![Copyright 2016 American Medical Association. All rights reserved.
Development and Validation of a Deep Learning Algorithm
for Detection of Diabetic Retinopathy
in Retinal Fundus Photographs
Varun Gulshan, PhD; Lily Peng, MD, PhD; Marc Coram, PhD; Martin C. Stumpe, PhD; Derek Wu, BS; Arunachalam Narayanaswamy, PhD;
Subhashini Venugopalan, MS; Kasumi Widner, MS; Tom Madams, MEng; Jorge Cuadros, OD, PhD; Ramasamy Kim, OD, DNB;
Rajiv Raman, MS, DNB; Philip C. Nelson, BS; Jessica L. Mega, MD, MPH; Dale R. Webster, PhD
IMPORTANCE Deep learning is a family of computational methods that allow an algorithm to
program itself by learning from a large set of examples that demonstrate the desired
behavior, removing the need to specify rules explicitly. Application of these methods to
medical imaging requires further assessment and validation.
OBJECTIVE To apply deep learning to create an algorithm for automated detection of diabetic
retinopathy and diabetic macular edema in retinal fundus photographs.
DESIGN AND SETTING A specific type of neural network optimized for image classification
called a deep convolutional neural network was trained using a retrospective development
data set of 128 175 retinal images, which were graded 3 to 7 times for diabetic retinopathy,
diabetic macular edema, and image gradability by a panel of 54 US licensed ophthalmologists
and ophthalmology senior residents between May and December 2015. The resultant
algorithm was validated in January and February 2016 using 2 separate data sets, both
graded by at least 7 US board-certified ophthalmologists with high intragrader consistency.
EXPOSURE Deep learning–trained algorithm.
MAIN OUTCOMES AND MEASURES The sensitivity and specificity of the algorithm for detecting
referable diabetic retinopathy (RDR), defined as moderate and worse diabetic retinopathy,
referable diabetic macular edema, or both, were generated based on the reference standard
of the majority decision of the ophthalmologist panel. The algorithm was evaluated at 2
operating points selected from the development set, one selected for high specificity and
another for high sensitivity.
RESULTS TheEyePACS-1datasetconsistedof9963imagesfrom4997patients(meanage,54.4
years;62.2%women;prevalenceofRDR,683/8878fullygradableimages[7.8%]);the
Messidor-2datasethad1748imagesfrom874patients(meanage,57.6years;42.6%women;
prevalenceofRDR,254/1745fullygradableimages[14.6%]).FordetectingRDR,thealgorithm
hadanareaunderthereceiveroperatingcurveof0.991(95%CI,0.988-0.993)forEyePACS-1and
0.990(95%CI,0.986-0.995)forMessidor-2.Usingthefirstoperatingcutpointwithhigh
specificity,forEyePACS-1,thesensitivitywas90.3%(95%CI,87.5%-92.7%)andthespecificity
was98.1%(95%CI,97.8%-98.5%).ForMessidor-2,thesensitivitywas87.0%(95%CI,81.1%-
91.0%)andthespecificitywas98.5%(95%CI,97.7%-99.1%).Usingasecondoperatingpoint
withhighsensitivityinthedevelopmentset,forEyePACS-1thesensitivitywas97.5%and
specificitywas93.4%andforMessidor-2thesensitivitywas96.1%andspecificitywas93.9%.
CONCLUSIONS AND RELEVANCE In this evaluation of retinal fundus photographs from adults
with diabetes, an algorithm based on deep machine learning had high sensitivity and
specificity for detecting referable diabetic retinopathy. Further research is necessary to
determine the feasibility of applying this algorithm in the clinical setting and to determine
whether use of the algorithm could lead to improved care and outcomes compared with
current ophthalmologic assessment.
JAMA. doi:10.1001/jama.2016.17216
Published online November 29, 2016.
Editorial
Supplemental content
Author Affiliations: Google Inc,
Mountain View, California (Gulshan,
Peng, Coram, Stumpe, Wu,
Narayanaswamy, Venugopalan,
Widner, Madams, Nelson, Webster);
Department of Computer Science,
University of Texas, Austin
(Venugopalan); EyePACS LLC,
San Jose, California (Cuadros); School
of Optometry, Vision Science
Graduate Group, University of
California, Berkeley (Cuadros);
Aravind Medical Research
Foundation, Aravind Eye Care
System, Madurai, India (Kim); Shri
Bhagwan Mahavir Vitreoretinal
Services, Sankara Nethralaya,
Chennai, Tamil Nadu, India (Raman);
Verily Life Sciences, Mountain View,
California (Mega); Cardiovascular
Division, Department of Medicine,
Brigham and Women’s Hospital and
Harvard Medical School, Boston,
Massachusetts (Mega).
Corresponding Author: Lily Peng,
MD, PhD, Google Research, 1600
Amphitheatre Way, Mountain View,
CA 94043 (lhpeng@google.com).
Research
JAMA | Original Investigation | INNOVATIONS IN HEALTH CARE DELIVERY
(Reprinted) E1
Copyright 2016 American Medical Association. All rights reserved.
Downloaded From: http://jamanetwork.com/ on 12/02/2016
안과
LETTERS
https://doi.org/10.1038/s41591-018-0335-9
1
Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou, China. 2
Institute for Genomic Medicine, Institute of
Engineering in Medicine, and Shiley Eye Institute, University of California, San Diego, La Jolla, CA, USA. 3
Hangzhou YITU Healthcare Technology Co. Ltd,
Hangzhou, China. 4
Department of Thoracic Surgery/Oncology, First Affiliated Hospital of Guangzhou Medical University, China State Key Laboratory and
National Clinical Research Center for Respiratory Disease, Guangzhou, China. 5
Guangzhou Kangrui Co. Ltd, Guangzhou, China. 6
Guangzhou Regenerative
Medicine and Health Guangdong Laboratory, Guangzhou, China. 7
Veterans Administration Healthcare System, San Diego, CA, USA. 8
These authors contributed
equally: Huiying Liang, Brian Tsui, Hao Ni, Carolina C. S. Valentim, Sally L. Baxter, Guangjian Liu. *e-mail: kang.zhang@gmail.com; xiahumin@hotmail.com
Artificial intelligence (AI)-based methods have emerged as
powerful tools to transform medical care. Although machine
learning classifiers (MLCs) have already demonstrated strong
performance in image-based diagnoses, analysis of diverse
and massive electronic health record (EHR) data remains chal-
lenging. Here, we show that MLCs can query EHRs in a manner
similar to the hypothetico-deductive reasoning used by physi-
cians and unearth associations that previous statistical meth-
ods have not found. Our model applies an automated natural
language processing system using deep learning techniques
to extract clinically relevant information from EHRs. In total,
101.6 million data points from 1,362,559 pediatric patient
visits presenting to a major referral center were analyzed to
train and validate the framework. Our model demonstrates
high diagnostic accuracy across multiple organ systems and is
comparable to experienced pediatricians in diagnosing com-
mon childhood diseases. Our study provides a proof of con-
cept for implementing an AI-based system as a means to aid
physicians in tackling large amounts of data, augmenting diag-
nostic evaluations, and to provide clinical decision support in
cases of diagnostic uncertainty or complexity. Although this
impact may be most evident in areas where healthcare provid-
ers are in relative shortage, the benefits of such an AI system
are likely to be universal.
Medical information has become increasingly complex over
time. The range of disease entities, diagnostic testing and biomark-
ers, and treatment modalities has increased exponentially in recent
years. Subsequently, clinical decision-making has also become more
complex and demands the synthesis of decisions from assessment
of large volumes of data representing clinical information. In the
current digital age, the electronic health record (EHR) represents a
massive repository of electronic data points representing a diverse
array of clinical information1–3
. Artificial intelligence (AI) methods
have emerged as potentially powerful tools to mine EHR data to aid
in disease diagnosis and management, mimicking and perhaps even
augmenting the clinical decision-making of human physicians1
.
To formulate a diagnosis for any given patient, physicians fre-
quently use hypotheticodeductive reasoning. Starting with the chief
complaint, the physician then asks appropriately targeted questions
relating to that complaint. From this initial small feature set, the
physician forms a differential diagnosis and decides what features
(historical questions, physical exam findings, laboratory testing,
and/or imaging studies) to obtain next in order to rule in or rule
out the diagnoses in the differential diagnosis set. The most use-
ful features are identified, such that when the probability of one of
the diagnoses reaches a predetermined level of acceptability, the
process is stopped, and the diagnosis is accepted. It may be pos-
sible to achieve an acceptable level of certainty of the diagnosis with
only a few features without having to process the entire feature set.
Therefore, the physician can be considered a classifier of sorts.
In this study, we designed an AI-based system using machine
learning to extract clinically relevant features from EHR notes to
mimic the clinical reasoning of human physicians. In medicine,
machine learning methods have already demonstrated strong per-
formance in image-based diagnoses, notably in radiology2
, derma-
tology4
, and ophthalmology5–8
, but analysis of EHR data presents
a number of difficult challenges. These challenges include the vast
quantity of data, high dimensionality, data sparsity, and deviations
Evaluation and accurate diagnoses of pediatric
diseases using artificial intelligence
Huiying Liang1,8
, Brian Y. Tsui" "2,8
, Hao Ni3,8
, Carolina C. S. Valentim4,8
, Sally L. Baxter" "2,8
,
Guangjian Liu1,8
, Wenjia Cai" "2
, Daniel S. Kermany1,2
, Xin Sun1
, Jiancong Chen2
, Liya He1
, Jie Zhu1
,
Pin Tian2
, Hua Shao2
, Lianghong Zheng5,6
, Rui Hou5,6
, Sierra Hewett1,2
, Gen Li1,2
, Ping Liang3
,
Xuan Zang3
, Zhiqi Zhang3
, Liyan Pan1
, Huimin Cai5,6
, Rujuan Ling1
, Shuhua Li1
, Yongwang Cui1
,
Shusheng Tang1
, Hong Ye1
, Xiaoyan Huang1
, Waner He1
, Wenqing Liang1
, Qing Zhang1
, Jianmin Jiang1
,
Wei Yu1
, Jianqun Gao1
, Wanxing Ou1
, Yingmin Deng1
, Qiaozhen Hou1
, Bei Wang1
, Cuichan Yao1
,
Yan Liang1
, Shu Zhang1
, Yaou Duan2
, Runze Zhang2
, Sarah Gibson2
, Charlotte L. Zhang2
, Oulan Li2
,
Edward D. Zhang2
, Gabriel Karin2
, Nathan Nguyen2
, Xiaokang Wu1,2
, Cindy Wen2
, Jie Xu2
, Wenqin Xu2
,
Bochu Wang2
, Winston Wang2
, Jing Li1,2
, Bianca Pizzato2
, Caroline Bao2
, Daoman Xiang1
, Wanting He1,2
,
Suiqin He2
, Yugui Zhou1,2
, Weldon Haw2,7
, Michael Goldbaum2
, Adriana Tremoulet2
, Chun-Nan Hsu" "2
,
Hannah Carter2
, Long Zhu3
, Kang Zhang" "1,2,7
* and Huimin Xia" "1
*
NATURE MEDICINE | www.nature.com/naturemedicine
소아청소년과
ARTICLES
https://doi.org/10.1038/s41591-018-0177-5
1
Applied Bioinformatics Laboratories, New York University School of Medicine, New York, NY, USA. 2
Skirball Institute, Department of Cell Biology,
New York University School of Medicine, New York, NY, USA. 3
Department of Pathology, New York University School of Medicine, New York, NY, USA.
4
School of Mechanical Engineering, National Technical University of Athens, Zografou, Greece. 5
Institute for Systems Genetics, New York University School
of Medicine, New York, NY, USA. 6
Department of Biochemistry and Molecular Pharmacology, New York University School of Medicine, New York, NY,
USA. 7
Center for Biospecimen Research and Development, New York University, New York, NY, USA. 8
Department of Population Health and the Center for
Healthcare Innovation and Delivery Science, New York University School of Medicine, New York, NY, USA. 9
These authors contributed equally to this work:
Nicolas Coudray, Paolo Santiago Ocampo. *e-mail: narges.razavian@nyumc.org; aristotelis.tsirigos@nyumc.org
A
ccording to the American Cancer Society and the Cancer
Statistics Center (see URLs), over 150,000 patients with lung
cancer succumb to the disease each year (154,050 expected
for 2018), while another 200,000 new cases are diagnosed on a
yearly basis (234,030 expected for 2018). It is one of the most widely
spread cancers in the world because of not only smoking, but also
exposure to toxic chemicals like radon, asbestos and arsenic. LUAD
and LUSC are the two most prevalent types of non–small cell lung
cancer1
, and each is associated with discrete treatment guidelines. In
the absence of definitive histologic features, this important distinc-
tion can be challenging and time-consuming, and requires confir-
matory immunohistochemical stains.
Classification of lung cancer type is a key diagnostic process
because the available treatment options, including conventional
chemotherapy and, more recently, targeted therapies, differ for
LUAD and LUSC2
. Also, a LUAD diagnosis will prompt the search
for molecular biomarkers and sensitizing mutations and thus has
a great impact on treatment options3,4
. For example, epidermal
growth factor receptor (EGFR) mutations, present in about 20% of
LUAD, and anaplastic lymphoma receptor tyrosine kinase (ALK)
rearrangements, present in<5% of LUAD5
, currently have tar-
geted therapies approved by the Food and Drug Administration
(FDA)6,7
. Mutations in other genes, such as KRAS and tumor pro-
tein P53 (TP53) are very common (about 25% and 50%, respec-
tively) but have proven to be particularly challenging drug targets
so far5,8
. Lung biopsies are typically used to diagnose lung cancer
type and stage. Virtual microscopy of stained images of tissues is
typically acquired at magnifications of 20×to 40×, generating very
large two-dimensional images (10,000 to>100,000 pixels in each
dimension) that are oftentimes challenging to visually inspect in
an exhaustive manner. Furthermore, accurate interpretation can be
difficult, and the distinction between LUAD and LUSC is not always
clear, particularly in poorly differentiated tumors; in this case, ancil-
lary studies are recommended for accurate classification9,10
. To assist
experts, automatic analysis of lung cancer whole-slide images has
been recently studied to predict survival outcomes11
and classifica-
tion12
. For the latter, Yu et al.12
combined conventional thresholding
and image processing techniques with machine-learning methods,
such as random forest classifiers, support vector machines (SVM) or
Naive Bayes classifiers, achieving an AUC of ~0.85 in distinguishing
normal from tumor slides, and ~0.75 in distinguishing LUAD from
LUSC slides. More recently, deep learning was used for the classi-
fication of breast, bladder and lung tumors, achieving an AUC of
0.83 in classification of lung tumor types on tumor slides from The
Cancer Genome Atlas (TCGA)13
. Analysis of plasma DNA values
was also shown to be a good predictor of the presence of non–small
cell cancer, with an AUC of ~0.94 (ref. 14
) in distinguishing LUAD
from LUSC, whereas the use of immunochemical markers yields an
AUC of ~0.94115
.
Here, we demonstrate how the field can further benefit from deep
learning by presenting a strategy based on convolutional neural
networks (CNNs) that not only outperforms methods in previously
Classification and mutation prediction from
non–small cell lung cancer histopathology
images using deep learning
Nicolas Coudray! !1,2,9
, Paolo Santiago Ocampo3,9
, Theodore Sakellaropoulos4
, Navneet Narula3
,
Matija Snuderl3
, David Fenyö5,6
, Andre L. Moreira3,7
, Narges Razavian! !8
* and Aristotelis Tsirigos! !1,3
*
Visual inspection of histopathology slides is one of the main methods used by pathologists to assess the stage, type and sub-
type of lung tumors. Adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) are the most prevalent subtypes of lung
cancer, and their distinction requires visual inspection by an experienced pathologist. In this study, we trained a deep con-
volutional neural network (inception v3) on whole-slide images obtained from The Cancer Genome Atlas to accurately and
automatically classify them into LUAD, LUSC or normal lung tissue. The performance of our method is comparable to that of
pathologists, with an average area under the curve (AUC) of 0.97. Our model was validated on independent datasets of frozen
tissues, formalin-fixed paraffin-embedded tissues and biopsies. Furthermore, we trained the network to predict the ten most
commonly mutated genes in LUAD. We found that six of them—STK11, EGFR, FAT1, SETBP1, KRAS and TP53—can be pre-
dicted from pathology images, with AUCs from 0.733 to 0.856 as measured on a held-out population. These findings suggest
that deep-learning models can assist pathologists in the detection of cancer subtype or gene mutations. Our approach can be
applied to any cancer type, and the code is available at https://github.com/ncoudray/DeepPATH.
NATURE MEDICINE | www.nature.com/naturemedicine
병리과
병리과
병리과
병리과
병리과
병리과
병리과
ARTICLES
https://doi.org/10.1038/s41551-018-0301-3
1
Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, Chengdu, China. 2
Shanghai Wision AI Co., Ltd, Shanghai, China. 3
Beth Israel
Deaconess Medical Center and Harvard Medical School, Center for Advanced Endoscopy, Boston , MA, USA. *e-mail: gary.samsph@gmail.com
C
olonoscopy is the gold-standard screening test for colorectal
cancer1–3
, one of the leading causes of cancer death in both the
United States4,5
and China6
. Colonoscopy can reduce the risk
of death from colorectal cancer through the detection of tumours
at an earlier, more treatable stage as well as through the removal of
precancerous adenomas3,7
. Conversely, failure to detect adenomas
may lead to the development of interval cancer. Evidence has shown
that each 1.0% increase in adenoma detection rate (ADR) leads to a
3.0% decrease in the risk of interval colorectal cancer8
.
Although more than 14million colonoscopies are performed
in the United States annually2
, the adenoma miss rate (AMR) is
estimated to be 6–27%9
. Certain polyps may be missed more fre-
quently, including smaller polyps10,11
, flat polyps12
and polyps in the
left colon13
. There are two independent reasons why a polyp may
be missed during colonoscopy: (i) it was never in the visual field or
(ii) it was in the visual field but not recognized. Several hardware
innovations have sought to address the first problem by improv-
ing visualization of the colonic lumen, for instance by providing a
larger, panoramic camera view, or by flattening colonic folds using a
distal-cap attachment. The problem of unrecognized polyps within
the visual field has been more difficult to address14
. Several studies
have shown that observation of the video monitor by either nurses
or gastroenterology trainees may increase polyp detection by up
to 30%15–17
. Ideally, a real-time automatic polyp-detection system
could serve as a similarly effective second observer that could draw
the endoscopist’s eye, in real time, to concerning lesions, effec-
tively creating an ‘extra set of eyes’ on all aspects of the video data
with fidelity. Although automatic polyp detection in colonoscopy
videos has been an active research topic for the past 20 years, per-
formance levels close to that of the expert endoscopist18–20
have not
been achieved. Early work in automatic polyp detection has focused
on applying deep-learning techniques to polyp detection, but most
published works are small in scale, with small development and/or
training validation sets19,20
.
