ebook, “Depression in College Students,” as an information-only control group (n=36). All participants completed Web-based versions of the 9-item Patient Health Questionnaire (PHQ-9), the 7-item Generalized Anxiety Disorder scale (GAD-7), and the Positive and Negative Affect Scale at baseline and 2-3 weeks later (T2). Results: Participants were on average 22.2 years old (SD 2.33), 67% female (47/70), mostly non-Hispanic (93%, 54/58), and Caucasian (79%, 46/58). Participants in the Woebot group engaged with the conversational agent an average of 12.14 (SD 2.23) times over the study period. No significant differences existed between the groups at baseline, and 83% (58/70) of participants provided data at T2 (17% attrition). Intent-to-treat univariate analysis of covariance revealed a significant group difference on depression such that those in the Woebot group significantly reduced their symptoms of depression over the study period as measured by the PHQ-9 (F=6.47; P=.01) while those in the information control group did not. In an analysis of completers, participants in both groups significantly reduced anxiety as measured by the GAD-7 (F1,54= 9.24; P=.004). Participants’ comments suggest that process factors were more influential on their acceptability of the program than content factors mirroring traditional therapy. Conclusions: Conversational agents appear to be a feasible, engaging, and effective way to deliver CBT. (JMIR Ment Health 2017;4(2):e19) doi:10.2196/mental.7785 KEYWORDS conversational agents; mobile mental health; mental health; chatbots; depression; anxiety; college students; digital health Introduction Up to 74% of mental health diagnoses have their first onset particularly common among college students, with more than half reporting symptoms of anxiety and depression in the previous year that were so severe they had difficulty functioning Fitzpatrick et alJMIR MENTAL HEALTH
- 227. depression at baselineas measured by the PHQ-9, while three-quarters (74%, 52/70) were in the severe range for anxiety as measured by the GAD-7. Figure 1. Participant recruitment flow. Table 1. Demographic and clinical variables of participants at baseline. WoebotInformation control Scale, mean (SD) 14.30 (6.65)13.25 (5.17)Depression (PHQ-9) 18.05 (5.89)19.02 (4.27)Anxiety (GAD-7) 25.54 (9.58)26.19 (8.37)Positive affect 24.87 (8.13)28.74 (8.92)Negative affect 22.58 (2.38)21.83 (2.24)Age, mean (SD) Gender, n (%) 7 (21)4 (7)Male 27 (79)20 (55)Female Ethnicity, n (%) 2 (6)2 (8)Latino/Hispanic 32 (94)22 (92)Non-Latino/Hispanic 28 (82)18 (75)Caucasian Fitzpatrick et alJMIR MENTAL HEALTH Delivering Cognitive Behavior Therapy toYoung Adults With Symptoms of Depression and Anxiety Using a Fully Automated Conversational Agent (Woebot):A Randomized Controlled Trial •분노장애와 우울증이 있다고 스스로 생각하는 대학생들이 사용하는 self-help 챗봇 •목적: 챗봇의 feasibility, acceptability, preliminary efficacy 를 보기 위함 •대학생 총 70명을 대상으로 2주 동안 진행 •실험군 (Woebot): 34명 •대조군 (information-only): 31명 •Oucome: PHQ-9, GAD-7
- 228. d cPFWoebotInformation-only control 95%CIb T2a 95% CIb T2a 0.44.0176.039.74-12.3211.14 (0.71)12.07-15.2713.67 (.81)PHQ-9 0.14.5810.3816.16-18.1317.35 (0.60)15.52-18.5616.84 (.67)GAD-7 0.02.7070.1724.35-29.4126.88 (1.29)23.17-28.8626.02 (1.45)PANAS positive affect 0.344.9120.9123.54-28.4225.98 (1.24)24.73-30.3227.53 (1.42)PANAS nega- tive affect a Baseline=pooled mean (standard error) b 95% confidence interval. c Cohen d shown for between-subjects effects using means and standard errors at Time 2. Figure 2. Change in mean depression (PHQ-9) score by group over the study period. Error bars represent standard error. Preliminary Efficacy Table 2 shows the results of the primary ITT analyses conducted on the entire sample. Univariate ANCOVA revealed a significant treatment effect on depression revealing that those in the Woebot group significantly reduced PHQ-9 score while those in the information control group did not (F1,48=6.03; P=.017) (see Figure 2). This represented a moderate between-groups effect size (d=0.44). This effect is robust after Bonferroni correction for multiple comparisons (P=.04). No other significant between-group differences were observed on anxiety or affect. Completer Analysis As a secondary analysis, to explore whether any main effects existed, 2x2 repeated measures ANOVAs were conducted on the primary outcome variables (with the exception of