Here, we report the development and validation of a deep-learn-
ing algorithm, integrated with a multi-threaded processing system,
for the automatic detection of polyps during colonoscopy. We vali-
dated the system in two image studies and two video studies. Each
study contained two independent validation datasets.
Results
We developed a deep-learning algorithm using 5,545colonoscopy
images from colonoscopy reports of 1,290patients that underwent
a colonoscopy examination in the Endoscopy Center of Sichuan
Provincial People’s Hospital between January 2007 and December
2015. Out of the 5,545images used, 3,634images contained polyps
(65.54%) and 1,911 images did not contain polyps (34.46%). For
algorithm training, experienced endoscopists annotated the pres-
ence of each polyp in all of the images in the development data-
set. We validated the algorithm on four independent datasets.
DatasetsA and B were used for image analysis, and datasetsC and D
were used for video analysis.
DatasetA contained 27,113colonoscopy images from colo-
noscopy reports of 1,138consecutive patients who underwent a
colonoscopy examination in the Endoscopy Center of Sichuan
Provincial People’s Hospital between January and December 2016
and who were found to have at least one polyp. Out of the 27,113
images, 5,541images contained polyps (20.44%) and 21,572images
did not contain polyps (79.56%). All polyps were confirmed histo-
logically after biopsy. DatasetB is a public database (CVC-ClinicDB;
Development and validation of a deep-learning
algorithm for the detection of polyps during
colonoscopy
Pu Wang1
, Xiao Xiao2
, Jeremy R. Glissen Brown3
, Tyler M. Berzin" "3
, Mengtian Tu1
, Fei Xiong1
,
Xiao Hu1
, Peixi Liu1
, Yan Song1
, Di Zhang1
, Xue Yang1
, Liangping Li1
, Jiong He2
, Xin Yi2
, Jingjia Liu2
and
Xiaogang Liu" "1
*
The detection and removal of precancerous polyps via colonoscopy is the gold standard for the prevention of colon cancer.
However, the detection rate of adenomatous polyps can vary significantly among endoscopists. Here, we show that a machine-
learningalgorithmcandetectpolypsinclinicalcolonoscopies,inrealtimeandwithhighsensitivityandspecificity.Wedeveloped
the deep-learning algorithm by using data from 1,290 patients, and validated it on newly collected 27,113 colonoscopy images
from 1,138 patients with at least one detected polyp (per-image-sensitivity, 94.38%; per-image-specificity, 95.92%; area under
the receiver operating characteristic curve, 0.984), on a public database of 612 polyp-containing images (per-image-sensitiv-
ity, 88.24%), on 138 colonoscopy videos with histologically confirmed polyps (per-image-sensitivity of 91.64%; per-polyp-sen-
sitivity, 100%), and on 54 unaltered full-range colonoscopy videos without polyps (per-image-specificity, 95.40%). By using a
multi-threaded processing system, the algorithm can process at least 25 frames per second with a latency of 76.80!±!5.60!ms
in real-time video analysis. The software may aid endoscopists while performing colonoscopies, and help assess differences in
polyp and adenoma detection performance among endoscopists.
NATURE BIOMEDICA L ENGINEERING | VOL 2 | OCTOBER 2018 | 741–748 | www.nature.com/natbiomedeng 741
소화기내과
1
Wang P, et al. Gut 2019;0:1–7. doi:10.1136/gutjnl-2018-317500
Endoscopy
ORIGINAL ARTICLE
Real-time automatic detection system increases
colonoscopic polyp and adenoma detection rates: a
prospective randomised controlled study
Pu Wang, 1
Tyler M Berzin, 2
Jeremy Romek Glissen Brown, 2
Shishira Bharadwaj,2
Aymeric Becq,2
Xun Xiao,1
Peixi Liu,1
Liangping Li,1
Yan Song,1
Di Zhang,1
Yi Li,1
Guangre Xu,1
Mengtian Tu,1
Xiaogang Liu 1
To cite: Wang P, Berzin TM,
Glissen Brown JR, et al. Gut
Epub ahead of print: [please
include Day Month Year].
doi:10.1136/
gutjnl-2018-317500
► Additional material is
published online only.To view
please visit the journal online
(http://dx.doi.org/10.1136/
gutjnl-2018-317500).
1
Department of
Gastroenterology, Sichuan
Academy of Medical Sciences
& Sichuan Provincial People’s
Hospital, Chengdu, China
2
Center for Advanced
Endoscopy, Beth Israel
Deaconess Medical Center and
Harvard Medical School, Boston,
Massachusetts, USA
Correspondence to
Xiaogang Liu, Department
of Gastroenterology Sichuan
Academy of Medical Sciences
and Sichuan Provincial People’s
Hospital, Chengdu, China;
Gary.samsph@gmail.com
Received 30 August 2018
Revised 4 February 2019
Accepted 13 February 2019
© Author(s) (or their
employer(s)) 2019. Re-use
permitted under CC BY-NC. No
commercial re-use. See rights
and permissions. Published
by BMJ.
ABSTRACT
Objective The effect of colonoscopy on colorectal
cancer mortality is limited by several factors, among them
a certain miss rate, leading to limited adenoma detection
rates (ADRs).We investigated the effect of an automatic
polyp detection system based on deep learning on polyp
detection rate and ADR.
Design In an open, non-blinded trial, consecutive
patients were prospectively randomised to undergo
diagnostic colonoscopy with or without assistance of a
real-time automatic polyp detection system providing
a simultaneous visual notice and sound alarm on polyp
detection.The primary outcome was ADR.
Results Of 1058 patients included, 536 were
randomised to standard colonoscopy, and 522 were
randomised to colonoscopy with computer-aided
diagnosis.The artificial intelligence (AI) system
significantly increased ADR (29.1%vs20.3%, p<0.001)
and the mean number of adenomas per patient
(0.53vs0.31, p<0.001).This was due to a higher number
of diminutive adenomas found (185vs102; p<0.001),
while there was no statistical difference in larger
adenomas (77vs58, p=0.075). In addition, the number
of hyperplastic polyps was also significantly increased
(114vs52, p<0.001).
Conclusions In a low prevalent ADR population, an
automatic polyp detection system during colonoscopy
resulted in a significant increase in the number of
diminutive adenomas detected, as well as an increase in
the rate of hyperplastic polyps.The cost–benefit ratio of
such effects has to be determined further.
Trial registration number ChiCTR-DDD-17012221;
Results.
INTRODUCTION
Colorectal cancer (CRC) is the second and third-
leading causes of cancer-related deaths in men and
women respectively.1
Colonoscopy is the gold stan-
dard for screening CRC.2 3
Screening colonoscopy
has allowed for a reduction in the incidence and
mortality of CRC via the detection and removal
of adenomatous polyps.4–8
Additionally, there is
evidence that with each 1.0% increase in adenoma
detection rate (ADR), there is an associated 3.0%
decrease in the risk of interval CRC.9 10
However,
polyps can be missed, with reported miss rates of
up to 27% due to both polyp and operator charac-
teristics.11 12
Unrecognised polyps within the visual field is
an important problem to address.11
Several studies
have shown that assistance by a second observer
increases the polyp detection rate (PDR), but such a
strategy remains controversial in terms of increasing
the ADR.13–15
Ideally, a real-time automatic polyp detec-
tion system, with performance close to that of
expert endoscopists, could assist the endosco-
pist in detecting lesions that might correspond to
adenomas in a more consistent and reliable way
Significance of this study
What is already known on this subject?
► Colorectal adenoma detection rate (ADR)
is regarded as a main quality indicator of
(screening) colonoscopy and has been shown
to correlate with interval cancers. Reducing
adenoma miss rates by increasing ADR has
been a goal of many studies focused on
imaging techniques and mechanical methods.
► Artificial intelligence has been recently
introduced for polyp and adenoma detection
as well as differentiation and has shown
promising results in preliminary studies.
What are the new findings?
► This represents the first prospective randomised
controlled trial examining an automatic polyp
detection during colonoscopy and shows an
increase of ADR by 50%, from 20% to 30%.
► This effect was mainly due to a higher rate of
small adenomas found.
► The detection rate of hyperplastic polyps was
also significantly increased.
How might it impact on clinical practice in the
foreseeable future?
► Automatic polyp and adenoma detection could
be the future of diagnostic colonoscopy in order
to achieve stable high adenoma detection rates.
► However, the effect on ultimate outcome is
still unclear, and further improvements such as
polyp differentiation have to be implemented.
on
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Impact of Deep Learning Assistance on the
Histopathologic Review of Lymph Nodes for Metastatic
Breast Cancer
David F. Steiner, MD, PhD,* Robert MacDonald, PhD,* Yun Liu, PhD,* Peter Truszkowski, MD,*
Jason D. Hipp, MD, PhD, FCAP,* Christopher Gammage, MS,* Florence Thng, MS,†
Lily Peng, MD, PhD,* and Martin C. Stumpe, PhD*
Abstract: Advances in the quality of whole-slide images have set the
stage for the clinical use of digital images in anatomic pathology.
Along with advances in computer image analysis, this raises the
possibility for computer-assisted diagnostics in pathology to improve
histopathologic interpretation and clinical care. To evaluate the
potential impact of digital assistance on interpretation of digitized
slides, we conducted a multireader multicase study utilizing our deep
learning algorithm for the detection of breast cancer metastasis in
lymph nodes. Six pathologists reviewed 70 digitized slides from lymph
node sections in 2 reader modes, unassisted and assisted, with a wash-
out period between sessions. In the assisted mode, the deep learning
algorithm was used to identify and outline regions with high like-
lihood of containing tumor. Algorithm-assisted pathologists demon-
strated higher accuracy than either the algorithm or the pathologist
alone. In particular, algorithm assistance significantly increased the
sensitivity of detection for micrometastases (91% vs. 83%, P=0.02).
In addition, average review time per image was significantly shorter
with assistance than without assistance for both micrometastases (61
vs. 116 s, P=0.002) and negative images (111 vs. 137 s, P=0.018).
Lastly, pathologists were asked to provide a numeric score regarding
the difficulty of each image classification. On the basis of this score,
pathologists considered the image review of micrometastases to be
significantly easier when interpreted with assistance (P=0.0005).
Utilizing a proof of concept assistant tool, this study demonstrates the
potential of a deep learning algorithm to improve pathologist accu-
racy and efficiency in a digital pathology workflow.
Key Words: artificial intelligence, machine learning, digital pathology,
breast cancer, computer aided detection
(Am J Surg Pathol 2018;00:000–000)
The regulatory approval and gradual implementation of
whole-slide scanners has enabled the digitization of glass
slides for remote consults and archival purposes.1 Digitiza-
tion alone, however, does not necessarily improve the con-
sistency or efficiency of a pathologist’s primary workflow. In
fact, image review on a digital medium can be slightly
slower than on glass, especially for pathologists with limited
digital pathology experience.2 However, digital pathology
and image analysis tools have already demonstrated po-
tential benefits, including the potential to reduce inter-reader
variability in the evaluation of breast cancer HER2 status.3,4
Digitization also opens the door for assistive tools based on
Artificial Intelligence (AI) to improve efficiency and con-
sistency, decrease fatigue, and increase accuracy.5
Among AI technologies, deep learning has demon-
strated strong performance in many automated image-rec-
ognition applications.6–8 Recently, several deep learning–
based algorithms have been developed for the detection of
breast cancer metastases in lymph nodes as well as for other
applications in pathology.9,10 Initial findings suggest that
some algorithms can even exceed a pathologist’s sensitivity
for detecting individual cancer foci in digital images. How-
ever, this sensitivity gain comes at the cost of increased false
positives, potentially limiting the utility of such algorithms for
automated clinical use.11 In addition, deep learning algo-
rithms are inherently limited to the task for which they have
been specifically trained. While we have begun to understand
the strengths of these algorithms (such as exhaustive search)
and their weaknesses (sensitivity to poor optical focus, tumor
mimics; manuscript under review), the potential clinical util-
ity of such algorithms has not been thoroughly examined.
While an accurate algorithm alone will not necessarily aid
pathologists or improve clinical interpretation, these benefits
may be achieved through thoughtful and appropriate in-
tegration of algorithm predictions into the clinical workflow.8
From the *Google AI Healthcare; and †Verily Life Sciences, Mountain
View, CA.
D.F.S., R.M., and Y.L. are co-first authors (equal contribution).
Work done as part of the Google Brain Healthcare Technology Fellowship
(D.F.S. and P.T.).
Conflicts of Interest and Source of Funding: D.F.S., R.M., Y.L., P.T.,
J.D.H., C.G., F.T., L.P., M.C.S. are employees of Alphabet and have
Alphabet stock.
Correspondence: David F. Steiner, MD, PhD, Google AI Healthcare,
1600 Amphitheatre Way, Mountain View, CA 94043
(e-mail: davesteiner@google.com).
Supplemental Digital Content is available for this article. Direct URL citations
appear in the printed text and are provided in the HTML and PDF
versions of this article on the journal’s website, www.ajsp.com.
Copyright © 2018 The Author(s). Published by Wolters Kluwer Health,
Inc. This is an open-access article distributed under the terms of the
Creative Commons Attribution-Non Commercial-No Derivatives
License 4.0 (CCBY-NC-ND), where it is permissible to download and
share the work provided it is properly cited. The work cannot be
changed in any way or used commercially without permission from
the journal.
ORIGINAL ARTICLE
Am J Surg Pathol ! Volume 00, Number 00, ’’ 2018 www.ajsp.com | 1
병리과
S E P S I S
A targeted real-time early warning score (TREWScore)
for septic shock
Katharine E. Henry,1
David N. Hager,2
Peter J. Pronovost,3,4,5
Suchi Saria1,3,5,6
*
Sepsis is a leading cause of death in the United States, with mortality highest among patients who develop septic
shock. Early aggressive treatment decreases morbidity and mortality. Although automated screening tools can detect
patients currently experiencing severe sepsis and septic shock, none predict those at greatest risk of developing
shock. We analyzed routinely available physiological and laboratory data from intensive care unit patients and devel-
oped “TREWScore,” a targeted real-time early warning score that predicts which patients will develop septic shock.
TREWScore identified patients before the onset of septic shock with an area under the ROC (receiver operating
characteristic) curve (AUC) of 0.83 [95% confidence interval (CI), 0.81 to 0.85]. At a specificity of 0.67, TREWScore
achieved a sensitivity of 0.85 and identified patients a median of 28.2 [interquartile range (IQR), 10.6 to 94.2] hours
before onset. Of those identified, two-thirds were identified before any sepsis-related organ dysfunction. In compar-
ison, the Modified Early Warning Score, which has been used clinically for septic shock prediction, achieved a lower
AUC of 0.73 (95% CI, 0.71 to 0.76). A routine screening protocol based on the presence of two of the systemic inflam-
matory response syndrome criteria, suspicion of infection, and either hypotension or hyperlactatemia achieved a low-
er sensitivity of 0.74 at a comparable specificity of 0.64. Continuous sampling of data from the electronic health
records and calculation of TREWScore may allow clinicians to identify patients at risk for septic shock and provide
earlier interventions that would prevent or mitigate the associated morbidity and mortality.
INTRODUCTION
Seven hundred fifty thousand patients develop severe sepsis and septic
shock in the United States each year. More than half of them are
admitted to an intensive care unit (ICU), accounting for 10% of all
ICU admissions, 20 to 30% of hospital deaths, and $15.4 billion in an-
nual health care costs (1–3). Several studies have demonstrated that
morbidity, mortality, and length of stay are decreased when severe sep-
sis and septic shock are identified and treated early (4–8). In particular,
one study showed that mortality from septic shock increased by 7.6%
with every hour that treatment was delayed after the onset of hypo-
tension (9).
More recent studies comparing protocolized care, usual care, and
early goal-directed therapy (EGDT) for patients with septic shock sug-
gest that usual care is as effective as EGDT (10–12). Some have inter-
preted this to mean that usual care has improved over time and reflects
important aspects of EGDT, such as early antibiotics and early ag-
gressive fluid resuscitation (13). It is likely that continued early identi-
fication and treatment will further improve outcomes. However, the
best approach to managing patients at high risk of developing septic
shock before the onset of severe sepsis or shock has not been studied.
Methods that can identify ahead of time which patients will later expe-
rience septic shock are needed to further understand, study, and im-
prove outcomes in this population.
General-purpose illness severity scoring systems such as the Acute
Physiology and Chronic Health Evaluation (APACHE II), Simplified
Acute Physiology Score (SAPS II), SequentialOrgan Failure Assessment
(SOFA) scores, Modified Early Warning Score (MEWS), and Simple
Clinical Score (SCS) have been validated to assess illness severity and
risk of death among septic patients (14–17). Although these scores
are useful for predicting general deterioration or mortality, they typical-
ly cannot distinguish with high sensitivity and specificity which patients
are at highest risk of developing a specific acute condition.
The increased use of electronic health records (EHRs), which can be
queried in real time, has generated interest in automating tools that
identify patients at risk for septic shock (18–20). A number of “early
warning systems,” “track and trigger” initiatives, “listening applica-
tions,” and “sniffers” have been implemented to improve detection
andtimelinessof therapy forpatients with severe sepsis andseptic shock
(18, 20–23). Although these tools have been successful at detecting pa-
tients currently experiencing severe sepsis or septic shock, none predict
which patients are at highest risk of developing septic shock.
The adoption of the Affordable Care Act has added to the growing
excitement around predictive models derived from electronic health
data in a variety of applications (24), including discharge planning
(25), risk stratification (26, 27), and identification of acute adverse
events (28, 29). For septic shock in particular, promising work includes
that of predicting septic shock using high-fidelity physiological signals
collected directly from bedside monitors (30, 31), inferring relationships
between predictors of septic shock using Bayesian networks (32), and
using routine measurements for septic shock prediction (33–35). No
current prediction models that use only data routinely stored in the
EHR predict septic shock with high sensitivity and specificity many
hours before onset. Moreover, when learning predictive risk scores, cur-
rent methods (34, 36, 37) often have not accounted for the censoring
effects of clinical interventions on patient outcomes (38). For instance,
a patient with severe sepsis who received fluids and never developed
septic shock would be treated as a negative case, despite the possibility
that he or she might have developed septic shock in the absence of such
treatment and therefore could be considered a positive case up until the
1
Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA.