PHQ-9) among completers only. A significant main effect was observed on GAD-7 (F1,54=9.24; P=.004) suggesting that completers experienced a significant reduction in symptoms of anxiety between baseline and T2, regardless of the group to which they were assigned with a within-subjects effect size of d=0.37. No main effects were observed for positive (F1,50=.001; P=.951; d=0.21) or negative affect (F1,50=.06; P=.80; d=0.003) as measured by the PANAS. To further elucidate the source and magnitude of change in depression, repeated measures dependent t tests were conducted and Cohen d effect sizes were calculated on individual items of the PHQ-9 among those in the Woebot condition. The analysis revealed that baseline-T2 changes were observed on the following items in order of decreasing magnitude: motoric symptoms (d=2.09), appetite (d=0.65), little interest or pleasure in things (d=0.44), feeling bad about self (d=0.40), and concentration (d=0.39), and suicidal thoughts (d=0.30), feeling down (d=0.14), sleep (d=0.12), and energy (d=0.06). JMIR Ment Health 2017 | vol. 4 | iss. 2 | e19 | p.6http://mental.jmir.org/2017/2/e19/ (page number not for citation purposes) XSL•FO RenderX Change in mean depression (PHQ-9) score by group over the study period •결과 •챗봇을 2주 동안 평균 12.14번 사용함 •우울증에 대해서는 significant group difference •Woebot 그룹에서는 우울증(PHQ-9)의 유의미한 감소가 있었음 •대조군에서는 유의미한 감소 없음 •분노 장애에 대해서는 두 그룹 모두 유의미한 감소가 있었음 (GAD-7 기준)
- 230. RespeRate •FDA 승인 받은유일한 비약물 고혈압 치료법 •sessions of therapeutic breathing 을 통해서 혈압 강하 효과 •15분씩 일주일에 a few times 활용하면 significant blood pressure reduction 증명 •전세계 25만 명 이상 사용
- 231.
- 232.
- 233. 2breathe •디지털 기기 중,수면 유도 목적으로는 2breathe가 유일 •고혈압 치료기기의 ‘부작용’으로 수면 유도 효과 발견 •안전성은 수십만 명의 환자에게 임상 시험 통해서 증명 •교감신경의 활성화를 줄임으로써 사용자의 릴렉스와 수면을 유도
- 235.
- 237. Effects of virtualreality-based rehabilitation on distal upper extremity function and health-related quality of life: a single-blinded, randomized controlled trial ments at T2 and 23 completed the follow-up assessments at T3. During the study, 5 and 8 participants from the SG and CON groups, respectively, did not complete the inter- vention programs. The sample sizes at the assessment time points are presented in Fig. 2. There were no serious ad- verse events, and only 1 participant from the CON group dropped out owing to dizziness, which was unrelated to the intervention. Thus, most of the study withdrawals were related to uncooperativeness, and the number was higher than that hypothesized in the study design. At baseline, dist: F = 4.64, df = 1.38, P = 0.024). Secondary outcomes Jebsen–Taylor hand function test The JTT scores of the SG and CON groups are presented in Table 2. There were no significant differences in the JTT-total, JTT-gross, and JTT-fine scores between the 2 groups at T0. The post-hoc test found that there were sig- nificant improvements in the JTT-total, JTT-gross, and JTT-fine scores in the SG group during the intervention Fig. 2 Flowchart of the participants through the study. Abbreviations: SG, Smart Glove; CON, conventional intervention Shin et al. Journal of NeuroEngineering and Rehabilitation (2016) 13:17 Shin et al. Journal of NeuroEngineering and Rehabilitation (2016) 13:17
- 238. Effects of virtualreality-based rehabilitation on distal upper extremity function and health-related quality of life: a single-blinded, randomized controlled trial composite SIS score (F = 5.76, df = 1.0, P = 0.021) and the overall SIS score (F = 6.408, df = 1.0, P = 0.015). Moreover, among individual domain scores, the Time × standard OT than using amount-matched conventional re- habilitation, without any adverse events, in stroke survivors. Additionally, this study noted improvements in the SIS- Fig. 3 Mean and standard errors for the FM scores in the SG and CON groups. Abbreviations: FM, Fugl–Meyer assessment, SG, Smart Glove; CON, conventional intervention Fig. 4 Mean and standard errors for the JTT scores in the SG and CON groups. Abbreviations: JTT, Jebsen–Taylor hand function test; SG, Smart Glove; CON, conventional intervention Shin et al. Journal of NeuroEngineering and Rehabilitation (2016) 13:17 Page 7 of 10 composite SIS score (F = 5.76, df = 1.0, P = 0.021) and the overall SIS score (F = 6.408, df = 1.0, P = 0.015). standard OT than using amount-matched conventional re- habilitation, without any adverse events, in stroke survivors. Fig. 3 Mean and standard errors for the FM scores in the SG and CON groups. Abbreviations: FM, Fugl–Meyer assessment, SG, Smart Glove; CON, conventional intervention Fig. 4 Mean and standard errors for the JTT scores in the SG and CON groups. Abbreviations: JTT, Jebsen–Taylor hand function test; SG, Smart Glove; CON, conventional intervention Shin et al. Journal of NeuroEngineering and Rehabilitation (2016) 13:17 Page 7 of 10 Shin et al. Journal of NeuroEngineering and Rehabilitation (2016) 13:17