2
Division of Pulmonary and Critical Care Medicine, Department of Medicine, School of
Medicine, Johns Hopkins University, Baltimore, MD 21205, USA. 3
Armstrong Institute for
Patient Safety and Quality, Johns Hopkins University, Baltimore, MD 21202, USA. 4
Department
of Anesthesiology and Critical Care Medicine, School of Medicine, Johns Hopkins University,
Baltimore, MD 21202, USA. 5
Department of Health Policy and Management, Bloomberg
School of Public Health, Johns Hopkins University, Baltimore, MD 21205, USA. 6
Department
of Applied Math and Statistics, Johns Hopkins University, Baltimore, MD 21218, USA.
*Corresponding author. E-mail: ssaria@cs.jhu.edu
R E S E A R C H A R T I C L E
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An Algorithm Based on Deep Learning for Predicting In-Hospital
Cardiac Arrest
Joon-myoung Kwon, MD;* Youngnam Lee, MS;* Yeha Lee, PhD; Seungwoo Lee, BS; Jinsik Park, MD, PhD
Background-—In-hospital cardiac arrest is a major burden to public health, which affects patient safety. Although traditional track-
and-trigger systems are used to predict cardiac arrest early, they have limitations, with low sensitivity and high false-alarm rates.
We propose a deep learning–based early warning system that shows higher performance than the existing track-and-trigger
systems.
Methods and Results-—This retrospective cohort study reviewed patients who were admitted to 2 hospitals from June 2010 to July
2017. A total of 52 131 patients were included. Specifically, a recurrent neural network was trained using data from June 2010 to
January 2017. The result was tested using the data from February to July 2017. The primary outcome was cardiac arrest, and the
secondary outcome was death without attempted resuscitation. As comparative measures, we used the area under the receiver
operating characteristic curve (AUROC), the area under the precision–recall curve (AUPRC), and the net reclassification index.
Furthermore, we evaluated sensitivity while varying the number of alarms. The deep learning–based early warning system (AUROC:
0.850; AUPRC: 0.044) significantly outperformed a modified early warning score (AUROC: 0.603; AUPRC: 0.003), a random forest
algorithm (AUROC: 0.780; AUPRC: 0.014), and logistic regression (AUROC: 0.613; AUPRC: 0.007). Furthermore, the deep learning–
based early warning system reduced the number of alarms by 82.2%, 13.5%, and 42.1% compared with the modified early warning
system, random forest, and logistic regression, respectively, at the same sensitivity.
Conclusions-—An algorithm based on deep learning had high sensitivity and a low false-alarm rate for detection of patients with
cardiac arrest in the multicenter study. (J Am Heart Assoc. 2018;7:e008678. DOI: 10.1161/JAHA.118.008678.)
Key Words: artificial intelligence • cardiac arrest • deep learning • machine learning • rapid response system • resuscitation
In-hospital cardiac arrest is a major burden to public health,
which affects patient safety.1–3
More than a half of cardiac
arrests result from respiratory failure or hypovolemic shock,
and 80% of patients with cardiac arrest show signs of
deterioration in the 8 hours before cardiac arrest.4–9
However,
209 000 in-hospital cardiac arrests occur in the United States
each year, and the survival discharge rate for patients with
cardiac arrest is <20% worldwide.10,11
Rapid response systems
(RRSs) have been introduced in many hospitals to detect
cardiac arrest using the track-and-trigger system (TTS).12,13
Two types of TTS are used in RRSs. For the single-parameter
TTS (SPTTS), cardiac arrest is predicted if any single vital sign
(eg, heart rate [HR], blood pressure) is out of the normal
range.14
The aggregated weighted TTS calculates a weighted
score for each vital sign and then finds patients with cardiac
arrest based on the sum of these scores.15
The modified early
warning score (MEWS) is one of the most widely used
approaches among all aggregated weighted TTSs (Table 1)16
;
however, traditional TTSs including MEWS have limitations, with
low sensitivity or high false-alarm rates.14,15,17
Sensitivity and
false-alarm rate interact: Increased sensitivity creates higher
false-alarm rates and vice versa.
Current RRSs suffer from low sensitivity or a high false-
alarm rate. An RRS was used for only 30% of patients before
unplanned intensive care unit admission and was not used for
22.8% of patients, even if they met the criteria.18,19
From the Departments of Emergency Medicine (J.-m.K.) and Cardiology (J.P.), Mediplex Sejong Hospital, Incheon, Korea; VUNO, Seoul, Korea (Youngnam L., Yeha L.,
S.L.).
*Dr Kwon and Mr Youngnam Lee contributed equally to this study.
Correspondence to: Joon-myoung Kwon, MD, Department of Emergency medicine, Mediplex Sejong Hospital, 20, Gyeyangmunhwa-ro, Gyeyang-gu, Incheon 21080,
Korea. E-mail: kwonjm@sejongh.co.kr
Received January 18, 2018; accepted May 31, 2018.
ª 2018 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley. This is an open access article under the terms of the Creative Commons
Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for
commercial purposes.
DOI: 10.1161/JAHA.118.008678 Journal of the American Heart Association 1
ORIGINAL RESEARCH
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감염내과 심장내과
BRIEF COMMUNICATION OPEN
Digital biomarkers of cognitive function
Paul Dagum1
To identify digital biomarkers associated with cognitive function, we analyzed human–computer interaction from 7 days of
smartphone use in 27 subjects (ages 18–34) who received a gold standard neuropsychological assessment. For several
neuropsychological constructs (working memory, memory, executive function, language, and intelligence), we found a family of
digital biomarkers that predicted test scores with high correlations (p < 10−4
). These preliminary results suggest that passive
measures from smartphone use could be a continuous ecological surrogate for laboratory-based neuropsychological assessment.
npj Digital Medicine (2018)1:10 ; doi:10.1038/s41746-018-0018-4
INTRODUCTION
By comparison to the functional metrics available in other
disciplines, conventional measures of neuropsychiatric disorders
have several challenges. First, they are obtrusive, requiring a
subject to break from their normal routine, dedicating time and
often travel. Second, they are not ecological and require subjects
to perform a task outside of the context of everyday behavior.
Third, they are episodic and provide sparse snapshots of a patient
only at the time of the assessment. Lastly, they are poorly scalable,
taxing limited resources including space and trained staff.
In seeking objective and ecological measures of cognition, we
attempted to develop a method to measure memory and
executive function not in the laboratory but in the moment,
day-to-day. We used human–computer interaction on smart-
phones to identify digital biomarkers that were correlated with
neuropsychological performance.
RESULTS
In 2014, 27 participants (ages 27.1 ± 4.4 years, education
14.1 ± 2.3 years, M:F 8:19) volunteered for neuropsychological
assessment and a test of the smartphone app. Smartphone
human–computer interaction data from the 7 days following
the neuropsychological assessment showed a range of correla-
tions with the cognitive scores. Table 1 shows the correlation
between each neurocognitive test and the cross-validated
predictions of the supervised kernel PCA constructed from
the biomarkers for that test. Figure 1 shows each participant
test score and the digital biomarker prediction for (a) digits
backward, (b) symbol digit modality, (c) animal fluency,
(d) Wechsler Memory Scale-3rd Edition (WMS-III) logical
memory (delayed free recall), (e) brief visuospatial memory test
(delayed free recall), and (f) Wechsler Adult Intelligence Scale-
4th Edition (WAIS-IV) block design. Construct validity of the
predictions was determined using pattern matching that
computed a correlation of 0.87 with p < 10−59
between the
covariance matrix of the predictions and the covariance matrix
of the tests.
Table 1. Fourteen neurocognitive assessments covering five cognitive
domains and dexterity were performed by a neuropsychologist.
Shown are the group mean and standard deviation, range of score,
and the correlation between each test and the cross-validated
prediction constructed from the digital biomarkers for that test
Cognitive predictions
Mean (SD) Range R (predicted),
p-value
Working memory
Digits forward 10.9 (2.7) 7–15 0.71 ± 0.10, 10−4
Digits backward 8.3 (2.7) 4–14 0.75 ± 0.08, 10−5
Executive function
Trail A 23.0 (7.6) 12–39 0.70 ± 0.10, 10−4
Trail B 53.3 (13.1) 37–88 0.82 ± 0.06, 10−6
Symbol digit modality 55.8 (7.7) 43–67 0.70 ± 0.10, 10−4
Language
Animal fluency 22.5 (3.8) 15–30 0.67 ± 0.11, 10−4
FAS phonemic fluency 42 (7.1) 27–52 0.63 ± 0.12, 10−3
Dexterity
Grooved pegboard test
(dominant hand)
62.7 (6.7) 51–75 0.73 ± 0.09, 10−4
Memory
California verbal learning test
(delayed free recall)
14.1 (1.9) 9–16 0.62 ± 0.12, 10−3
WMS-III logical memory
(delayed free recall)
29.4 (6.2) 18–42 0.81 ± 0.07, 10−6
Brief visuospatial memory test
(delayed free recall)
10.2 (1.8) 5–12 0.77 ± 0.08, 10−5
Intelligence scale
WAIS-IV block design 46.1(12.8) 12–61 0.83 ± 0.06, 10−6
WAIS-IV matrix reasoning 22.1(3.3) 12–26 0.80 ± 0.07, 10−6
WAIS-IV vocabulary 40.6(4.0) 31–50 0.67 ± 0.11, 10−4
Received: 5 October 2017 Revised: 3 February 2018 Accepted: 7 February 2018
1
Mindstrong Health, 248 Homer Street, Palo Alto, CA 94301, USA
Correspondence: Paul Dagum (paul@mindstronghealth.com)
www.nature.com/npjdigitalmed
정신의학과
P R E C I S I O N M E D I C I N E
Identification of type 2 diabetes subgroups through
topological analysis of patient similarity
Li Li,1
Wei-Yi Cheng,1
Benjamin S. Glicksberg,1
Omri Gottesman,2
Ronald Tamler,3
Rong Chen,1
Erwin P. Bottinger,2
Joel T. Dudley1,4
*
Type 2 diabetes (T2D) is a heterogeneous complex disease affecting more than 29 million Americans alone with a
rising prevalence trending toward steady increases in the coming decades. Thus, there is a pressing clinical need to
improve early prevention and clinical management of T2D and its complications. Clinicians have understood that
patients who carry the T2D diagnosis have a variety of phenotypes and susceptibilities to diabetes-related compli-
cations. We used a precision medicine approach to characterize the complexity of T2D patient populations based
on high-dimensional electronic medical records (EMRs) and genotype data from 11,210 individuals. We successfully
identified three distinct subgroups of T2D from topology-based patient-patient networks. Subtype 1 was character-
ized by T2D complications diabetic nephropathy and diabetic retinopathy; subtype 2 was enriched for cancer ma-
lignancy and cardiovascular diseases; and subtype 3 was associated most strongly with cardiovascular diseases,
neurological diseases, allergies, and HIV infections. We performed a genetic association analysis of the emergent
T2D subtypes to identify subtype-specific genetic markers and identified 1279, 1227, and 1338 single-nucleotide
polymorphisms (SNPs) that mapped to 425, 322, and 437 unique genes specific to subtypes 1, 2, and 3, respec-
tively. By assessing the human disease–SNP association for each subtype, the enriched phenotypes and
biological functions at the gene level for each subtype matched with the disease comorbidities and clinical dif-
ferences that we identified through EMRs. Our approach demonstrates the utility of applying the precision
medicine paradigm in T2D and the promise of extending the approach to the study of other complex, multi-
factorial diseases.
INTRODUCTION
Type 2 diabetes (T2D) is a complex, multifactorial disease that has
emerged as an increasing prevalent worldwide health concern asso-
ciated with high economic and physiological burdens. An estimated
29.1 million Americans (9.3% of the population) were estimated to
have some form of diabetes in 2012—up 13% from 2010—with T2D
representing up to 95% of all diagnosed cases (1, 2). Risk factors for
T2D include obesity, family history of diabetes, physical inactivity, eth-
nicity, and advanced age (1, 2). Diabetes and its complications now
rank among the leading causes of death in the United States (2). In fact,
diabetes is the leading cause of nontraumatic foot amputation, adult
blindness, and need for kidney dialysis, and multiplies risk for myo-
cardial infarction, peripheral artery disease, and cerebrovascular disease
(3–6). The total estimated direct medical cost attributable to diabetes in
the United States in 2012 was $176 billion, with an estimated $76 billion
attributable to hospital inpatient care alone. There is a great need to im-
prove understanding of T2D and its complex factors to facilitate pre-
vention, early detection, and improvements in clinical management.
A more precise characterization of T2D patient populations can en-
hance our understanding of T2D pathophysiology (7, 8). Current
clinical definitions classify diabetes into three major subtypes: type 1 dia-
betes (T1D), T2D, and maturity-onset diabetes of the young. Other sub-
types based on phenotype bridge the gap between T1D and T2D, for
example, latent autoimmune diabetes in adults (LADA) (7) and ketosis-
prone T2D. The current categories indicate that the traditional definition of
diabetes, especially T2D, might comprise additional subtypes with dis-
tinct clinical characteristics. A recent analysis of the longitudinal Whitehall
II cohort study demonstrated improved assessment of cardiovascular
risks when subgrouping T2D patients according to glucose concentration
criteria (9). Genetic association studies reveal that the genetic architec-
ture of T2D is profoundly complex (10–12). Identified T2D-associated
risk variants exhibit allelic heterogeneity and directional differentiation
among populations (13, 14). The apparent clinical and genetic com-
plexity and heterogeneity of T2D patient populations suggest that there
are opportunities to refine the current, predominantly symptom-based,
definition of T2D into additional subtypes (7).
Because etiological and pathophysiological differences exist among
T2D patients, we hypothesize that a data-driven analysis of a clinical
population could identify new T2D subtypes and factors. Here, we de-
velop a data-driven, topology-based approach to (i) map the complexity
of patient populations using clinical data from electronic medical re-
cords (EMRs) and (ii) identify new, emergent T2D patient subgroups
with subtype-specific clinical and genetic characteristics. We apply this
approachtoadatasetcomprisingmatchedEMRsandgenotypedatafrom
more than 11,000 individuals. Topological analysis of these data revealed
three distinct T2D subtypes that exhibited distinct patterns of clinical
characteristics and disease comorbidities. Further, we identified genetic
markers associated with each T2D subtype and performed gene- and
pathway-level analysis of subtype genetic associations. Biological and
phenotypic features enriched in the genetic analysis corroborated clinical
disparities observed among subgroups. Our findings suggest that data-
driven,topologicalanalysisofpatientco
내분비내과
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![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]
10
Oct
2015
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/knaps-210128022335/85/KNAPS-29-320.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]
10
Oct
2015
• 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/knaps-210128022335/85/KNAPS-30-320.jpg)
![Letters
RESEARCH LETTER
Psychological Distress and Loneliness Reported by
US Adults in 2018 and April 2020
Coronavirus disease 2019 (COVID-19) introduced stressors to
mental health, including loneliness stemming from social
isolation, fear of contracting the disease, economic strain,
and uncertainty about the future. We fielded a national sur-
vey measuring symptoms of psychological distress and lone-
liness among US adults in April 2020 and compared results
with national data from 2018.
Methods|WefieldedtheJohnsHopkinsCOVID-19CivicLifeand
Public Health Survey from April 7 to April 13, 2020, using
NORC’s AmeriSpeak Panel. AmeriSpeak is a probability-
basedpaneldesignedtoberepresentativeoftheUSadultpopu-
lation. The panel is sourced from NORC’s area probability
sample and from a US Postal Service address–based sample
covering 97% of US households.1
The panel has a recruitment
rate of 34% and includes approximately 35 000 members. The
samplefortheJohnsHopkinssurveywasdrawnfromthispanel
and the survey was administered online. NORC obtains in-
formed consent prior to enrolling individuals in the panel.
The Johns Hopkins Bloomberg School of Public Health insti-
tutional review board deemed this study not human partici-
pants research and waived informed consent.
We measured the prevalence of symptoms of serious psy-
chological distress in the overall sample and among demo-
graphic subgroups using the Kessler 6 Psychological Distress
Scale, with the validated measure of serious distress defined
as a score of 13 or higher on the 0- to 24-point scale.2
We also
measured the proportion of respondents who reported that
they always or often feel lonely in response to the item “How
often do you feel lonely?” with response options always, of-
ten, sometimes, rarely, and never.
We compared the prevalence of symptoms of serious
psychological distress in April 2020 with an identical mea-
sure from the 2018 National Health Interview Survey (NHIS),
which used the Kessler 6 scale among 25 417 adults aged 18
years or older in household interviews. The 2018 NHIS
response rate was 64.2%.3
For each measure, we calculated proportions and 95% CIs
usingStataversion15(StataCorp).TheJohnsHopkinsandNHIS
survey data were analyzed separately. Analyses of both data
sets incorporated survey sampling weights to generate nation-
ally representative estimates.
Results | The survey response rate was 70.4%, with a final
sample of 1468 adults aged 18 years or older.
In April 2020, 13.6% (95% CI, 11.1%-16.5%) of US adults re-
ported symptoms of serious psychological distress, relative to
3.9% (95% CI, 3.6%-4.2%) in 2018 (Figure). Among the sub-
groups examined, in April 2020, symptoms of psychological
distress were highest among young adults aged 18 to 29 years
(24.0% [95% CI, 16.3%-33.8%]), adults with household in-
come of less than $35 000 per year (19.3% [95% CI, 14.2%-
25.6%]), and Hispanic adults (18.3% [95% CI, 11.2%-28.3%]).
Figure. Psychological Distress Among US Adults Aged 18 Years or Older
Overall and by Subgroup, April 2020 vs 2018
5 10 15 20 25 30 35
0
Adults aged ≥18 y
Sex
Male
Female
Living situation
Lives alone
Lives with others
Race/ethnicitya
White
Black
Hispanic
Age group, y
18-29
30-54
≥55
Household income, $
<35000
35000-74999
≥75000
April 2020
2018
US adults aged ≥18 y with serious
psychological distress, %
April 2020 measures are from wave 1 of the Johns Hopkins COVID-19 Civic Life and
Public Health Survey, fielded April 7-13, 2020 (N = 1468 adults aged !18 years).
2018 Measures of psychological distress are from the 2018 National Health
Interview Survey (N = 25 417 adults aged !18 years). Psychological distress was
measured using the Kessler 6 Psychological Distress Scale, with scores of 13 or
higher indicating serious psychological distress. The error bars indicate 95% CIs.
a
Race/ethnicity was collected as part of the demographic profile in both the
April 2020 Johns Hopkins survey and the 2018 National Health Interview
Survey. In both surveys, the options were defined by the study investigators,
and participants classified their own race/ethnicity.
jama.com (Reprinted) JAMA Published online June 3, 2020 E1
© 2020 American Medical Association. All rights reserved.