- 241. 14© 2017 byHURAYPOSITIVE INC., a Digital Healthcare Service Provider. This information is strictly privileged and confidential. All rights reserved. 제2형 당뇨병 환자 95% 임신성 당뇨병 환자 2% 기타 1% 정상인 당뇨병 전단계 환자 당뇨병 환자 경증합병증 동반 당뇨병 환자 중증합병증 동반 당뇨병 환자 제1형 당뇨병 환자 2% 보건복지부/건강보험공단 (국민건강증진 및 관리) 병원/제약사/보험사 (비용절감 및 고객만족) 차기 위험단계로의 적극적인 진입 억제를 위한 헬스케어 솔루션 휴레이포지티브 헬스케어 솔루션 $ key facts Products & Services 서비스 대상 & 역할
- 242. 15© 2017 byHURAYPOSITIVE INC., a Digital Healthcare Service Provider. This information is strictly privileged and confidential. All rights reserved. Products & Services 서비스 흐름 & 솔루션 구성 건강상태 데이터 실시간 데이터 분석을 통한 의료 통찰력 제공 즉각적인 알림 및 중재 경험 및 데이터 (EMR/PHR) 개인별 고객 맞춤형 중재 서비스 사용자 헬스케어 제공자 Switch Band 신체에 접촉되어 실시간 생활습관 관리를 위한 알림 및 적극적 동기부여 Health Switch 혈당, 혈압, 식사, 복약, 운동, 체중 등 생활습관 관리를 위한 모바일 앱 Switch Station 가정 내에서 효과적인 알림 및 동기부여를 통한 생활습관 관리 기기(특히, 노령환자 대상) 혈당 측정기 혈당 측정에 관한 기술적 개발 및 상용화 프로젝트 Health Switch PRO 헬스케어 제공자가 더욱 정밀하고 정확한 진단과 진료를 할 수 있도록 의료 통찰력을 제공하는 관리 소프트웨어 key facts 하드웨어와 소프트웨어 모든 분야에서의 풍부한 기술적 경험 Advanced Database Big Data/ Analytics Machine Learning Lifestyle Intervention
- 243. 16© 2017 byHURAYPOSITIVE INC., a Digital Healthcare Service Provider. This information is strictly privileged and confidential. All rights reserved. 7 7.2 7.4 7.6 7.8 8 8.2 3M 6M 9M 12M0M ▼0.63%p. ▼0.64%p. 당화혈색소(HbA1c,%) & Products & Services 의학적 유효성(Health Switch를 활용한 임상실험) 기간 • 1차 실험(0M-6M) 실험군: 중재 O ( ) 대조군: 중재 X ( ) • 2차 실험: 실험군과 대조군 교차(6M-12M) 대조군: 중재 X ( ) 실험군: 중재 O ( ) 당화혈색소 0.63%p. 감소 무의미한 변화 당화혈색소 수준 유지 당화혈색소 0.64%p. 감소 ▼0.04%p. • N = 148명 • 평균 연령: 52.2세 결과 임상 대상자 1 모바일 중재 서비스의 의미 있는 혈당 감소 효과 2 약 6개월의 서비스 후 생활습관 유지 가능성 3 고령 환자들도 사용할 수 있는 간편한 서비스 임상실험을 통해 검증된 Health Switch의 효과 key facts • 특징: 제2형 당뇨병 유병자 • 기간: 2014.10 ~ 2015.12
- 244. 1SCIENTIFIC REPORTS |(2018) 8:3642 | DOI:10.1038/s41598-018-22034-0 www.nature.com/scientificreports The effectiveness, reproducibility, and durability of tailored mobile coaching on diabetes management in policyholders:A randomized, controlled, open-label study DaYoung Lee1,2 , Jeongwoon Park3 , DooahChoi3 , Hong-YupAhn4 , Sung-Woo Park1 & Cheol-Young Park 1 This randomized, controlled, open-label study conducted in Kangbuk Samsung Hospital evaluated the effectiveness, reproducibility, and durability of tailored mobile coaching (TMC) on diabetes management.The participants included 148 Korean adult policyholders with type 2 diabetes divided into the Intervention-Maintenance (I-M) group (n=74) andControl-Intervention (C-I) group (n=74). Intervention was the addition ofTMC to typical diabetes care. In the 6-month phase 1, the I-M group receivedTMC, and theC-I group received their usual diabetes care. During the second 6-month phase 2, theC-I group receivedTMC, and the I-M group received only regular information messages.After the 6-month phase 1, a significant decrease (0.6%) in HbA1c levels compared with baseline values was observed in only the I-M group (from 8.1±1.4% to 7.5±1.1%, P<0.001 based on a paired t-test). At the end of phase 2, HbA1c levels in theC-I group decreased by 0.6% compared with the value at 6 months (from 7.9±1.5 to 7.3±1.0, P<0.001 based on a paired t-test). In the I-M group, no changes were observed. Both groups showed significant improvements in frequency of blood-glucose testing and exercise. In conclusion, addition ofTMC to conventional treatment for diabetes improved glycemic control, and this effect was maintained without individualized message feedback. The incidence and prevalence of type 2 diabetes are increasing rapidly worldwide, and the disease is expected to affect 439 million adults by 20301 . Previous large clinical trials indicated that adequate glycemic control con- tributed to a reduction in both microvascular and macrovascular complications as well as mortality rates due to diabetes2,3 . Complications from diabetes result in greater expenditure and reduced productivity. Therefore, it is