JAMA 2020
In April 2020, 13.6% of US adults reported symptoms of
serious psychological distress, relative to 3.9% in 2018](https://image.slidesharecdn.com/knaps-210128022335/85/KNAPS-90-320.jpg)
Uploaded byYoon Sup Choi
[KNAPS] 포스트 코로나 시대, 제약 산업과 디지털 헬스케어
The document discusses the post-COVID-19 era and the partnership between the pharmaceutical industry and digital healthcare. It introduces Dr. Yoonsup Choi, a leading expert in digital healthcare in Korea who has published numerous papers and books on medical AI. He founded the first research institute on digital healthcare in Korea and co-founded a startup accelerator for healthcare companies. The COVID-19 pandemic has accelerated changes in healthcare and increased investment in digital health.
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- 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
- 5. 최윤섭 지음 의료인공지능 표지 디자인•최승협 컴퓨터공학, 생명과학,의학의 융합을 통해 디지 털 헬스케어 분야의 혁신을 창출하고 사회적 가 치를 만드는 것을 화두로 삼고 있는 융합생명과학자, 미래의료학자, 기업가, 엔젤투자가, 에반젤리스트이다. 국내 디지털 헬스케어 분야 의 대표적인 전문가로, 활발한 연구, 저술 및 강연 등을 통해 국내에 이 분야를 처음 소개한 장본인이다. 포항공과대학교에서 컴퓨터공학과 생명과학을 복수전공하였으며 동 대학원 시스템생명공학부에서 전산생물학으로 이학박사 학위를 취득하였다. 스탠퍼드대학교 방문연구원, 서울의대 암연구소 연구 조교수, KT 종합기술원 컨버전스연구소 팀장, 서울대병원 의생명연 구원 연구조교수 등을 거쳤다. 『사이언스』를 비롯한 세계적인 과학 저널에 10여 편의 논문을 발표했다. 국내 최초로 디지털 헬스케어를 본격적으로 연구하는 연구소인 ‘최 윤섭 디지털 헬스케어 연구소’를 설립하여 소장을 맡고 있다. 또한 국내 유일의 헬스케어 스타트업 전문 엑셀러레이터 ‘디지털 헬스케 어 파트너스’의 공동 창업자 및 대표 파트너로 혁신적인 헬스케어 스타트업을 의료 전문가들과 함께 발굴, 투자, 육성하고 있다. 성균 관대학교 디지털헬스학과 초빙교수로도 재직 중이다. 뷰노, 직토, 3billion, 서지컬마인드, 닥터다이어리, VRAD, 메디히어, 소울링, 메디히어, 모바일닥터 등의 헬스케어 스타트업에 투자하고 자문을 맡아 한국에서도 헬스케어 혁신을 만들어내기 위해 노력하 고 있다. 국내 최초의 디지털 헬스케어 전문 블로그 『최윤섭의 헬스 케어 이노베이션』에 활발하게 집필하고 있으며, 『매일경제』에 칼럼 을 연재하고 있다. 저서로 『헬스케어 이노베이션: 이미 시작된 미래』 와 『그렇게 나는 스스로 기업이 되었다』가 있다. •블로그_ http://www.yoonsupchoi.com/ •페이스북_ https://www.facebook.com/yoonsup.choi •이메일_ yoonsup.choi@gmail.com 최윤섭 의료 인공지능은 보수적인 의료 시스템을 재편할 혁신을 일으키고 있다. 의료 인공지능의 빠른 발전과 광범위한 영향은 전문화, 세분화되며 발전해 온 현대 의료 전문가들이 이해하기가 어려우며, 어디서부 터 공부해야 할지도 막연하다. 이런 상황에서 의료 인공지능의 개념과 적용, 그리고 의사와의 관계를 쉽 게 풀어내는 이 책은 좋은 길라잡이가 될 것이다. 특히 미래의 주역이 될 의학도와 젊은 의료인에게 유용 한 소개서이다. ━ 서준범, 서울아산병원 영상의학과 교수, 의료영상인공지능사업단장 인공지능이 의료의 패러다임을 크게 바꿀 것이라는 것에 동의하지 않는 사람은 거의 없다. 하지만 인공 지능이 처리해야 할 의료의 난제는 많으며 그 해결 방안도 천차만별이다. 흔히 생각하는 만병통치약 같 은 의료 인공지능은 존재하지 않는다. 이 책은 다양한 의료 인공지능의 개발, 활용 및 가능성을 균형 있 게 분석하고 있다. 인공지능을 도입하려는 의료인, 생소한 의료 영역에 도전할 인공지능 연구자 모두에 게 일독을 권한다. ━ 정지훈, 경희사이버대 미디어커뮤니케이션학과 선임강의교수, 의사 서울의대 기초의학교육을 책임지고 있는 교수의 입장에서, 산업화 이후 변하지 않은 현재의 의학 교육 으로는 격변하는 인공지능 시대에 의대생을 대비시키지 못한다는 한계를 절실히 느낀다. 저와 함께 의 대 인공지능 교육을 개척하고 있는 최윤섭 소장의 전문적 분석과 미래 지향적 안목이 담긴 책이다. 인공 지능이라는 미래를 대비할 의대생과 교수, 그리고 의대 진학을 고민하는 학생과 학부모에게 추천한다. ━ 최형진, 서울대학교 의과대학 해부학교실 교수, 내과 전문의 최근 의료 인공지능의 도입에 대해서 극단적인 시각과 태도가 공존하고 있다. 이 책은 다양한 사례와 깊 은 통찰을 통해 의료 인공지능의 현황과 미래에 대해 균형적인 시각을 제공하여, 인공지능이 의료에 본 격적으로 도입되기 위한 토론의 장을 마련한다. 의료 인공지능이 일상화된 10년 후 돌아보았을 때, 이 책 이 그런 시대를 이끄는 길라잡이 역할을 하였음을 확인할 수 있기를 기대한다. ━ 정규환, 뷰노 CTO 의료 인공지능은 다른 분야 인공지능보다 더 본질적인 이해가 필요하다. 단순히 인간의 일을 대신하는 수준을 넘어 의학의 패러다임을 데이터 기반으로 변화시키기 때문이다. 따라서 인공지능을 균형있게 이 해하고, 어떻게 의사와 환자에게 도움을 줄 수 있을지 깊은 고민이 필요하다. 세계적으로 일어나고 있는 이러한 노력의 결과물을 집대성한 이 책이 반가운 이유다. ━ 백승욱, 루닛 대표 의료 인공지능의 최신 동향뿐만 아니라, 의의와 한계, 전망, 그리고 다양한 생각거리까지 주는 책이다. 논쟁이 되는 여러 이슈에 대해서도 저자는 자신의 시각을 명확한 근거에 기반하여 설득력 있게 제시하 고 있다. 개인적으로는 이 책을 대학원 수업 교재로 활용하려 한다. ━ 신수용, 성균관대학교 디지털헬스학과 교수 최윤섭 지음 의료 인공지능 값 20,000원 ISBN 979-11-86269-99-2 미래의료학자 최윤섭 박사가 제시하는 의료 인공지능의 현재와 미래 의료 딥러닝과 IBM 왓슨의 현주소 인공지능은 의사를 대체하는가 값 20,000원 ISBN 979-11-86269-99-2 소울링, 메디히어, 모바일닥터 등의 헬스케어 스타트업에 투자하고 자문을 맡아 한국에서도 헬스케어 혁신을 만들어내기 위해 노력하 고 있다. 국내 최초의 디지털 헬스케어 전문 블로그 『최윤섭의 헬스 케어 이노베이션』에 활발하게 집필하고 있으며, 『매일경제』에 칼럼 을 연재하고 있다. 저서로 『헬스케어 이노베이션: 이미 시작된 미래』 와 『그렇게 나는 스스로 기업이 되었다』가 있다. •블로그_ http://www.yoonsupchoi.com/ •페이스북_ https://www.facebook.com/yoonsup.choi •이메일_ yoonsup.choi@gmail.com (2014) (2018) (2020)
- 7. 의료가 맞이하는 피할수 없는 쓰나미
- 8. 디지털 트랜스포메이션 COVID-19 헬스케어의변화를 촉발시키고 변화를 더 가속화, 장벽을 무너뜨린다 뉴 노멀이 올 것인가?
- 9. 헬스케어 넓은 의미의 건강관리에는 해당되지만, 디지털 기술이 적용되지 않고, 전문 의료 영역도 아닌 것 예) 운동, 영양, 수면 디지털 헬스케어 건강 관리 중에 디지털 기술이 사용되는 것 예) 사물인터넷, 인공지능, 3D 프린터, VR/AR 모바일 헬스케어 디지털 헬스케어 중 모바일 기술이 사용되는 것 예) 스마트폰, 사물인터넷, SNS 의료 인공지능 EMR 분석 의료 영상 분석 시그널 분석 왓슨 의료 질병 예방, 치료, 처방, 관리 등 전문 의료 영역 원격의료 원격 환자 모니터링 원격진료 전화, 화상, 판독 명상 앱 ADHD 치료 게임 PTSD 치료 VR 디지털 치료제 중독 치료 앱 헬스케어 관련 분야 구성도
- 10. https://rockhealth.com/reports/amidst-a-record-3-1b-funding-in-q1-2020-digital-health-braces-for-covid-19-impa •최근 몇년 동안디지털 헬스케어 분야의 투자는 지속적으로 증가 •2020년 1분기에는 사상 최대의 투자 규모를 기록하였으나, •COVID-19 판데믹 이후, 2분기부터는 시장이 매우 불확실해질 것으로 예측
- 11. •코로나19 판데믹으로, 디지털헬스케어는 오히려 전기를 맞이함 •2020년에 디지털 헬스케어 분야 역대 최대 투자가 이뤄짐 ($14B) •투자 횟수, 건당 투자 규모 역시 최고 기록을 갱신 •Mega Deal ($100M 이상) 역시 40건으로 역대 최고 기록을 경신 https://rockhealth.com/reports/2020-midyear-digital-health-market-update-unprecedented-funding-in-an-unprecedented-time/
- 12.
- 13. GV(구글벤처스)는 100여 개의헬스케어 스타트업에 투자
- 14. •최근 몇년 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
- 15. Analysis Target Discovery Analysis LeadDiscovery Clinical Trial Post Market Surveillance Digital Healthcare in Drug Development
- 16. Analysis Target Discovery Analysis LeadDiscovery Clinical Trial Post Market Surveillance Digital Healthcare in Drug Development •개인 유전 정보 분석 •블록체인 기반 유전체 분석 •딥러닝 기반 후보 물질 •인공지능+제약사 •환자 모집 •데이터 측정: 웨어러블 •디지털 표현형 •원격 임상 시험 •SNS 기반의 PMS •블록체인 기반의 PMS + Digital Therapeutics
- 17. Analysis Target Discovery Analysis LeadDiscovery Clinical Trial Post Market Surveillance Digital Healthcare in Drug Development •딥러닝 기반 후보 물질 •인공지능+제약사
- 18. No choice butto bring AI into the medicine
- 19. Martin Duggan,“IBM WatsonHealth - Integrated Care & the Evolution to Cognitive Computing”
- 20. Copyright 2016 AmericanMedical Association. All rights reserved. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs Varun Gulshan, PhD; Lily Peng, MD, PhD; Marc Coram, PhD; Martin C. Stumpe, PhD; Derek Wu, BS; Arunachalam Narayanaswamy, PhD; Subhashini Venugopalan, MS; Kasumi Widner, MS; Tom Madams, MEng; Jorge Cuadros, OD, PhD; Ramasamy Kim, OD, DNB; Rajiv Raman, MS, DNB; Philip C. Nelson, BS; Jessica L. Mega, MD, MPH; Dale R. Webster, PhD IMPORTANCE Deep learning is a family of computational methods that allow an algorithm to program itself by learning from a large set of examples that demonstrate the desired behavior, removing the need to specify rules explicitly. Application of these methods to medical imaging requires further assessment and validation. OBJECTIVE To apply deep learning to create an algorithm for automated detection of diabetic retinopathy and diabetic macular edema in retinal fundus photographs. DESIGN AND SETTING A specific type of neural network optimized for image classification called a deep convolutional neural network was trained using a retrospective development data set of 128 175 retinal images, which were graded 3 to 7 times for diabetic retinopathy, diabetic macular edema, and image gradability by a panel of 54 US licensed ophthalmologists and ophthalmology senior residents between May and December 2015. The resultant algorithm was validated in January and February 2016 using 2 separate data sets, both graded by at least 7 US board-certified ophthalmologists with high intragrader consistency. EXPOSURE Deep learning–trained algorithm. MAIN OUTCOMES AND MEASURES The sensitivity and specificity of the algorithm for detecting referable diabetic retinopathy (RDR), defined as moderate and worse diabetic retinopathy, referable diabetic macular edema, or both, were generated based on the reference standard of the majority decision of the ophthalmologist panel. The algorithm was evaluated at 2 operating points selected from the development set, one selected for high specificity and another for high sensitivity. RESULTS TheEyePACS-1datasetconsistedof9963imagesfrom4997patients(meanage,54.4 years;62.2%women;prevalenceofRDR,683/8878fullygradableimages[7.8%]);the Messidor-2datasethad1748imagesfrom874patients(meanage,57.6years;42.6%women; prevalenceofRDR,254/1745fullygradableimages[14.6%]).FordetectingRDR,thealgorithm hadanareaunderthereceiveroperatingcurveof0.991(95%CI,0.988-0.993)forEyePACS-1and 0.990(95%CI,0.986-0.995)forMessidor-2.Usingthefirstoperatingcutpointwithhigh specificity,forEyePACS-1,thesensitivitywas90.3%(95%CI,87.5%-92.7%)andthespecificity was98.1%(95%CI,97.8%-98.5%).ForMessidor-2,thesensitivitywas87.0%(95%CI,81.1%- 91.0%)andthespecificitywas98.5%(95%CI,97.7%-99.1%).Usingasecondoperatingpoint withhighsensitivityinthedevelopmentset,forEyePACS-1thesensitivitywas97.5%and specificitywas93.4%andforMessidor-2thesensitivitywas96.1%andspecificitywas93.9%. CONCLUSIONS AND RELEVANCE In this evaluation of retinal fundus photographs from adults with diabetes, an algorithm based on deep machine learning had high sensitivity and specificity for detecting referable diabetic retinopathy. Further research is necessary to determine the feasibility of applying this algorithm in the clinical setting and to determine whether use of the algorithm could lead to improved care and outcomes compared with current ophthalmologic assessment. JAMA. doi:10.1001/jama.2016.17216 Published online November 29, 2016. Editorial Supplemental content Author Affiliations: Google Inc, Mountain View, California (Gulshan, Peng, Coram, Stumpe, Wu, Narayanaswamy, Venugopalan, Widner, Madams, Nelson, Webster); Department of Computer Science, University of Texas, Austin (Venugopalan); EyePACS LLC, San Jose, California (Cuadros); School of Optometry, Vision Science Graduate Group, University of California, Berkeley (Cuadros); Aravind Medical Research Foundation, Aravind Eye Care System, Madurai, India (Kim); Shri Bhagwan Mahavir Vitreoretinal Services, Sankara Nethralaya, Chennai, Tamil Nadu, India (Raman); Verily Life Sciences, Mountain View, California (Mega); Cardiovascular Division, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts (Mega). Corresponding Author: Lily Peng, MD, PhD, Google Research, 1600 Amphitheatre Way, Mountain View, CA 94043 (lhpeng@google.com). Research JAMA | Original Investigation | INNOVATIONS IN HEALTH CARE DELIVERY (Reprinted) E1 Copyright 2016 American Medical Association. All rights reserved. Downloaded From: http://jamanetwork.com/ on 12/02/2016 안과 LETTERS https://doi.org/10.1038/s41591-018-0335-9 1 Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou, China. 2 Institute for Genomic Medicine, Institute of Engineering in Medicine, and Shiley Eye Institute, University of California, San Diego, La Jolla, CA, USA. 3 Hangzhou YITU Healthcare Technology Co. Ltd, Hangzhou, China. 4 Department of Thoracic Surgery/Oncology, First Affiliated Hospital of Guangzhou Medical University, China State Key Laboratory and National Clinical Research Center for Respiratory Disease, Guangzhou, China. 5 Guangzhou Kangrui Co. Ltd, Guangzhou, China. 6 Guangzhou Regenerative Medicine and Health Guangdong Laboratory, Guangzhou, China. 7 Veterans Administration Healthcare System, San Diego, CA, USA. 8 These authors contributed equally: Huiying Liang, Brian Tsui, Hao Ni, Carolina C. S. Valentim, Sally L. Baxter, Guangjian Liu. *e-mail: kang.zhang@gmail.com; xiahumin@hotmail.com Artificial intelligence (AI)-based methods have emerged as powerful tools to transform medical care. Although machine learning classifiers (MLCs) have already demonstrated strong performance in image-based diagnoses, analysis of diverse and massive electronic health record (EHR) data remains chal- lenging. Here, we show that MLCs can query EHRs in a manner similar to the hypothetico-deductive reasoning used by physi- cians and unearth associations that previous statistical meth- ods have not found. Our model applies an automated natural language processing system using deep learning techniques to extract clinically relevant information from EHRs. In total, 101.6 million data points from 1,362,559 pediatric patient visits presenting to a major referral center were analyzed to train and validate the framework. Our model demonstrates high diagnostic accuracy across multiple organ systems and is comparable to experienced pediatricians in diagnosing com- mon childhood diseases. Our study provides a proof of con- cept for implementing an AI-based system as a means to aid physicians in tackling large amounts of data, augmenting diag- nostic evaluations, and to provide clinical decision support in cases of diagnostic uncertainty or complexity. Although this impact may be most evident in areas where healthcare provid- ers are in relative shortage, the benefits of such an AI system are likely to be universal. Medical information has become increasingly complex over time. The range of disease entities, diagnostic testing and biomark- ers, and treatment modalities has increased exponentially in recent years. Subsequently, clinical decision-making has also become more complex and demands the synthesis of decisions from assessment of large volumes of data representing clinical information. In the current digital age, the electronic health record (EHR) represents a massive repository of electronic data points representing a diverse array of clinical information1–3 . Artificial intelligence (AI) methods have emerged as potentially powerful tools to mine EHR data to aid in disease diagnosis and management, mimicking and perhaps even augmenting the clinical decision-making of human physicians1 . To formulate a diagnosis for any given patient, physicians fre- quently use hypotheticodeductive reasoning. Starting with the chief complaint, the physician then asks appropriately targeted questions relating to that complaint. From this initial small feature set, the physician forms a differential diagnosis and decides what features (historical questions, physical exam findings, laboratory testing, and/or imaging studies) to obtain next in order to rule in or rule out the diagnoses in the differential diagnosis set. The most use- ful features are identified, such that when the probability of one of the diagnoses reaches a predetermined level of acceptability, the process is stopped, and the diagnosis is accepted. It may be pos- sible to achieve an acceptable level of certainty of the diagnosis with only a few features without having to process the entire feature set. Therefore, the physician can be considered a classifier of sorts. In this study, we designed an AI-based system using machine learning to extract clinically relevant features from EHR notes to mimic the clinical reasoning of human physicians. In medicine, machine learning methods have already demonstrated strong per- formance in image-based diagnoses, notably in radiology2 , derma- tology4 , and ophthalmology5–8 , but analysis of EHR data presents a number of difficult challenges. These challenges include the vast quantity of data, high dimensionality, data sparsity, and deviations Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence Huiying Liang1,8 , Brian Y. Tsui" "2,8 , Hao Ni3,8 , Carolina C. S. Valentim4,8 , Sally L. Baxter" "2,8 , Guangjian Liu1,8 , Wenjia Cai" "2 , Daniel S. Kermany1,2 , Xin Sun1 , Jiancong Chen2 , Liya He1 , Jie Zhu1 , Pin Tian2 , Hua Shao2 , Lianghong Zheng5,6 , Rui Hou5,6 , Sierra Hewett1,2 , Gen Li1,2 , Ping Liang3 , Xuan Zang3 , Zhiqi Zhang3 , Liyan Pan1 , Huimin Cai5,6 , Rujuan Ling1 , Shuhua Li1 , Yongwang Cui1 , Shusheng Tang1 , Hong Ye1 , Xiaoyan Huang1 , Waner He1 , Wenqing Liang1 , Qing Zhang1 , Jianmin Jiang1 , Wei Yu1 , Jianqun Gao1 , Wanxing Ou1 , Yingmin Deng1 , Qiaozhen Hou1 , Bei Wang1 , Cuichan Yao1 , Yan Liang1 , Shu Zhang1 , Yaou Duan2 , Runze Zhang2 , Sarah Gibson2 , Charlotte L. Zhang2 , Oulan Li2 , Edward D. Zhang2 , Gabriel Karin2 , Nathan Nguyen2 , Xiaokang Wu1,2 , Cindy Wen2 , Jie Xu2 , Wenqin Xu2 , Bochu Wang2 , Winston Wang2 , Jing Li1,2 , Bianca Pizzato2 , Caroline Bao2 , Daoman Xiang1 , Wanting He1,2 , Suiqin He2 , Yugui Zhou1,2 , Weldon Haw2,7 , Michael Goldbaum2 , Adriana Tremoulet2 , Chun-Nan Hsu" "2 , Hannah Carter2 , Long Zhu3 , Kang Zhang" "1,2,7 * and Huimin Xia" "1 * NATURE MEDICINE | www.nature.com/naturemedicine 소아청소년과 ARTICLES https://doi.org/10.1038/s41591-018-0177-5 1 Applied Bioinformatics Laboratories, New York University School of Medicine, New York, NY, USA. 2 Skirball Institute, Department of Cell Biology, New York University School of Medicine, New York, NY, USA. 3 Department of Pathology, New York University School of Medicine, New York, NY, USA. 4 School of Mechanical Engineering, National Technical University of Athens, Zografou, Greece. 