a socioeconomic concern4,5 . Adequate glycemic control is important not only as an individual health problem, but also as a challenge to healthcare systems worldwide. However, approximately 40% of subjects with diabetes in the United States do not meet the recommended target for glycemic control, low-density lipoprotein cholesterol (LDL-C) level, or blood pressure (BP)6 . In Korea, glycated hemoglobin (HbA1c) levels for nearly half of diabetic patients were above 7.0%7 . Although successful diabetes care requires therapeutic lifestyle modification in addition to proper medica- tion8–10 , only 55% of individuals with type 2 diabetes receive diabetes education from healthcare professionals11 , and 16% report adhering to recommended self-management activities9 . Multifaceted professional inter- ventions are needed to support patient efforts for behavior change including healthy lifestyle choices, disease self-management, and prevention of diabetes complications10 . 1 Division of Endocrinology and Metabolism, Department of Internal Medicine, Kangbuk Samsung Hospital, SungkyunkwanUniversitySchool of Medicine,Seoul, Republic of Korea.2 Division of Endocrinology and Metabolism, Department of Internal Medicine, KoreaUniversityCollege of Medicine,Seoul, Republic of Korea.3 Huraypositive Inc. Sinsa-dong, Gangnam-gu, Seoul, Republic of Korea. 4 Department of Statistics, Dongguk University-Seoul, Seoul, Republic of Korea. Correspondence and requests for materials should be addressed to C.-Y.P. (email: cydoctor@ chol.com) Received: 29 November 2017 Accepted: 15 February 2018 Published: xx xx xxxx OPEN
- 245. 1SCIENTIFIC REPORTS |(2018) 8:3642 | DOI:10.1038/s41598-018-22034-0 www.nature.com/scientificreports The effectiveness, reproducibility, and durability of tailored mobile coaching on diabetes management in policyholders:A randomized, controlled, open-label study DaYoung Lee1,2 , Jeongwoon Park3 , DooahChoi3 , Hong-YupAhn4 , Sung-Woo Park1 & Cheol-Young Park 1 This randomized, controlled, open-label study conducted in Kangbuk Samsung Hospital evaluated the effectiveness, reproducibility, and durability of tailored mobile coaching (TMC) on diabetes management.The participants included 148 Korean adult policyholders with type 2 diabetes divided into the Intervention-Maintenance (I-M) group (n=74) andControl-Intervention (C-I) group (n=74). Intervention was the addition ofTMC to typical diabetes care. In the 6-month phase 1, the I-M group receivedTMC, and theC-I group received their usual diabetes care. During the second 6-month phase 2, theC-I group receivedTMC, and the I-M group received only regular information messages.After the 6-month phase 1, a significant decrease (0.6%) in HbA1c levels compared with baseline values was observed in only the I-M group (from 8.1±1.4% to 7.5±1.1%, P<0.001 based on a paired t-test). At the end of phase 2, HbA1c levels in theC-I group decreased by 0.6% compared with the value at 6 months (from 7.9±1.5 to 7.3±1.0, P<0.001 based on a paired t-test). In the I-M group, no changes were observed. Both groups showed significant improvements in frequency of blood-glucose testing and exercise. In conclusion, addition ofTMC to conventional treatment for diabetes improved glycemic control, and this effect was maintained without individualized message feedback. The incidence and prevalence of type 2 diabetes are increasing rapidly worldwide, and the disease is expected to affect 439 million adults by 20301 . Previous large clinical trials indicated that adequate glycemic control con- tributed to a reduction in both microvascular and macrovascular complications as well as mortality rates due to diabetes2,3 . Complications from diabetes result in greater expenditure and reduced productivity. Therefore, it is a socioeconomic concern4,5 . Adequate glycemic control is important not only as an individual health problem, but also as a challenge to healthcare systems worldwide. However, approximately 40% of subjects with diabetes in the United States do not meet the recommended target for glycemic control, low-density lipoprotein cholesterol (LDL-C) level, or blood pressure (BP)6 . In Korea, glycated hemoglobin (HbA1c) levels for nearly half of diabetic patients were above 7.0%7 . Although successful diabetes care requires therapeutic lifestyle modification in addition to proper medica- tion8–10 , only 55% of individuals with type 2 diabetes receive diabetes education from healthcare professionals11 , and 16% report adhering to recommended self-management activities9 . Multifaceted professional inter- ventions are needed to support patient efforts for behavior change including healthy lifestyle choices, disease self-management, and prevention of diabetes complications10 . 