5 Institute for Systems Genetics, New York University School of Medicine, New York, NY, USA. 6 Department of Biochemistry and Molecular Pharmacology, New York University School of Medicine, New York, NY, USA. 7 Center for Biospecimen Research and Development, New York University, New York, NY, USA. 8 Department of Population Health and the Center for Healthcare Innovation and Delivery Science, New York University School of Medicine, New York, NY, USA. 9 These authors contributed equally to this work: Nicolas Coudray, Paolo Santiago Ocampo. *e-mail: narges.razavian@nyumc.org; aristotelis.tsirigos@nyumc.org A ccording to the American Cancer Society and the Cancer Statistics Center (see URLs), over 150,000 patients with lung cancer succumb to the disease each year (154,050 expected for 2018), while another 200,000 new cases are diagnosed on a yearly basis (234,030 expected for 2018). It is one of the most widely spread cancers in the world because of not only smoking, but also exposure to toxic chemicals like radon, asbestos and arsenic. LUAD and LUSC are the two most prevalent types of non–small cell lung cancer1 , and each is associated with discrete treatment guidelines. In the absence of definitive histologic features, this important distinc- tion can be challenging and time-consuming, and requires confir- matory immunohistochemical stains. Classification of lung cancer type is a key diagnostic process because the available treatment options, including conventional chemotherapy and, more recently, targeted therapies, differ for LUAD and LUSC2 . Also, a LUAD diagnosis will prompt the search for molecular biomarkers and sensitizing mutations and thus has a great impact on treatment options3,4 . For example, epidermal growth factor receptor (EGFR) mutations, present in about 20% of LUAD, and anaplastic lymphoma receptor tyrosine kinase (ALK) rearrangements, present in<5% of LUAD5 , currently have tar- geted therapies approved by the Food and Drug Administration (FDA)6,7 . Mutations in other genes, such as KRAS and tumor pro- tein P53 (TP53) are very common (about 25% and 50%, respec- tively) but have proven to be particularly challenging drug targets so far5,8 . Lung biopsies are typically used to diagnose lung cancer type and stage. Virtual microscopy of stained images of tissues is typically acquired at magnifications of 20×to 40×, generating very large two-dimensional images (10,000 to>100,000 pixels in each dimension) that are oftentimes challenging to visually inspect in an exhaustive manner. Furthermore, accurate interpretation can be difficult, and the distinction between LUAD and LUSC is not always clear, particularly in poorly differentiated tumors; in this case, ancil- lary studies are recommended for accurate classification9,10 . To assist experts, automatic analysis of lung cancer whole-slide images has been recently studied to predict survival outcomes11 and classifica- tion12 . For the latter, Yu et al.12 combined conventional thresholding and image processing techniques with machine-learning methods, such as random forest classifiers, support vector machines (SVM) or Naive Bayes classifiers, achieving an AUC of ~0.85 in distinguishing normal from tumor slides, and ~0.75 in distinguishing LUAD from LUSC slides. More recently, deep learning was used for the classi- fication of breast, bladder and lung tumors, achieving an AUC of 0.83 in classification of lung tumor types on tumor slides from The Cancer Genome Atlas (TCGA)13 . Analysis of plasma DNA values was also shown to be a good predictor of the presence of non–small cell cancer, with an AUC of ~0.94 (ref. 14 ) in distinguishing LUAD from LUSC, whereas the use of immunochemical markers yields an AUC of ~0.94115 . Here, we demonstrate how the field can further benefit from deep learning by presenting a strategy based on convolutional neural networks (CNNs) that not only outperforms methods in previously Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning Nicolas Coudray! !1,2,9 , Paolo Santiago Ocampo3,9 , Theodore Sakellaropoulos4 , Navneet Narula3 , Matija Snuderl3 , David Fenyö5,6 , Andre L. Moreira3,7 , Narges Razavian! !8 * and Aristotelis Tsirigos! !1,3 * Visual inspection of histopathology slides is one of the main methods used by pathologists to assess the stage, type and sub- type of lung tumors. Adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) are the most prevalent subtypes of lung cancer, and their distinction requires visual inspection by an experienced pathologist. In this study, we trained a deep con- volutional neural network (inception v3) on whole-slide images obtained from The Cancer Genome Atlas to accurately and automatically classify them into LUAD, LUSC or normal lung tissue. The performance of our method is comparable to that of pathologists, with an average area under the curve (AUC) of 0.97. Our model was validated on independent datasets of frozen tissues, formalin-fixed paraffin-embedded tissues and biopsies. Furthermore, we trained the network to predict the ten most commonly mutated genes in LUAD. We found that six of them—STK11, EGFR, FAT1, SETBP1, KRAS and TP53—can be pre- dicted from pathology images, with AUCs from 0.733 to 0.856 as measured on a held-out population. These findings suggest that deep-learning models can assist pathologists in the detection of cancer subtype or gene mutations. Our approach can be applied to any cancer type, and the code is available at https://github.com/ncoudray/DeepPATH. NATURE MEDICINE | www.nature.com/naturemedicine 병리과 병리과 병리과 병리과 병리과 병리과 병리과 ARTICLES https://doi.org/10.1038/s41551-018-0301-3 1 Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, Chengdu, China. 2 Shanghai Wision AI Co., Ltd, Shanghai, China. 3 Beth Israel Deaconess Medical Center and Harvard Medical School, Center for Advanced Endoscopy, Boston , MA, USA. *e-mail: gary.samsph@gmail.com C olonoscopy is the gold-standard screening test for colorectal cancer1–3 , one of the leading causes of cancer death in both the United States4,5 and China6 . Colonoscopy can reduce the risk of death from colorectal cancer through the detection of tumours at an earlier, more treatable stage as well as through the removal of precancerous adenomas3,7 . Conversely, failure to detect adenomas may lead to the development of interval cancer. Evidence has shown that each 1.0% increase in adenoma detection rate (ADR) leads to a 3.0% decrease in the risk of interval colorectal cancer8 . Although more than 14million colonoscopies are performed in the United States annually2 , the adenoma miss rate (AMR) is estimated to be 6–27%9 . Certain polyps may be missed more fre- quently, including smaller polyps10,11 , flat polyps12 and polyps in the left colon13 . There are two independent reasons why a polyp may be missed during colonoscopy: (i) it was never in the visual field or (ii) it was in the visual field but not recognized. Several hardware innovations have sought to address the first problem by improv- ing visualization of the colonic lumen, for instance by providing a larger, panoramic camera view, or by flattening colonic folds using a distal-cap attachment. The problem of unrecognized polyps within the visual field has been more difficult to address14 . Several studies have shown that observation of the video monitor by either nurses or gastroenterology trainees may increase polyp detection by up to 30%15–17 . Ideally, a real-time automatic polyp-detection system could serve as a similarly effective second observer that could draw the endoscopist’s eye, in real time, to concerning lesions, effec- tively creating an ‘extra set of eyes’ on all aspects of the video data with fidelity. Although automatic polyp detection in colonoscopy videos has been an active research topic for the past 20 years, per- formance levels close to that of the expert endoscopist18–20 have not been achieved. Early work in automatic polyp detection has focused on applying deep-learning techniques to polyp detection, but most published works are small in scale, with small development and/or training validation sets19,20 . Here, we report the development and validation of a deep-learn- ing algorithm, integrated with a multi-threaded processing system, for the automatic detection of polyps during colonoscopy. We vali- dated the system in two image studies and two video studies. Each study contained two independent validation datasets. Results We developed a deep-learning algorithm using 5,545colonoscopy images from colonoscopy reports of 1,290patients that underwent a colonoscopy examination in the Endoscopy Center of Sichuan Provincial People’s Hospital between January 2007 and December 2015. Out of the 5,545images used, 3,634images contained polyps (65.54%) and 1,911 images did not contain polyps (34.46%). For algorithm training, experienced endoscopists annotated the pres- ence of each polyp in all of the images in the development data- set. We validated the algorithm on four independent datasets. DatasetsA and B were used for image analysis, and datasetsC and D were used for video analysis. DatasetA contained 27,113colonoscopy images from colo- noscopy reports of 1,138consecutive patients who underwent a colonoscopy examination in the Endoscopy Center of Sichuan Provincial People’s Hospital between January and December 2016 and who were found to have at least one polyp. Out of the 27,113 images, 5,541images contained polyps (20.44%) and 21,572images did not contain polyps (79.56%). All polyps were confirmed histo- logically after biopsy. DatasetB is a public database (CVC-ClinicDB; Development and validation of a deep-learning algorithm for the detection of polyps during colonoscopy Pu Wang1 , Xiao Xiao2 , Jeremy R. Glissen Brown3 , Tyler M. Berzin" "3 , Mengtian Tu1 , Fei Xiong1 , Xiao Hu1 , Peixi Liu1 , Yan Song1 , Di Zhang1 , Xue Yang1 , Liangping Li1 , Jiong He2 , Xin Yi2 , Jingjia Liu2 and Xiaogang Liu" "1 * The detection and removal of precancerous polyps via colonoscopy is the gold standard for the prevention of colon cancer. However, the detection rate of adenomatous polyps can vary significantly among endoscopists. Here, we show that a machine- learningalgorithmcandetectpolypsinclinicalcolonoscopies,inrealtimeandwithhighsensitivityandspecificity.Wedeveloped the deep-learning algorithm by using data from 1,290 patients, and validated it on newly collected 27,113 colonoscopy images from 1,138 patients with at least one detected polyp (per-image-sensitivity, 94.38%; per-image-specificity, 95.92%; area under the receiver operating characteristic curve, 0.984), on a public database of 612 polyp-containing images (per-image-sensitiv- ity, 88.24%), on 138 colonoscopy videos with histologically confirmed polyps (per-image-sensitivity of 91.64%; per-polyp-sen- sitivity, 100%), and on 54 unaltered full-range colonoscopy videos without polyps (per-image-specificity, 95.40%). By using a multi-threaded processing system, the algorithm can process at least 25 frames per second with a latency of 76.80!±!5.60!ms in real-time video analysis. The software may aid endoscopists while performing colonoscopies, and help assess differences in polyp and adenoma detection performance among endoscopists. NATURE BIOMEDICA L ENGINEERING | VOL 2 | OCTOBER 2018 | 741–748 | www.nature.com/natbiomedeng 741 소화기내과 1 Wang P, et al. Gut 2019;0:1–7. doi:10.1136/gutjnl-2018-317500 Endoscopy ORIGINAL ARTICLE Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: a prospective randomised controlled study Pu Wang, 1 Tyler M Berzin, 2 Jeremy Romek Glissen Brown, 2 Shishira Bharadwaj,2 Aymeric Becq,2 Xun Xiao,1 Peixi Liu,1 Liangping Li,1 Yan Song,1 Di Zhang,1 Yi Li,1 Guangre Xu,1 Mengtian Tu,1 Xiaogang Liu 1 To cite: Wang P, Berzin TM, Glissen Brown JR, et al. Gut Epub ahead of print: [please include Day Month Year]. doi:10.1136/ gutjnl-2018-317500 ► Additional material is published online only.To view please visit the journal online (http://dx.doi.org/10.1136/ gutjnl-2018-317500). 1 Department of Gastroenterology, Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, Chengdu, China 2 Center for Advanced Endoscopy, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USA Correspondence to Xiaogang Liu, Department of Gastroenterology Sichuan Academy of Medical Sciences and Sichuan Provincial People’s Hospital, Chengdu, China; Gary.samsph@gmail.com Received 30 August 2018 Revised 4 February 2019 Accepted 13 February 2019 © Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. ABSTRACT Objective The effect of colonoscopy on colorectal cancer mortality is limited by several factors, among them a certain miss rate, leading to limited adenoma detection rates (ADRs).We investigated the effect of an automatic polyp detection system based on deep learning on polyp detection rate and ADR. Design In an open, non-blinded trial, consecutive patients were prospectively randomised to undergo diagnostic colonoscopy with or without assistance of a real-time automatic polyp detection system providing a simultaneous visual notice and sound alarm on polyp detection.The primary outcome was ADR. Results Of 1058 patients included, 536 were randomised to standard colonoscopy, and 522 were randomised to colonoscopy with computer-aided diagnosis.The artificial intelligence (AI) system significantly increased ADR (29.1%vs20.3%, p<0.001) and the mean number of adenomas per patient (0.53vs0.31, p<0.001).This was due to a higher number of diminutive adenomas found (185vs102; p<0.001), while there was no statistical difference in larger adenomas (77vs58, p=0.075). In addition, the number of hyperplastic polyps was also significantly increased (114vs52, p<0.001). Conclusions In a low prevalent ADR population, an automatic polyp detection system during colonoscopy resulted in a significant increase in the number of diminutive adenomas detected, as well as an increase in the rate of hyperplastic polyps.The cost–benefit ratio of such effects has to be determined further. Trial registration number ChiCTR-DDD-17012221; Results. INTRODUCTION Colorectal cancer (CRC) is the second and third- leading causes of cancer-related deaths in men and women respectively.1 Colonoscopy is the gold stan- dard for screening CRC.2 3 Screening colonoscopy has allowed for a reduction in the incidence and mortality of CRC via the detection and removal of adenomatous polyps.4–8 Additionally, there is evidence that with each 1.0% increase in adenoma detection rate (ADR), there is an associated 3.0% decrease in the risk of interval CRC.9 10 However, polyps can be missed, with reported miss rates of up to 27% due to both polyp and operator charac- teristics.11 12 Unrecognised polyps within the visual field is an important problem to address.11 Several studies have shown that assistance by a second observer increases the polyp detection rate (PDR), but such a strategy remains controversial in terms of increasing the ADR.13–15 Ideally, a real-time automatic polyp detec- tion system, with performance close to that of expert endoscopists, could assist the endosco- pist in detecting lesions that might correspond to adenomas in a more consistent and reliable way Significance of this study What is already known on this subject? ► Colorectal adenoma detection rate (ADR) is regarded as a main quality indicator of (screening) colonoscopy and has been shown to correlate with interval cancers. Reducing adenoma miss rates by increasing ADR has been a goal of many studies focused on imaging techniques and mechanical methods. ► Artificial intelligence has been recently introduced for polyp and adenoma detection as well as differentiation and has shown promising results in preliminary studies. What are the new findings? ► This represents the first prospective randomised controlled trial examining an automatic polyp detection during colonoscopy and shows an increase of ADR by 50%, from 20% to 30%. ► This effect was mainly due to a higher rate of small adenomas found. ► The detection rate of hyperplastic polyps was also significantly increased. How might it impact on clinical practice in the foreseeable future? ► Automatic polyp and adenoma detection could be the future of diagnostic colonoscopy in order to achieve stable high adenoma detection rates. ► However, the effect on ultimate outcome is still unclear, and further improvements such as polyp differentiation have to be implemented. on 17 March 2019 by guest. Protected by copyright. http://gut.bmj.com/ Gut: first published as 10.1136/gutjnl-2018-317500 on 27 February 2019. Downloaded from 소화기내과 Downloaded from https://journals.lww.com/ajsp by BhDMf5ePHKav1zEoum1tQfN4a+kJLhEZgbsIHo4XMi0hCywCX1AWnYQp/IlQrHD3MyLIZIvnCFZVJ56DGsD590P5lh5KqE20T/dBX3x9CoM= on 10/14/2018 Downloaded from https://journals.lww.com/ajsp by BhDMf5ePHKav1zEoum1tQfN4a+kJLhEZgbsIHo4XMi0hCywCX1AWnYQp/IlQrHD3MyLIZIvnCFZVJ56DGsD590P5lh5KqE20T/dBX3x9CoM= on 10/14/2018 Impact of Deep Learning Assistance on the Histopathologic Review of Lymph Nodes for Metastatic Breast Cancer David F. Steiner, MD, PhD,* Robert MacDonald, PhD,* Yun Liu, PhD,* Peter Truszkowski, MD,* Jason D. Hipp, MD, PhD, FCAP,* Christopher Gammage, MS,* Florence Thng, MS,† Lily Peng, MD, PhD,* and Martin C. Stumpe, PhD* Abstract: Advances in the quality of whole-slide images have set the stage for the clinical use of digital images in anatomic pathology. Along with advances in computer image analysis, this raises the possibility for computer-assisted diagnostics in pathology to improve histopathologic interpretation and clinical care. To evaluate the potential impact of digital assistance on interpretation of digitized slides, we conducted a multireader multicase study utilizing our deep learning algorithm for the detection of breast cancer metastasis in lymph nodes. Six pathologists reviewed 70 digitized slides from lymph node sections in 2 reader modes, unassisted and assisted, with a wash- out period between sessions. In the assisted mode, the deep learning algorithm was used to identify and outline regions with high like- lihood of containing tumor. Algorithm-assisted pathologists demon- strated higher accuracy than either the algorithm or the pathologist alone. In particular, algorithm assistance significantly increased the sensitivity of detection for micrometastases (91% vs. 83%, P=0.02). In addition, average review time per image was significantly shorter with assistance than without assistance for both micrometastases (61 vs. 116 s, P=0.002) and negative images (111 vs. 137 s, P=0.018). Lastly, pathologists were asked to provide a numeric score regarding the difficulty of each image classification. On the basis of this score, pathologists considered the image review of micrometastases to be significantly easier when interpreted with assistance (P=0.0005). Utilizing a proof of concept assistant tool, this study demonstrates the potential of a deep learning algorithm to improve pathologist accu- racy and efficiency in a digital pathology workflow. Key Words: artificial intelligence, machine learning, digital pathology, breast cancer, computer aided detection (Am J Surg Pathol 2018;00:000–000) The regulatory approval and gradual implementation of whole-slide scanners has enabled the digitization of glass slides for remote consults and archival purposes.1 Digitiza- tion alone, however, does not necessarily improve the con- sistency or efficiency of a pathologist’s primary workflow. In fact, image review on a digital medium can be slightly slower than on glass, especially for pathologists with limited digital pathology experience.2 However, digital pathology and image analysis tools have already demonstrated po- tential benefits, including the potential to reduce inter-reader variability in the evaluation of breast cancer HER2 status.3,4 Digitization