1 Division of Endocrinology and Metabolism, Department of Internal Medicine, Kangbuk Samsung Hospital, SungkyunkwanUniversitySchool of Medicine,Seoul, Republic of Korea.2 Division of Endocrinology and Metabolism, Department of Internal Medicine, KoreaUniversityCollege of Medicine,Seoul, Republic of Korea.3 Huraypositive Inc. Sinsa-dong, Gangnam-gu, Seoul, Republic of Korea. 4 Department of Statistics, Dongguk University-Seoul, Seoul, Republic of Korea. Correspondence and requests for materials should be addressed to C.-Y.P. (email: cydoctor@ chol.com) Received: 29 November 2017 Accepted: 15 February 2018 Published: xx xx xxxx OPEN e.com/scientificreports/ Figure 3. Changes in means and standard errors of glycated hemoglobin (H study period. HbA1c levels of the C-I group who received TMC during phase 2 of the study decreased by 0.6% compared to phase 1 levels. In the I-M group, initial improvement in HbA1c levels at 3 months continued until 12 months. Consequently, HbA1c levels in both the C-I and I-M groups decreased significantly compared to baseline values over the 12-month study period.
- 247. 보험사 환자 사용료 스타트업 보험료 데이터 질병 관리 서비스 “사업적수익” “보험금 지급 감소” “질병 관리”
- 250. Weight loss efficacyof a novel mobile Diabetes Prevention Program delivery platform with human coaching Andreas Michaelides, Christine Raby, Meghan Wood, Kit Farr, Tatiana Toro-Ramos To cite: Michaelides A, Raby C, Wood M, et al. Weight loss efficacy of a novel mobile Diabetes Prevention Program delivery platform with human coaching. BMJ Open Diabetes Research and Care 2016;4:e000264. doi:10.1136/bmjdrc-2016- 000264 Received 4 May 2016 Revised 19 July 2016 Accepted 11 August 2016 Noom, Inc., New York, New York, USA Correspondence to Dr Andreas Michaelides; andreas@noom.com ABSTRACT Objective: To evaluate the weight loss efficacy of a novel mobile platform delivering the Diabetes Prevention Program. Research Design and Methods: 43 overweight or obese adult participants with a diagnosis of prediabetes signed-up to receive a 24-week virtual Diabetes Prevention Program with human coaching, through a mobile platform. Weight loss and engagement were the main outcomes, evaluated by repeated measures analysis of variance, backward regression, and mediation regression. Results: Weight loss at 16 and 24 weeks was significant, with 56% of starters and 64% of completers losing over 5% body weight. Mean weight loss at 24 weeks was 6.58% in starters and 7.5% in completers. Participants were highly engaged, with 84% of the sample completing 9 lessons or more. In-app actions related to self-monitoring significantly predicted weight loss. Conclusions: Our findings support the effectiveness of a uniquely mobile prediabetes intervention, producing weight loss comparable to studies with high engagement, with potential for scalable population health management. INTRODUCTION Lifestyle interventions,1 including the National Diabetes Prevention Program (NDPP) have proven effective in preventing type 2 diabetes.2 3 Online delivery of an adapted NDPP has resulted in high levels of engagement, weight loss, and improvements in glycated hemoglobin (HbA1c).4 5 Prechronic and chronic care efforts delivered by other means (text and emails,6 nurse support,7 DVDs,8 community care9 ) have also been successful in promoting behavior change, weight loss, and glycemic control. One study10 adapted the NDPP to deliver the first part of the curriculum in-person and the remaining sessions through a mobile app, and found 6.8% weight loss at 5 months. Mobile health poses a promising means of delivering prechronic and chronic care,11 12 and provides a scalable, convenient, and accessible method to deliver the NDPP. The weight loss efficacy of a completely mobile delivery of a structured NDPP has not been tested. The main aim of this pilot study was to evaluate the weight loss efficacy of Noom’s smartphone-based NDPP-based cur- ricula with human coaching in a group of overweight and obese hyperglycemic adults receiving 16 weeks of core, plus postcore