also opens the door for assistive tools based on Artificial Intelligence (AI) to improve efficiency and con- sistency, decrease fatigue, and increase accuracy.5 Among AI technologies, deep learning has demon- strated strong performance in many automated image-rec- ognition applications.6–8 Recently, several deep learning– based algorithms have been developed for the detection of breast cancer metastases in lymph nodes as well as for other applications in pathology.9,10 Initial findings suggest that some algorithms can even exceed a pathologist’s sensitivity for detecting individual cancer foci in digital images. How- ever, this sensitivity gain comes at the cost of increased false positives, potentially limiting the utility of such algorithms for automated clinical use.11 In addition, deep learning algo- rithms are inherently limited to the task for which they have been specifically trained. While we have begun to understand the strengths of these algorithms (such as exhaustive search) and their weaknesses (sensitivity to poor optical focus, tumor mimics; manuscript under review), the potential clinical util- ity of such algorithms has not been thoroughly examined. While an accurate algorithm alone will not necessarily aid pathologists or improve clinical interpretation, these benefits may be achieved through thoughtful and appropriate in- tegration of algorithm predictions into the clinical workflow.8 From the *Google AI Healthcare; and †Verily Life Sciences, Mountain View, CA. D.F.S., R.M., and Y.L. are co-first authors (equal contribution). Work done as part of the Google Brain Healthcare Technology Fellowship (D.F.S. and P.T.). Conflicts of Interest and Source of Funding: D.F.S., R.M., Y.L., P.T., J.D.H., C.G., F.T., L.P., M.C.S. are employees of Alphabet and have Alphabet stock. Correspondence: David F. Steiner, MD, PhD, Google AI Healthcare, 1600 Amphitheatre Way, Mountain View, CA 94043 (e-mail: davesteiner@google.com). Supplemental Digital Content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal’s website, www.ajsp.com. Copyright © 2018 The Author(s). Published by Wolters Kluwer Health, Inc. This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. ORIGINAL ARTICLE Am J Surg Pathol ! Volume 00, Number 00, ’’ 2018 www.ajsp.com | 1 병리과 S E P S I S A targeted real-time early warning score (TREWScore) for septic shock Katharine E. Henry,1 David N. Hager,2 Peter J. Pronovost,3,4,5 Suchi Saria1,3,5,6 * Sepsis is a leading cause of death in the United States, with mortality highest among patients who develop septic shock. Early aggressive treatment decreases morbidity and mortality. Although automated screening tools can detect patients currently experiencing severe sepsis and septic shock, none predict those at greatest risk of developing shock. We analyzed routinely available physiological and laboratory data from intensive care unit patients and devel- oped “TREWScore,” a targeted real-time early warning score that predicts which patients will develop septic shock. TREWScore identified patients before the onset of septic shock with an area under the ROC (receiver operating characteristic) curve (AUC) of 0.83 [95% confidence interval (CI), 0.81 to 0.85]. At a specificity of 0.67, TREWScore achieved a sensitivity of 0.85 and identified patients a median of 28.2 [interquartile range (IQR), 10.6 to 94.2] hours before onset. Of those identified, two-thirds were identified before any sepsis-related organ dysfunction. In compar- ison, the Modified Early Warning Score, which has been used clinically for septic shock prediction, achieved a lower AUC of 0.73 (95% CI, 0.71 to 0.76). A routine screening protocol based on the presence of two of the systemic inflam- matory response syndrome criteria, suspicion of infection, and either hypotension or hyperlactatemia achieved a low- er sensitivity of 0.74 at a comparable specificity of 0.64. Continuous sampling of data from the electronic health records and calculation of TREWScore may allow clinicians to identify patients at risk for septic shock and provide earlier interventions that would prevent or mitigate the associated morbidity and mortality. INTRODUCTION Seven hundred fifty thousand patients develop severe sepsis and septic shock in the United States each year. More than half of them are admitted to an intensive care unit (ICU), accounting for 10% of all ICU admissions, 20 to 30% of hospital deaths, and $15.4 billion in an- nual health care costs (1–3). Several studies have demonstrated that morbidity, mortality, and length of stay are decreased when severe sep- sis and septic shock are identified and treated early (4–8). In particular, one study showed that mortality from septic shock increased by 7.6% with every hour that treatment was delayed after the onset of hypo- tension (9). More recent studies comparing protocolized care, usual care, and early goal-directed therapy (EGDT) for patients with septic shock sug- gest that usual care is as effective as EGDT (10–12). Some have inter- preted this to mean that usual care has improved over time and reflects important aspects of EGDT, such as early antibiotics and early ag- gressive fluid resuscitation (13). It is likely that continued early identi- fication and treatment will further improve outcomes. However, the best approach to managing patients at high risk of developing septic shock before the onset of severe sepsis or shock has not been studied. Methods that can identify ahead of time which patients will later expe- rience septic shock are needed to further understand, study, and im- prove outcomes in this population. General-purpose illness severity scoring systems such as the Acute Physiology and Chronic Health Evaluation (APACHE II), Simplified Acute Physiology Score (SAPS II), SequentialOrgan Failure Assessment (SOFA) scores, Modified Early Warning Score (MEWS), and Simple Clinical Score (SCS) have been validated to assess illness severity and risk of death among septic patients (14–17). Although these scores are useful for predicting general deterioration or mortality, they typical- ly cannot distinguish with high sensitivity and specificity which patients are at highest risk of developing a specific acute condition. The increased use of electronic health records (EHRs), which can be queried in real time, has generated interest in automating tools that identify patients at risk for septic shock (18–20). A number of “early warning systems,” “track and trigger” initiatives, “listening applica- tions,” and “sniffers” have been implemented to improve detection andtimelinessof therapy forpatients with severe sepsis andseptic shock (18, 20–23). Although these tools have been successful at detecting pa- tients currently experiencing severe sepsis or septic shock, none predict which patients are at highest risk of developing septic shock. The adoption of the Affordable Care Act has added to the growing excitement around predictive models derived from electronic health data in a variety of applications (24), including discharge planning (25), risk stratification (26, 27), and identification of acute adverse events (28, 29). For septic shock in particular, promising work includes that of predicting septic shock using high-fidelity physiological signals collected directly from bedside monitors (30, 31), inferring relationships between predictors of septic shock using Bayesian networks (32), and using routine measurements for septic shock prediction (33–35). No current prediction models that use only data routinely stored in the EHR predict septic shock with high sensitivity and specificity many hours before onset. Moreover, when learning predictive risk scores, cur- rent methods (34, 36, 37) often have not accounted for the censoring effects of clinical interventions on patient outcomes (38). For instance, a patient with severe sepsis who received fluids and never developed septic shock would be treated as a negative case, despite the possibility that he or she might have developed septic shock in the absence of such treatment and therefore could be considered a positive case up until the 1 Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA. 2 Division of Pulmonary and Critical Care Medicine, Department of Medicine, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA. 3 Armstrong Institute for Patient Safety and Quality, Johns Hopkins University, Baltimore, MD 21202, USA. 4 Department of Anesthesiology and Critical Care Medicine, School of Medicine, Johns Hopkins University, Baltimore, MD 21202, USA. 5 Department of Health Policy and Management, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21205, USA. 6 Department of Applied Math and Statistics, Johns Hopkins University, Baltimore, MD 21218, USA. *Corresponding author. E-mail: ssaria@cs.jhu.edu R E S E A R C H A R T I C L E www.ScienceTranslationalMedicine.org 5 August 2015 Vol 7 Issue 299 299ra122 1 on November 3, 2016 http://stm.sciencemag.org/ Downloaded from An Algorithm Based on Deep Learning for Predicting In-Hospital Cardiac Arrest Joon-myoung Kwon, MD;* Youngnam Lee, MS;* Yeha Lee, PhD; Seungwoo Lee, BS; Jinsik Park, MD, PhD Background-—In-hospital cardiac arrest is a major burden to public health, which affects patient safety. Although traditional track- and-trigger systems are used to predict cardiac arrest early, they have limitations, with low sensitivity and high false-alarm rates. We propose a deep learning–based early warning system that shows higher performance than the existing track-and-trigger systems. Methods and Results-—This retrospective cohort study reviewed patients who were admitted to 2 hospitals from June 2010 to July 2017. A total of 52 131 patients were included. Specifically, a recurrent neural network was trained using data from June 2010 to January 2017. The result was tested using the data from February to July 2017. The primary outcome was cardiac arrest, and the secondary outcome was death without attempted resuscitation. As comparative measures, we used the area under the receiver operating characteristic curve (AUROC), the area under the precision–recall curve (AUPRC), and the net reclassification index. Furthermore, we evaluated sensitivity while varying the number of alarms. The deep learning–based early warning system (AUROC: 0.850; AUPRC: 0.044) significantly outperformed a modified early warning score (AUROC: 0.603; AUPRC: 0.003), a random forest algorithm (AUROC: 0.780; AUPRC: 0.014), and logistic regression (AUROC: 0.613; AUPRC: 0.007). Furthermore, the deep learning– based early warning system reduced the number of alarms by 82.2%, 13.5%, and 42.1% compared with the modified early warning system, random forest, and logistic regression, respectively, at the same sensitivity. Conclusions-—An algorithm based on deep learning had high sensitivity and a low false-alarm rate for detection of patients with cardiac arrest in the multicenter study. (J Am Heart Assoc. 2018;7:e008678. DOI: 10.1161/JAHA.118.008678.) Key Words: artificial intelligence • cardiac arrest • deep learning • machine learning • rapid response system • resuscitation In-hospital cardiac arrest is a major burden to public health, which affects patient safety.1–3 More than a half of cardiac arrests result from respiratory failure or hypovolemic shock, and 80% of patients with cardiac arrest show signs of deterioration in the 8 hours before cardiac arrest.4–9 However, 209 000 in-hospital cardiac arrests occur in the United States each year, and the survival discharge rate for patients with cardiac arrest is <20% worldwide.10,11 Rapid response systems (RRSs) have been introduced in many hospitals to detect cardiac arrest using the track-and-trigger system (TTS).12,13 Two types of TTS are used in RRSs. For the single-parameter TTS (SPTTS), cardiac arrest is predicted if any single vital sign (eg, heart rate [HR], blood pressure) is out of the normal range.14 The aggregated weighted TTS calculates a weighted score for each vital sign and then finds patients with cardiac arrest based on the sum of these scores.15 The modified early warning score (MEWS) is one of the most widely used approaches among all aggregated weighted TTSs (Table 1)16 ; however, traditional TTSs including MEWS have limitations, with low sensitivity or high false-alarm rates.14,15,17 Sensitivity and false-alarm rate interact: Increased sensitivity creates higher false-alarm rates and vice versa. Current RRSs suffer from low sensitivity or a high false- alarm rate. An RRS was used for only 30% of patients before unplanned intensive care unit admission and was not used for 22.8% of patients, even if they met the criteria.18,19 From the Departments of Emergency Medicine (J.-m.K.) and Cardiology (J.P.), Mediplex Sejong Hospital, Incheon, Korea; VUNO, Seoul, Korea (Youngnam L., Yeha L., S.L.). *Dr Kwon and Mr Youngnam Lee contributed equally to this study. Correspondence to: Joon-myoung Kwon, MD, Department of Emergency medicine, Mediplex Sejong Hospital, 20, Gyeyangmunhwa-ro, Gyeyang-gu, Incheon 21080, Korea. E-mail: kwonjm@sejongh.co.kr Received January 18, 2018; accepted May 31, 2018. ª 2018 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. DOI: 10.1161/JAHA.118.008678 Journal of the American Heart Association 1 ORIGINAL RESEARCH by guest on June 28, 2018 http://jaha.ahajournals.org/ Downloaded from 감염내과 심장내과 BRIEF COMMUNICATION OPEN Digital biomarkers of cognitive function Paul Dagum1 To identify digital biomarkers associated with cognitive function, we analyzed human–computer interaction from 7 days of smartphone use in 27 subjects (ages 18–34) who received a gold standard neuropsychological assessment. For several neuropsychological constructs (working memory, memory, executive function, language, and intelligence), we found a family of digital biomarkers that predicted test scores with high correlations (p < 10−4 ). These preliminary results suggest that passive measures from smartphone use could be a continuous ecological surrogate for laboratory-based neuropsychological assessment. npj Digital Medicine (2018)1:10 ; doi:10.1038/s41746-018-0018-4 INTRODUCTION By comparison to the functional metrics available in other disciplines, conventional measures of neuropsychiatric disorders have several challenges. First, they are obtrusive, requiring a subject to break from their normal routine, dedicating time and often travel. Second, they are not ecological and require subjects to perform a task outside of the context of everyday behavior. Third, they are episodic and provide sparse snapshots of a patient only at the time of the assessment. Lastly, they are poorly scalable, taxing limited resources including space and trained staff. In seeking objective and ecological measures of cognition, we attempted to develop a method to measure memory and executive function not in the laboratory but in the moment, day-to-day. We used human–computer interaction on smart- phones to identify digital biomarkers that were correlated with neuropsychological performance. RESULTS In 2014, 27 participants (ages 27.1 ± 4.4 years, education 14.1 ± 2.3 years, M:F 8:19) volunteered for neuropsychological assessment and a test of the smartphone app. Smartphone human–computer interaction data from the 7 days following the neuropsychological assessment showed a range of correla- tions with the cognitive scores. Table 1 shows the correlation between each neurocognitive test and the cross-validated predictions of the supervised kernel PCA constructed from the biomarkers for that test. Figure 1 shows each participant test score and the digital biomarker prediction for (a) digits backward, (b) symbol digit modality, (c) animal fluency, (d) Wechsler Memory Scale-3rd Edition (WMS-III) logical memory (delayed free recall), (e) brief visuospatial memory test (delayed free recall), and (f) Wechsler Adult Intelligence Scale- 4th Edition (WAIS-IV) block design. Construct validity of the predictions was determined using pattern matching that computed a correlation of 0.87 with p < 10−59 between the covariance matrix of the predictions and the covariance matrix of the tests. Table 1. Fourteen neurocognitive assessments covering five cognitive domains and dexterity were performed by a neuropsychologist. Shown are the group mean and standard deviation, range of score, and the correlation between each test and the cross-validated prediction constructed from the digital biomarkers for that test Cognitive predictions Mean (SD) Range R (predicted), p-value Working memory Digits forward 10.9 (2.7) 7–15 0.71 ± 0.10, 10−4 Digits backward 8.3 (2.7) 4–14 0.75 ± 0.08, 10−5 Executive function Trail A 23.0 (7.6) 12–39 0.70 ± 0.10, 10−4 Trail B 53.3 (13.1) 37–88 0.82 ± 0.06, 10−6 Symbol digit modality 55.8 (7.7) 43–67 0.70 ± 0.10, 10−4 Language Animal fluency 22.5 (3.8) 15–30 0.67 ± 0.11, 10−4 FAS phonemic fluency 42 (7.1) 27–52 0.63 ± 0.12, 10−3 Dexterity Grooved pegboard test (dominant hand) 62.7 (6.7) 51–75 0.73 ± 0.09, 10−4 Memory California verbal learning test (delayed free recall) 14.1 (1.9) 9–16 0.62 ± 0.12, 10−3 WMS-III logical memory (delayed free recall) 29.4 (6.2) 18–42 0.81 ± 0.07, 10−6 Brief visuospatial memory test (delayed free recall) 10.2 (1.8) 5–12 0.77 ± 0.08, 10−5 Intelligence scale WAIS-IV block design 46.1(12.8) 12–61 0.83 ± 0.06, 10−6 WAIS-IV matrix reasoning 22.1(3.3) 12–26 0.80 ± 0.07, 10−6 WAIS-IV vocabulary 40.6(4.0) 31–50 0.67 ± 0.11, 10−4 Received: 5 October 2017 Revised: 3 February 2018 Accepted: 7 February 2018 1 Mindstrong Health, 248 Homer Street, Palo Alto, CA 94301, USA Correspondence: Paul Dagum (paul@mindstronghealth.com) www.nature.com/npjdigitalmed 정신의학과 P R E C I S I O N M E D I C I N E Identification of type 2 diabetes subgroups through topological analysis of patient similarity Li Li,1 Wei-Yi Cheng,1 Benjamin S. Glicksberg,1 Omri Gottesman,2 Ronald Tamler,3 Rong Chen,1 Erwin P. Bottinger,2 Joel T. Dudley1,4 * Type 2 diabetes (T2D) is a heterogeneous complex disease affecting more than 29 million Americans alone with a rising prevalence trending toward