cur- riculum. In this study, it was hypothesized that the mobile DPP could produce trans- formative weight loss over time. RESEARCH DESIGN AND METHODS A large Northeast-based insurance company offered its employees free access to Noom Health, a mobile-based application that deli- vers structured curricula with human coaches. An email or regular mail invitation with information describing the study was sent to potential participants based on an elevated HbA1c status found in their medical records, reflecting a diagnosis of prediabetes. Interested participants were assigned to a virtual Centers for Disease Control and Prevention (CDC)-recognized NDPP master’s level coach. Key messages ▪ To the best of our knowledge, this study is the first fully mobile translation of the Diabetes Prevention Program. ▪ A National Diabetes Prevention Program (NDPP) intervention delivered entirely through a smart- phone platform showed high engagement and 6-month transformative weight loss, comparable to the original NDPP and comparable to trad- itional in-person programmes. ▪ This pilot shows that a novel mobile NDPP inter- vention has the potential for scalability, and can address the major barriers facing the widespread translation of the NDPP into the community setting, such as a high fixed overhead, fixed locations, and lower levels of engagement and weight loss. BMJ Open Diabetes Research and Care 2016;4:e000264. doi:10.1136/bmjdrc-2016-000264 1 Open Access Research group.bmj.comon April 27, 2017 - Published byhttp://drc.bmj.com/Downloaded from •Noom Coach 앱이 체중 감량을 위해서 효과적임을 증명 •완전히 모바일로 이뤄진 최초의 당뇨병 예방 연구 •43명의 전당뇨단계에 있는 과체중이나 비만 환자를 대상 •24주간 Noom Coach의 앱과 모바일 코칭을 제공 •그 결과 64% 의 참가자들이 5-7% 의 체중 감량 효과 •84%에 달하는 사람들이 마지막까지 이 6개월 간의 프로그램에 참여
- 251. www.nature.com/scientificreports Successful weight reduction andmaintenance by using a smartphone application in those with overweight and obesity SangOukChin1,* ,Changwon Keum2,* , JunghoonWoo3 , Jehwan Park2 , Hyung JinChoi4 , Jeong-taekWoo5 & SangYoul Rhee5 A discrepancy exists with regard to the effect of smartphone applications (apps) on weight reduction due to the several limitations of previous studies.This is a retrospective cohort study, aimed to investigate the effectiveness of a smartphone app on weight reduction in obese or overweight individuals, based on the complete enumeration study that utilized the clinical and logging data entered by NoomCoach app users betweenOctober 2012 andApril 2014.A total of 35,921 participants were included in the analysis, of whom 77.9% reported a decrease in body weight while they were using the app (median 267 days; interquartile range=182). Dinner input frequency was the most important factor for successful weight loss (OR=10.69; 95%CI=6.20–19.53; p<0.001), and more frequent input of weight significantly decreased the possibility of experiencing the yo-yo effect (OR=0.59, 95%CI=0.39–0.89; p<0.001).This study demonstrated the clinical utility of an app for successful weight reduction in the majority of the app users; the effects were more significant for individuals who monitored their weight and diet more frequently. Obesity is a global epidemic with a rapidly increasing prevalence worldwide1,2 . As obese individuals experience significantly higher mortality when compared with the non-obese population3,4 , this phenomenon poses a sig- nificant socioeconomic burden, necessitating strategies to manage overweight and prevent obesity5 . Although numerous interventions such as life style modification including exercise6–10 , and pharmacotherapy11–13 have been shown effective for both the prevention and treatment of obesity, some of these methods were found to have a limitation which required substantial financial inputs and repeated time-consuming processes14,15 . Recently, as the number of smartphone users is increasing dramatically, many investigators have attempted to implement smartphone applications (app) for health promotion16–19 . Consequently, many smartphone apps have demonstrated at least partial efficacy in promoting successful weight reduction according to the number of previous studies20–24 . However, due to the limitations associated with study design such as small-scale studies and short investigation periods, a discrepancy exists with regard to the effect of apps on weight reduction20,21,23 . Even systemic reviews which investigated the efficacy of mobile apps for weight reduction reported more or less inconsistent results; Flores Mateo et al. reported a significant weight loss by mobile phone app intervention when compared with control groups25 whereas Semper et al. reported that four of the six studies included in the analysis showed no significant difference of weight reduction between comparison groups26 . Thus, the aim of this study was to investigate the effectiveness of a smartphone app on weight reduction in obese or overweight individuals Recei e : 0 pri 016 Accepte : 15 eptem er 016 Pu is e : 0 o em er 016 OPEN •스마트폰 앱이 체중 감량에 도움을 줄 수 있는가? •2012년부터 2014년 까지 최소 6개월 이상 애플리케이션을 사용 •80여 국가(미국, 독일, 한국, 영국, 일본 등)에서 모집된 35,921명의 데이터 •애플리케이션 평균 사용기간은 267일 Chin et al. Sci Rep 2016
- 252. www.nature.com/scientificreports/ Figure 1. Distributionof weight loss among app users. Percentages (and 95% CIs) of participants achieving <5%, 5–10%, 10–15%, 15–20% and >20% weight loss relative to baseline at the end of the 6-month trial period. Data are reported as the mean±SD. Univariate Linear Regression p-value Multivariate Linear Regression p-valueβ (95% CI) β (95% CI) Gender (male) 0.60 (0.54, 0.66) <0.001 0.71 (0.65, 0.77) <0.001 Age 0.01 (0.008, 0.013) <0.001 −0.026 (−0.03, −0.02) <0.001 Follow-up Days −0.001 (−0.001, −0.001) <0.001 0.00 (0.00, 0.00) 0.886 Baseline BMI 0.146 (0.143, 0.150) <0.001 0.165 (0.161, 0.168) <0.001 Successful weight reduction and maintenance by using a smartphone application in those with overweight and obesity Chin et al. Sci Rep 2016 •대상자의 약 77.9%에서 성공적인 체중감량 효과를 확인 •이 중 23%는 본인 체중의 10% 이상 감량에 성공 •앱의 사용이 약물 치료 등 다른 비만 관리 기법에 비해 체중 감량 효과가 뒤쳐지지 않음
- 253. Successful weight reduction and maintenance by using a smartphone application in those with overweight and obesity Chin et al.Sci Rep 2016 •체중을 자주 기록하고 저녁식사를 자주 입력한 사용자의 체중감량 효과가 가장 높았음 •비만 관리에서 강조되던 생활 습관의 중요성을 글로벌 스케일의 데이터로 증명 nature.com/scientificreports/ Diabetes Prevention Program (DPP)-intensive lifestyle intervention is one such method, designed to produce clinically significant weight reduction in adults with prediabetes, proving its effectiveness for both weight loss and cardiometabolic outcomes8 . In addition, life style modification has been shown to be effective for reducing body weight and cardiovascular risk6–10 ; however, each of these studies had important limitations, particularly in that some of them were resource intensive, expensive, and time-consuming14,15 . Frequent group and individual Univariate Logistic Regression Wald Test p-value Multivariate Logistic Regression Wald Test p-valueOR (95% CI) OR (95% CI) Gender (male) 1.44 (1.29, 1.60) <0.001 2.05 (1.79, 2.36) <0.001 Age 0.99 (0.99, 1.00) 0.002 0.97 (0.95, 0.97) <0.001 Follow-up Days 1.00 (1.000, 1.00) 0.627 — — Baseline BMI 1.10 (1.09, 1.11) <0.001 1.13 (1.12, 1.14) <0.001 Weight input frequency (n/person-day) 2.85 (2.20, 3.70) <0.001 3.0 (2.21, 4.08) <0.001 Breakfast input frequency (n/person-day) 3.15 (2.72, 3.66) <0.001 0.36 (0.22, 0.57) <0.001 Lunch input frequency (n/person-day) 3.98 (3.42, 4.64) <0.001 1.14 (0.57, 2.28) 0.718 Dinner input frequency (n/person-day) 4.86 (4.16, 5.68) <0.001 10.69 (6.20, 18.53) <0.001 Breakfast calories (kcal/person-day) 1.00 (1.00, 1.00) <0.001 1.00 (1.00, 1.00) <0.001 Lunch calories (kcal/person-day) 1.00 (1.00, 1.00) <0.001 1.00 (1.00, 1.00) <0.001 Dinner calories (kcal/person-day) 1.00 (1.00, 1.00) 0.105 1.00 (1.00, 1.00) <0.001 Exercise input frequency (n/person-day) 4.02 (3.30, 4.90) <0.001 2.49 (1.96, 3.17) <0.001 Exercise calories expenditure (kcal/person-day) 1.00 (1.00, 1.00) <0.001 1.00 (1.00, 1.00) 0.085 Table 4. Factors contributing to being a success or a partial success against stationary subgroup. Abbreviations: BMI, body mass index; OR, odds ratio; CI, confidence interval.
- 254. •미국 CDC의 당뇨병예방 프로그램(DPP)으로 공식 인증 •CDC에서 fully recognised 된 첫번째 ‘virtual provider’ •2018년 1월부터 CMS(Centers for Medicare&Medicaid Services)의 보험 수가를 적용 •메디케어 1인당 2년에 성취도에 따라 $630 까지 지급 •B2B 사업으로도 확대 예정 "눔은 OEM(주문자상표부착생산) 업체로서 라이선스를 사간 기업에 모바일 플랫폼과 건강관리 코치들, 교육프로그램 등을 종합적으로 제공한다"
- 257. •Omada Health는 당뇨병예방 프로그램(DPP)에 대한 최대 규모 임상 시작 •The Preventing Diabetes With Digital Health and Coaching (PREDICTS) •2019년 9월까지 성인 484명을 대상 •Randomized, controlled trial •실험군: Omada + 코칭 •대조군: 병원의 표준치료 •Outcome •Primary: HbA1c •Secondary: 체중감량, CVD의 위험도 감소 •추가적으로: QoL, healthcare utilization, 의료진의 인식
- 259.