steady increases in the coming decades. Thus, there is a pressing clinical need to improve early prevention and clinical management of T2D and its complications. Clinicians have understood that patients who carry the T2D diagnosis have a variety of phenotypes and susceptibilities to diabetes-related compli- cations. We used a precision medicine approach to characterize the complexity of T2D patient populations based on high-dimensional electronic medical records (EMRs) and genotype data from 11,210 individuals. We successfully identified three distinct subgroups of T2D from topology-based patient-patient networks. Subtype 1 was character- ized by T2D complications diabetic nephropathy and diabetic retinopathy; subtype 2 was enriched for cancer ma- lignancy and cardiovascular diseases; and subtype 3 was associated most strongly with cardiovascular diseases, neurological diseases, allergies, and HIV infections. We performed a genetic association analysis of the emergent T2D subtypes to identify subtype-specific genetic markers and identified 1279, 1227, and 1338 single-nucleotide polymorphisms (SNPs) that mapped to 425, 322, and 437 unique genes specific to subtypes 1, 2, and 3, respec- tively. By assessing the human disease–SNP association for each subtype, the enriched phenotypes and biological functions at the gene level for each subtype matched with the disease comorbidities and clinical dif- ferences that we identified through EMRs. Our approach demonstrates the utility of applying the precision medicine paradigm in T2D and the promise of extending the approach to the study of other complex, multi- factorial diseases. INTRODUCTION Type 2 diabetes (T2D) is a complex, multifactorial disease that has emerged as an increasing prevalent worldwide health concern asso- ciated with high economic and physiological burdens. An estimated 29.1 million Americans (9.3% of the population) were estimated to have some form of diabetes in 2012—up 13% from 2010—with T2D representing up to 95% of all diagnosed cases (1, 2). Risk factors for T2D include obesity, family history of diabetes, physical inactivity, eth- nicity, and advanced age (1, 2). Diabetes and its complications now rank among the leading causes of death in the United States (2). In fact, diabetes is the leading cause of nontraumatic foot amputation, adult blindness, and need for kidney dialysis, and multiplies risk for myo- cardial infarction, peripheral artery disease, and cerebrovascular disease (3–6). The total estimated direct medical cost attributable to diabetes in the United States in 2012 was $176 billion, with an estimated $76 billion attributable to hospital inpatient care alone. There is a great need to im- prove understanding of T2D and its complex factors to facilitate pre- vention, early detection, and improvements in clinical management. A more precise characterization of T2D patient populations can en- hance our understanding of T2D pathophysiology (7, 8). Current clinical definitions classify diabetes into three major subtypes: type 1 dia- betes (T1D), T2D, and maturity-onset diabetes of the young. Other sub- types based on phenotype bridge the gap between T1D and T2D, for example, latent autoimmune diabetes in adults (LADA) (7) and ketosis- prone T2D. The current categories indicate that the traditional definition of diabetes, especially T2D, might comprise additional subtypes with dis- tinct clinical characteristics. A recent analysis of the longitudinal Whitehall II cohort study demonstrated improved assessment of cardiovascular risks when subgrouping T2D patients according to glucose concentration criteria (9). Genetic association studies reveal that the genetic architec- ture of T2D is profoundly complex (10–12). Identified T2D-associated risk variants exhibit allelic heterogeneity and directional differentiation among populations (13, 14). The apparent clinical and genetic com- plexity and heterogeneity of T2D patient populations suggest that there are opportunities to refine the current, predominantly symptom-based, definition of T2D into additional subtypes (7). Because etiological and pathophysiological differences exist among T2D patients, we hypothesize that a data-driven analysis of a clinical population could identify new T2D subtypes and factors. Here, we de- velop a data-driven, topology-based approach to (i) map the complexity of patient populations using clinical data from electronic medical re- cords (EMRs) and (ii) identify new, emergent T2D patient subgroups with subtype-specific clinical and genetic characteristics. We apply this approachtoadatasetcomprisingmatchedEMRsandgenotypedatafrom more than 11,000 individuals. Topological analysis of these data revealed three distinct T2D subtypes that exhibited distinct patterns of clinical characteristics and disease comorbidities. Further, we identified genetic markers associated with each T2D subtype and performed gene- and pathway-level analysis of subtype genetic associations. Biological and phenotypic features enriched in the genetic analysis corroborated clinical disparities observed among subgroups. Our findings suggest that data- driven,topologicalanalysisofpatientco 내분비내과 LETTER Derma o og - eve c a ca on o k n cancer w h deep neura ne work 피부과 FOCUS LETTERS W W W W W Ca d o og s eve a hy hm a de ec on and c ass ca on n ambu a o y e ec oca d og ams us ng a deep neu a ne wo k M m M FOCUS LETTERS 심장내과 D p a n ng nab obu a m n and on o human b a o y a n v o a on 산부인과 O G NA A W on o On o og nd b e n e e men e ommend on g eemen w h n e pe mu d p n umo bo d 종양내과 D m m B D m OHCA m Kw MD K H MD M H M K m MD M M K m MD M M L m MD M K H K m MD D MD D MD D R K C MD D B H O MD D D m Em M M H K D C C C M H K T w A D C D m M C C M H G m w G R K Tw w C A K H MD D C D m M C C M H K G m w G R K T E m m @ m m A A m O OHCA m m m w w T m m DCA M T w m K OHCA w A C C E P T E D M A N U S C R I P T 응급의학과
- 21. 인공지능 기반 의료기기 FDA 인허가 현황 An infographic about the 29 FDA-approved, AI/ML-based medical technologies. The devices have features such as date pproval; name of the device, its short description and which primary and secondary medical specialty it is related to. S. Benjamens et al. • FDA가 공식 발표에서 AI/ML 기반이라고 언급한 것이 29개 • 진료과: Radiology (46.9%), Cardiology (25%), Internal Medicine/General (15.6%) • 년도별: 2018년(13개) 2019년(10개), 2020년(4개) npj Digi Med 2020
- 22. http://www.hitnews.co.kr/news/articleView.html • 인공지능이 적용된의료기기는 총 53개 (2020년 9월) • 의료영상분석장치소프트웨어(2등급) 26건 • 의료영상검출보조소프트웨어(2등급) 12건 • 의료영상진단보조소프트웨어(3등급) 5건 • 의료영상전송장치소프트웨어(2등급) 3건 • 2018년 (4개), 2019년 (10개), 2020년 9월 (39개) 인공지능 기반 의료기기 국내 인허가 현황
- 23.
- 24. 인공지능 기계학습 딥러닝 전문가 시스템 사이버네틱스 … 인공신경망 결정트리 서포트 벡터머신 … 컨볼루션 신경망 (CNN) 순환신경망(RNN) … 인공지능과 딥러닝의 관계
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- 26. 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- cismaboutAIissofteningintogenuineinterest, 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, clinicalreportsandscientificpublications. BenevolentAI takes a similar approach with algorithmsthatminetheresearchliteratureand 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/Science Photo Library N E W S © 2017 Nature America, Inc., part of Springer Nature. All rights reserved. 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’s head, 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
- 28. 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?
- 29. 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] 10 Oct 2015 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
- 30. 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] 10 Oct 2015 • 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
- 31. 단백질 구조가 밝혀지지않은 경우는?
- 32.
- 33.
- 34.
- 35. Analysis Target Discovery Analysis LeadDiscovery Clinical Trial Post Market Surveillance Digital Healthcare in Drug Development •환자 모집 •데이터 측정: 웨어러블 •디지털 표현형 •원격 임상 시험
- 36. https://rockhealth.com/reports/next-gen-digital-health-innovation-in-clinical-trials/ •판데믹 상황에서 임상시험에관한 디지털 헬스케어 스타트업에 대한 투자 규모가 폭증
- 37. •임상 시험의 각단계에서 다양한 방식으로 디지털 기술이 접목되고 있음 https://rockhealth.com/reports/next-gen-digital-health-innovation-in-clinical-trials/
- 38. •임상 시험의 각단계에서 다양한 방식으로 디지털 기술이 접목되고 있음 https://rockhealth.com/reports/next-gen-digital-health-innovation-in-clinical-trials/ 임상 프로토콜에 맞는 환자가 많은 지역을 택할 수 있게 해줌
- 39. •임상 시험의 각단계에서 다양한 방식으로 디지털 기술이 접목되고 있음 https://rockhealth.com/reports/next-gen-digital-health-innovation-in-clinical-trials/ 인공지능 (자연어처리) 기술로 환자의 진료기록을 분석하여 환자 리크루팅을 도와줌
- 40. Analysis Target Discovery Analysis LeadDiscovery Clinical Trial Post Market Surveillance Digital Healthcare in Drug Development •환자 모집 •데이터 측정: 웨어러블 •디지털 표현형 •원격 임상 시험
- 41. J onathan Cotliar knewhe was ahead of thecurvefouryearsagowhenhejoined Science 37, a company that supports virtual clinical trials conducted mostlyonline.ThefirminLosAngeles, California, was growing slowly before March, receiving about a dozen calls a week from potential clients. But since theCOVID-19pandemicbegan,Science37has been running at fever pitch. Cotliar,thecompany’schiefmedicalofficer, says Science 37 now receives hundreds of enquiries every week from potential clients, such as pharmaceutical companies, medical centresandevenindividualinvestigators.With hospitalsformingtheepicentresofCOVID-19 outbreaks around the world, clinical-trial participantshavebecomereluctanttoattend routine check-ups and monitoring, and health-care workers are stretched beyond their capacity. This has caused researchers to put many clinical trials on hold or to shift to a virtual trial structure by performing consultations online and collecting as much paperwork and data as possible remotely. The pandemic might hasten the kind of change in clinical trials that Cotliar and Science 37 were hoping to make anyway. And there could be other lasting effects on drug development: companies that are usually competitors are now collaborating, and many are trying to make their supply chains more robust to deal with disruption. Some researchers and companies in the drug-developmentfieldsaythesystemmight never be the same again. The pandemic has touched nearly all aspects of the industry, says Kenneth Kaitin, director of the Tufts Center for the Study of DrugDevelopmentinBoston,Massachusetts. “Thishasreallyturnedupsidedownthewhole drug-development process,” he says. “The entire investigative world is focused just on developing treatments for COVID-19.” Some changes are likely to be temporary, Kaitinpredicts.DrugregulatorsintheUnited States and other countries have acted fast to approve clinical trials of therapies and allow new uses of existing medicines to fight COVID-19, without demanding as much data and paperwork as they normally would. Such changes are likely to stick only for as long as the outbreak lasts. “The flexibilities that are being granted for clinical-trial development are being granted under the auspices of a public-health declaration,” says Esther Krofah, executive director of FasterCures, a WashingtonDCthinktank.“That,tome,isvery much an emergency operation.” Trial tweaks In other ways,the pandemic could catalyse lasting change. What might linger, Krofah says, is the culture of collaboration across government, industry and academia that has emerged during the outbreak. “We have traditional competitors working together in newways,”shesays.Anallianceofmorethana dozencompanies—includingGileadinFoster City,California,NovartisinBasel,Switzerland, and WuXi AppTec in Shanghai, China — has been working to discover and test antiviral treatmentsbysharingdataaboutearlyresults and basic science, as well as collaborating on designsforclinicaltrials.Ifthesegroupefforts bear fruit, they might continue, says Krofah. Pharmaceutical companies might also makelong-lastingadjustmentstotheirsupply chains, says David Simchi-Levi, who studies operationsmanagementattheMassachusetts InstituteofTechnologyinCambridge.Overthe past few decades, drug makers have increas- ingly shifted their manufacturing away from the United States and Europe to countries such as India and China, which can produce the drugs at lower cost. But over the past few years,manyfirmshavebeguntolookforways to diversify their supplies of services and raw materials, to reduce the risk of supply inter- ruptionsintheeventofaUS–Chinatradewar, says Simchi-Levi. The coronavirus outbreak could accelerate that trend. “Some shocks were anticipated, but not at this scale,” says Krofah. “This is going to cause a fundamental re-examinationofthatrisk.” Momentum for a shift towards virtual clinical trials has been gradually building for years.Butprogresshadbeenhinderedbyalack ofclearguidancefromregulatorssuchasthe USFoodandDrugAdministration(FDA)anda reluctancetoinvestinthetechnologyneeded torunsuchtrials—untilthepandemichit,says Cotliar. Companies such as Science 37 are suddenly seeing their popularity skyrocket. “It’s exponentially accelerated the adoption curve of what we were already doing,” Cotliar says. “That’s been a bit surreal.” At the University of Minnesota in Minneapolis,forexample,infectious-disease specialist David Boulware and his colleagues conducted a randomized, controlled, virtual trial of the malaria drug hydroxychloroquine tofindoutwhetheritcanprotectpeoplewho are at high risk of contracting COVID-19. The trial, which included more than 800 people and found the treatment had no benefit (D. R. Boulware et al. N. Engl. J. Med. http://doi. org/dxkv; 2020), sent participants medicine byFedExdeliveryandmonitoredtheirhealth remotely. Patient advocates have long pushed for morevirtualtrials,andifthetrendcatcheson, it could speed up participant enrolment — a time-consumingaspectofdrugdevelopment. And now that the pandemic has driven medical centres to set up much-needed technology, and forced the FDA to release guidelines for virtual trials during the pandemic, it is hard to imagine clinical research going back to the way it was before, says Krofah. “We’re going to see this as a new, normalpartofclinicalresearch,”shesays.“The cat is out of the bag.” Heidi Ledford is a senior reporter with Nature in London. ITMIGHT BECOME QUICKERAND EASIERTO TRIALDRUGS Thecrisisispushingthe drug-developmentindustry intoanewnormalofvirtual clinicalresearch. 172 | Nature | Vol 582 | 11 June 2020 FeatureScienceafterthepandemic • 제약 업계에서 COVID-19의 가장 큰 타격: 신약 임상시험 진행이 어려워짐 • 의료진과 임상 참여자들의 대면이 어려워짐 • 병원의 리소스가 코로나 환자 진단/치료에 쏠림 • Virtual Clinical Trial (원격 임상 시험)이 큰 주목: Siteless, Decentralized, Patients-centric • 이전에도 원격 임상 실험에 대한 시도가 있었으나, 판데믹으로 더욱 가속화 • 온라인으로 환자를 모집, 신약 후보 물질은 우편으로 배송 • 원격의료를 통해서 환자 증상 체크, 필요한 경우 간호사가 가정으로 방문
- 42. • 사상 최초의원격 임상시험: 화이자의 REMOTE trial (June 2011) • 휴대폰과 웹기반 기술로 임상시험 사이트를 방문하지 않고, 약 배송 및 데이터 수집 • 과민성 방광 치료제(OB) 데트롤 LA: 4상 결과를 그대로 재현할 수 있는지 여부 검증 목표 • 10개 주에서 600명의 환자를 등록이 목표였으나, 결국 환자 리크루팅에 실패
- 43. https://prahs.com/insights/janssen-pharmaceuticals-and-pra-health-launch-first-fully-virtual-trial-for-heart-failure-drug-approval • 얀센이 PRA와파트너십을 통해, 인허가를 위한 최초의 fully 원격임상시험 시작 (Nov 2019) • CHIEF trial: 당뇨병 치료제 인보카나의 심부전(HF)에 대한 효능 검증 • Primary endpoint : 증상 개선에 대한 PRO (Patient Reported Outcome) • 모바일 플랫폼과 웨어러블에서 얻은 RWD 활용
- 44. • 판데믹 이후,2월 초부터 5월 말까지 제약사들이 취소한 임상 시험은 340개 • 다국적 제약사 중에서 가장 빠르게 움직이는 것은 화이자 • 이미 수십개(dozens of) 임상시험 디자인을 원격으로 하도록 수정 • 향후 18개월 이내에 화이자의 ‘모든’ 임상 시험이 virtual component를 가질 것 • 최초로 fully virtual trial을 시작할 계획: 피부염 관련 임상 (피부 사진을 찍어서 전송 등) • 노바티스도 적극적 • 지난 5년 동안 virtual trial tech 에 투자해왔음 • 최근에는 이미 1,100번 이상 약을 원격으로 보내주고, trial site 200개 이상이 원격으로 진행
- 45. • 환자의 안전이보장되고, 적절한 수단이 있는 경우라면, FDA의 별도 리뷰나, IRB 승인 없이도, 임상시험의 프로토콜을 화상통화, 의약품 배송 등을 통해서 원격으로 변경할 수 있도록 허용함을 적시한 가이드라인
- 46.
- 47. • Science 37 •원격 임상 시험 플랫폼을 제공하는 대표적인 스타트업 • 온라인 환자 등록부터, outcome 평가까지 end-to-end 원격 임상 시험 제공 • COVID-19 시대에 다국적 제약사 등으로부터 큰 주목을 받고 있음 • 2020년 8월 펀딩에 노바티스, 암젠, 사노피 등의 다국적 제약사가 투자자로 참여
- 48. • Science 37+ Ai Cure 의 콜라보레이션 • Ai Cure는 인공지능 기반의 복약 순응도 측정 플랫폼 • 스마트폰 카메라 기반의 인공지능을 통해 환자 본인 확인 / 의약품 확인 / 복용 확인 • Science 37과 협력을 통해서, Ai Cure는 원격 임상 시험에서 환자의 복약을 추적하는 역할
- 49.
- 50. https://www.businessinsider.com/mdlive-gears-up-to-go-public-in-2021-2020-8 코로나 이후 더많은 환자들이 원격진료를 사용하기 시작 COVID-19 •코로나 이전에는 원격의료를 써보지 않았거나/모르는 사람의 비율이 70% 이상이었으나, •코로나 이후에는 이 비율이 감소해서, 6월 기준 40% 이하로 내려옴
- 51. https://www.mckinsey.com/industries/healthcare-systems-and-services/our-insights/telehealth-a-quarter-trillion-dollar-post-covid-19-reality Exhibit 1 How hasCOVID-19 changed the outlook for telehealth? Web <year> <article slug> Exhibit <x> of <y> How has COVID-19 changed the outlook for telehealth? Health systems, independent practices, behavioral health providers, and others rapidly scaled telehealth offerings to fill the gap between need and cancelled in-person care, and are reporting the number of telehealth visits pre-COVID.⁴ 50–175x use of telehealth in 2019 now interested in using telehealth going forward Consumer 1 2 11% 76% While the surge in telehealth has been driven by the immediate goal to avoid exposure to COVID-19, with more than 70 percent of in-person visits cancelled,¹ 76 percent of survey respondents indicated they were highly or moderately likely to use telehealth going forward,² and 74 percent of telehealth users reported high satisfaction.³ Provider In addition, 57% 64% of providers view telehealth more favorably than they did before COVID-19 and are more comfortable using it.⁵ Shift from: To: •2019년 원격진료를 사용해본 미국인은 11%에 불과 •2020년 5월 기준으로 46%의 미국인이 대면 진료를 대체해서 사용 중. •76%는 향후 원격진료를 계속 (highly or moderately) 사용할 의향 있음 •74%는 원격진료를 사용하는 것에 만족 코로나 이후 더 많은 환자들이 원격진료를 사용하기 시작
- 53. • ‘온디맨드 처방’모델: Hims, Hers, Ro, Nurx, Lemonaid Health • 원격으로 문진을 하고, 의약품을 처방 및 배송해주는 모델 • 특정 분야 질병에 대한 처방 여부만 결정: 피임, 발기부전, 탈모, 금연, 여드름, UTI 등 • 규모의 경제 & 낮은 오버헤드: (Hims의 경우) 오프라인 약국보다 50~80% 저렴하게 판매
- 56. 아마존, 의약품 배송스타트업 PillPack을 1조원에 인수 (2019)
- 57. 아마존, 온라인 약국Amazon Pharmacy 진출 선언 (2020)
- 58. 웨어러블-EMR-대면진료/왕진-원격진료-온라인 약국-약 배송-AI스피커 (Amazone Halo)(Cerner 연동) (Amazon Care) (Crossover Health) (Amazon Pharmacy) (Amazon Alexa) 아마존 e커머스 목소리 톤에서 감정도 측정 필요하면 집으로 의사 왕진까지 아마존 프라임 회원들은 제네릭 약가 최대 80% 할인 이틀 배송 병원 예약 복약 알람 리필 처방 데이터 및 서비스 1차 의료 시장 진출
- 59. 우리는 어떻게 변화를맞이해야 하는가?