- 260. 우울증 치료 임상결과 1 임상 기간 : 2014년 10월 ~ 2016년 12월 N=96, 1회 30분 자극 Severe Moderate Mild 10 20 30 40 Beck Depression Inventory II 6주 42회 연속 복용SSRI Ybrain 5회 1회 1회5회 0 10 20 30 40 BASELINE 2 WEEK 4 WEEK 6 WEEK MADRS 6주 42회 연속 복용 Ybrain SSRI 5회 1회 1회5회 Severe Moderate Mild None Primary Outcome: 몽고메리-아스퍼그 우울평가척도(MADRS) Secondary Outcome: Beck 우울 척도(Beck Depression Inventory II) Courtesy of 이기원 대표님, YBrain •국내 96명 환자를 대상으로 2년간 double-blinded randomised 임상 연구 실시 •실험군: 가짜 약+ 진짜 자극기기 •대조군: 진짜 약 + 가짜 자극기기 •Primary Outcome인 MADRS 스케일에서 기기가 약에 조금 못 미치는 결과
- 261. 우울증 치료 임상결과 1 임상 기간 : 2014년 10월 ~ 2016년 12월 N=96, 1회 30분 자극 Severe Moderate Mild 10 20 30 40 Beck Depression Inventory II 6주 42회 연속 복용SSRI Ybrain 5회 1회 1회5회 0 10 20 30 40 BASELINE 2 WEEK 4 WEEK 6 WEEK MADRS 6주 42회 연속 복용 Ybrain SSRI 5회 1회 1회5회 Severe Moderate Mild None Primary Outcome: 몽고메리-아스퍼그 우울평가척도(MADRS) Secondary Outcome: Beck 우울 척도(Beck Depression Inventory II) Courtesy of 이기원 대표님, YBrain •Primary Outcome인 MADRS에서 기존 약물에 비해서 약간 효능이 적게 나옴 •Secondary Outcome인 BDI 에 대해서는 기존 약물과 동등하게 나옴 •이러한 결과에 따라서 식약처에서 ‘3등급 보조의료기기’ 로 인허가 •따라서, 원칙적으로는 기존에 우울증 약을 복용하는 환자를 대상으로 사용하게 될 것임
- 262. •경두개 직류자극치료술(tDCS) •2017년 3월국내 최초로 식약처의 3등급 보조의료기기 허가 •7월에는 유럽 CE허가를 받을 예정 •2~3년 내 FDA 허가를 받는 것을 목표 •추가 임상 연구 예정 •우울증 •독거 노인 우울증 치료 시범 사업 진행 중 •10월부터 하버드 의대와 아시아 지역 500명 대상의 임상 예정 •경도인지장애 임상 예정 •조현병 1차 임상 마무리 + 논문 출판 예정 •신의료기술평가 진행 예정
- 263.
- 264. • reSET® wasevaluated in a clinical trial of 507 patients with SUD across 10 treatment centers nation-wide over 12 weeks.* • In patients who were dependent on stimulants, marijuana, cocaine, or alcohol (n=395), 58.1% of patients receiving reSET®* were abstinent in study weeks 9-12, while 29.8% of patients receiving face-to-face therapy alone were abstinent during the same time frame (p<0.01). • Participants who tested positive for drug use at the start of the study (n=191), 26.7% of patients receiving reSET®* were abstinent in study weeks 9-12, while 3.2% of patients receiving traditional face-to-face therapy were abstinent during the same time frame (p<0.01). Pear Therapeutics Campbell et al. Am J Psychiatry. 2014.
- 265. Campbell et al.Am J Psychiatry. 2014. Pear Therapeutics • Patients receiving reSET® showed statistically significant improvement in retention compared to face-to-face therapy alone (p=0.0316).At the end of 12 weeks of treatment 59% of patients receiving face-to-face therapy were retained in the study compared to 67% of patients receiving reSET®.
- 266. Pear Therapeutics •최초로 스마트폰앱이 digital therapeutics 로 질병 치료 목적으로 FDA de novo clearance (기기 없이 '앱'만으로 구성된 시스템이 '질병 치료' 목적으로 허가 받은 것은 최초) •Pear Therapeutics의 reSET 이라는 시스템으로 각종 중독을 치료하는 목적의 앱 •12주에 걸쳐서 대마, 코카인, 알콜 중독에 대한 중독과 의존성을 치료
- 267. AnalysisTarget Discovery AnalysisLeadDiscovery Clinical Trial Post Market Surveillance Digital Healthcare in Drug Development •개인 유전 정보 •블록체인 기반 유전체 분석 •딥러닝 •인공지능+제약사 •환자 모집 •데이터 측정: 센서&웨어러블 •디지털 표현형 •복약 순응도 •SNS 기반의 PMS •블록체인 기반의 PMS + Digital Therapeutics
- 269. Feedback/Questions • E-mail: yoonsup.choi@gmail.com •Blog: http://www.yoonsupchoi.com • Facebook: 최윤섭 디지털 헬스케어 연구소