- 60. Analysis Target Discovery Analysis LeadDiscovery Clinical Trial Post Market Surveillance Digital Healthcare in Drug Development •개인 유전 정보 분석 •블록체인 기반 유전체 분석 •딥러닝 기반 후보 물질 •인공지능+제약사 •SNS 기반의 PMS •블록체인 기반의 PMS •환자 모집 •데이터 측정: 웨어러블 •디지털 표현형 •원격 임상 시험
- 61.
- 62.
- 63. Analysis Target Discovery Analysis LeadDiscovery Clinical Trial Post Market Surveillance Digital Healthcare in Drug Development •개인 유전 정보 분석 •블록체인 기반 유전체 분석 •딥러닝 기반 후보 물질 •인공지능+제약사 •환자 모집 •데이터 측정: 웨어러블 •디지털 표현형 •복약 순응도 •SNS 기반의 PMS •블록체인 기반의 PMS + Digital Therapeutics
- 64. Digital Therapeutics 디지털 치료제/ 디지털 치료기기 / 디지털 신약
- 65. "The Birth ofPrescription Digital Therapeutics," Pear Therapeutics and InCrowd, IIeX 2018”
- 66. 5 www.dtxalliance.org Defining Digital Therapeutics Thoughtleaders across the digital therapeutics industry, supported by the Digital Therapeutics Alliance, collaborated to develop the following comprehensive definition: Digital therapeutics (DTx) deliver evidence-based therapeutic interventions to patients that are driven by high quality software programs to prevent, manage, or treat a medical disorder or disease. They are used independently or in concert with medications, devices, or other therapies to optimize patient care and health outcomes. DTx products incorporate advanced technology best practices relating to design, clinical validation, usability, and data security. They are reviewed and cleared or approved by regulatory bodies as required to support product claims regarding risk, efficacy, and intended use. Digital therapeutics empower patients, healthcare providers, and payers with intelligent and accessible tools for addressing a wide range of conditions through high quality, safe, and effective data-driven interventions. Digital therapeutics present the market with evidence-based technologies that have the ability to elevate medical best practices, address unmet medical needs, expand healthcare access, and improve clinical and health economic outcomes. • 질병을 예방, 관리, 혹은 치료하는 고도의 소프트웨어 프로그램 • 독립적으로 사용될 수도 있고, 약제/기기/다른 치료제와 함께 사용될 수 있음 • 효능, 목적, 위험도 등의 주장과 관련해서는 규제기관의 인허가를 거침 Digital Therapeutics Alliance (DTA) white paper, 2018
- 67. 디지털치료기기 허가·심사 가이드라인(민원인 안내서) 의료 기 기 심 사 부 하여야 한다 다만 판단기준에 따라 디지털치료기기에 해당되지 않더라 의료기기 또는 의료기기가 아닌 것으로 분류될 수 있다 일반적으로 의료기기에 해당하는지의 여부를 판단하기 위해서 의료기기법 제 조의 사용목적으로 사용되는 것으로서 제품에 대한 사 목적은 제조자의 의도에 따라 그 제품의 구조와 형태 표방하는 사 목적과 효과 판매하고자 할 때의 광고 또는 설명 등이 종합적으 고려되어야 한다
- 68. •국내에서는 2019년 하반기부터갑자기 주목 받기 시작 •최근 몇개월 사이 갑자기 ‘디지털 치료제’ 키워드가 언론에 등장: Hype 이 있음 •초기 스타트업 중에서도 이 키워드를 쓰는 곳 증가 / 대기업들도 관심을 가지기 시작 •관계 부처들 사이에서도 이니셔티브 경쟁 시작 •식약처 의료기기심사부에서 허가심사 가이드라인 제정 중 (2020년 8월 발표 예정) •2020년이 국내에서는 원년이 될 것으로 예상.
- 69. 이런 컨셉은 예전부터있던 것 아닌가? 맞습니다.
- 70. 이런 컨셉은 예전부터있던 것 아닌가? 왜 지금에 와서, ‘디지털 치료제’라고 따로 부르며 호들갑인가? 맞습니다. 의학적: 임상적 근거 축적 규제적: 인허가 사례 증가 + 새로운 허가/심사 기준 산업적: 개발 사례 증가 + 시장 출시 시작 + 보험 적용 각 분야의 발전이 임계점을 넘으며 독립적인 분야로 발돋움
- 71. ©2020. HAPPIFY HEALTH.ALL RIGHTS RESERVED 4 Digital therapeutic landscape by relationship (Happify Health, DTx 2020)
- 72. 대표적인 Digital Therapeutics의사례연구 • Pear Therapeutics • Akili Interactive • Click Therapeutics • Dthera Science • Noom, Omada Health • Hurray Positive, SK Health Connect • Virtual Vietnam • AppliedVR • Woebot • Cognoa • Propeller Health • Neofect
- 73. Pear Therapeutics •Pear Therapeutics의reSET •의사의 ‘처방’을 받아, 12주에 걸쳐 알콜, 코카인, 대마 등의 중독과 의존성을 치료 •스마트폰 앱 만으로 치료용 FDA 인허가 (De Novo)를 받은 것은 최초 (2017년 9월) •업계에서는 digital therapeutics의 시초로 이 Pear Therapeutics를 꼽음
- 74. • reSET-O • 2018년12월 10일 Opioid Use Disorder(OUD)에 대해서도 FDA 인허가 • FDA 인허가 받은 두번째 digital therapeutics • Outpatients 에 buprenorphine과 함께 보조적으로 12주 사용한 결과, 대조군 대비 retention 에 유의미한 상승 • overall retention rate of 82.4% vs. 68.4%
- 75. • Somryst: 불면증치료용 DTx 를 2020년 3월 FDA 인허가 • 510(k)와 Pre-Cert 를 동시에 진행 • 불면증 CBT (인지행동치료) 소프트웨어: 처방을 받아야만 사용 가능 • 두 개의 RCT (무작위 대조군 임상시험)을 통해서 유효성을 증명 • 총 1,400명 이상의 불면증 및 우울증+불면증 환자가 등록 • 한 대규모 임상에서 1,00명의 성인에게 9주 동안 치료를 제공하여 불면증에 유의미한 개선 • 임상적 유효성이 18개월 동안 지속 되었음
- 77. • EndeavorRx: 아이패드기반의 ADHD 치료 목적의 게임 • 8-12세 ADHD의 attention 증진 목적으로 2020년 4월 FDA 인허가 • 처방을 받아야만 사용가능 • 5개의 임상 연구에서, ADHD 관련 효능 검증 • 2013 Nature 에 발표된, 기능성 게임 ‘뉴로 레이서’를 기반으로 만든 게임 • Pivotal Trial (STARS-ADHD) • ADHD 환자 600명 규모의 RCT 에서 attention (TOVA) 의 유의미한 개선 증명 • ADHD에 대한 secondary outcome 에 대해서는 유의미한 개선 보여주지 못함
- 78.
- 79.
- 80. • 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)
- 81. 대표적인 Digital Therapeutics의사례연구 • Pear Therapeutics • Akili Interactive • Click Therapeutics • Dthera Science • Noom, Omada Health • Hurray Positive, SK Health Connect • Big Health • AppliedVR • Woebot • Cognoa • Propeller Health • Neofect
- 82. Our first product isSleepio • A fully automated Cognitive Behavioral Therapy (CBT) program for insomnia • Accessible via app and web, it is an effective digital medicine for insomnia • Helps alleviate co-morbid anxiety and depression For more info see bighealth.com/our-solution Digital Therapeutics and Digital Medicine Summit | February 2018 • 불면증에 대한 인지행동치료 (Cognitive Behavioral Therapy, CBT) • 인지 치료: 잠에 대한 잘못된 생각을 바로 잡는 치료 (교육, 자극 조절, 인지 재구성…) • 행동 치료: 잠에 방해가 되는 행동/습관 교정 (수면 위생법, 이완/호흡 훈련 …) • Sleepio: 기존에는 대면으로 치료 받던 방식을, App으로 구현한 것 (최소 6주 과정)
- 83. •2019년 영국의 NHS가천만 명에게 Sleepio를 지불하기로 결정 •DTx의 무한한 확장성: 대규모 인원에게 단번에 약효를 전달할 수 있음 •이 확장성은 양날의 검일 수 있음: 부작용 및 비용효과성이 예상과 다르다면?
- 84. •미국의 대형 PBM(PharmacyBenefit Manager)인 CVS Health •Sleepio 의 보험 적용을 권장하기로 결정 •CVS Health는 미국인의 30% 정도의 처방약 결정에 관여
- 85. • 미국의 대형PBM(Pharmacy Benefit Manager)인 Express Scripts • “Digital Health Formulary”를 발표 (19년 12월): 공식 권고 리스트 • 정확성 / 사용성 / 비용 효과성을 모두 고려
- 86. • First Cohort:4가지 카테고리의 15가지 앱이 포함 • 당뇨 / 심혈관 / 폐질환 / 정신 건강 • 이후에도 앱이 추가될 것임을 예고 • 향후 민간 보험사에서 이러한 헬스케어 앱의 활용/coverage 확대 예상
- 87. COVID-19 시대의 디지털치료제, 왜 주목받는가? 비대면, 그리고 확장성
- 88. COVID-19 판데믹이, 인구수준의 정신 건강 문제를 야기하고 있음
- 89. COVID-19 판데믹이, 인구수준의 정신 건강 문제를 야기하고 있음
- 90. Letters RESEARCH LETTER Psychological Distressand Loneliness Reported by US Adults in 2018 and April 2020 Coronavirus disease 2019 (COVID-19) introduced stressors to mental health, including loneliness stemming from social isolation, fear of contracting the disease, economic strain, and uncertainty about the future. We fielded a national sur- vey measuring symptoms of psychological distress and lone- liness among US adults in April 2020 and compared results with national data from 2018. Methods|WefieldedtheJohnsHopkinsCOVID-19CivicLifeand Public Health Survey from April 7 to April 13, 2020, using NORC’s AmeriSpeak Panel. AmeriSpeak is a probability- basedpaneldesignedtoberepresentativeoftheUSadultpopu- lation. The panel is sourced from NORC’s area probability sample and from a US Postal Service address–based sample covering 97% of US households.1 The panel has a recruitment rate of 34% and includes approximately 35 000 members. The samplefortheJohnsHopkinssurveywasdrawnfromthispanel and the survey was administered online. NORC obtains in- formed consent prior to enrolling individuals in the panel. The Johns Hopkins Bloomberg School of Public Health insti- tutional review board deemed this study not human partici- pants research and waived informed consent. We measured the prevalence of symptoms of serious psy- chological distress in the overall sample and among demo- graphic subgroups using the Kessler 6 Psychological Distress Scale, with the validated measure of serious distress defined as a score of 13 or higher on the 0- to 24-point scale.2 We also measured the proportion of respondents who reported that they always or often feel lonely in response to the item “How often do you feel lonely?” with response options always, of- ten, sometimes, rarely, and never. We compared the prevalence of symptoms of serious psychological distress in April 2020 with an identical mea- sure from the 2018 National Health Interview Survey (NHIS), which used the Kessler 6 scale among 25 417 adults aged 18 years or older in household interviews. The 2018 NHIS response rate was 64.2%.3 For each measure, we calculated proportions and 95% CIs usingStataversion15(StataCorp).TheJohnsHopkinsandNHIS survey data were analyzed separately. Analyses of both data sets incorporated survey sampling weights to generate nation- ally representative estimates. Results | The survey response rate was 70.4%, with a final sample of 1468 adults aged 18 years or older. In April 2020, 13.6% (95% CI, 11.1%-16.5%) of US adults re- ported symptoms of serious psychological distress, relative to 3.9% (95% CI, 3.6%-4.2%) in 2018 (Figure). Among the sub- groups examined, in April 2020, symptoms of psychological distress were highest among young adults aged 18 to 29 years (24.0% [95% CI, 16.3%-33.8%]), adults with household in- come of less than $35 000 per year (19.3% [95% CI, 14.2%- 25.6%]), and Hispanic adults (18.3% [95% CI, 11.2%-28.3%]). Figure. Psychological Distress Among US Adults Aged 18 Years or Older Overall and by Subgroup, April 2020 vs 2018 5 10 15 20 25 30 35 0 Adults aged ≥18 y Sex Male Female Living situation Lives alone Lives with others Race/ethnicitya White Black Hispanic Age group, y 18-29 30-54 ≥55 Household income, $ <35000 35000-74999 ≥75000 April 2020 2018 US adults aged ≥18 y with serious psychological distress, % April 2020 measures are from wave 1 of the Johns Hopkins COVID-19 Civic Life and Public Health Survey, fielded April 7-13, 2020 (N = 1468 adults aged !18 years). 2018 Measures of psychological distress are from the 2018 National Health Interview Survey (N = 25 417 adults aged !18 years). Psychological distress was measured using the Kessler 6 Psychological Distress Scale, with scores of 13 or higher indicating serious psychological distress. The error bars indicate 95% CIs. a Race/ethnicity was collected as part of the demographic profile in both the April 2020 Johns Hopkins survey and the 2018 National Health Interview Survey. In both surveys, the options were defined by the study investigators, and participants classified their own race/ethnicity. jama.com (Reprinted) JAMA Published online June 3, 2020 E1 © 2020 American Medical Association. All rights reserved. JAMA 2020 In April 2020, 13.6% of US adults reported symptoms of serious psychological distress, relative to 3.9% in 2018
- 91. • COVID-19 판데믹에서,디지털 치료제의 역할이 부상 중 • 판데믹에서 비대면으로, 확장가능한 방식으로 의료적 효용을 전달 가능 • 특히, 코로나 때문에 방치되는 기저 질환자, 만성 질환자들 치료 가능
- 92. • FDA는 정신질환 (psychiatric disorder)를 치료하는 디지털 치료제에 대 해서 510(k) 면제 등 규제를 한시적으로 면제 해주겠다는 것을 발표 • 미국에서 출시를 기다리고 있는 다양한 정신 질환 관련 DTx (우울증, 불면증, 공황장애, 불안, ADHD, PTSD...) 등이 다양하게 시장에 출시 가능
- 93. Contains Nonbinding Recommendations EnforcementPolicy for Digital Health Devices For Treating Psychiatric Disorders During the Coronavirus Disease 2019 (COVID-19) Public Health Emergency Guidance for Industry and Food and Drug Administration Staff April 2020 U.S. Department of Health and Human Services Food and Drug Administration Center for Devices and Radiological Health (CDRH) Office of Product Evaluation and Quality (OPEQ) ADHD Schizophrenia Anxiety
- 94. Digital Therapeutics, TheUncharted Territory 디지털 치료제, 앞으로 증명해야 할 것들
- 95. 임상-인허가-보험-의사 처방-RWE-환자 사용 (RealWorld Evidence)
- 96. 임상-인허가-보험-의사 처방-RWE-환자 사용 •DTx에도 RCT가 필요한가 • 임상 연구 디자인은 어떻게 해야 하나 • 효용/가치를 어떻게 증명할 것인가 (Real World Evidence)
- 97. 임상-인허가-보험-의사 처방-RWE-환자 사용 •DTx에도 RCT가 필요한가 • 임상 연구 디자인은 어떻게 해야 하나 • 효용/가치를 어떻게 증명할 것인가 • 인허가 없이 공산품으로 판매할 것인지, 의료기기 인허가를 받을 것인지 결정 • 인허가를 받겠다면, 어떤 규제 방식을 택할 것인가 • 규제기관은 DTx의 효능/부작용을 어떻게 심사할까 (Real World Evidence)
- 98. 임상-인허가-보험-의사 처방-RWE-환자 사용 •DTx에도 RCT가 필요한가 • 임상 연구 디자인은 어떻게 해야 하나 • 효용/가치를 어떻게 증명할 것인가 • 인허가 없이 공산품으로 판매할 것인지, 의료기기 인허가를 받을 것인지 결정 • 인허가를 받겠다면, 어떤 규제 방식을 택할 것인가 • 규제기관은 DTx의 효능/부작용을 어떻게 심사할까 • 보험사는 DTx를 어떻게 바라볼까 • 수가를 받을 수 있는가/받아야 하는가 (Real World Evidence)
- 99. 임상-인허가-보험-의사 처방-RWE-환자 사용 •DTx에도 RCT가 필요한가 • 임상 연구 디자인은 어떻게 해야 하나 • 효용/가치를 어떻게 증명할 것인가 • 인허가 없이 공산품으로 판매할 것인지, 의료기기 인허가를 받을 것인지 결정 • 인허가를 받겠다면, 어떤 규제 방식을 택할 것인가 • 규제기관은 DTx의 효능/부작용을 어떻게 심사할까 • 보험사는 DTx를 어떻게 바라볼까 • 수가를 받을 수 있는가/받아야 하는가 • 기존 약 대신에 의사가 처방할까 • 기존 약 대비 어떤 강점을 가져야 하나 (Real World Evidence)
- 100. 임상-인허가-보험-의사 처방-RWE-환자 사용 •DTx에도 RCT가 필요한가 • 임상 연구 디자인은 어떻게 해야 하나 • 효용/가치를 어떻게 증명할 것인가 • 인허가 없이 공산품으로 판매할 것인지, 의료기기 인허가를 받을 것인지 결정 • 인허가를 받겠다면, 어떤 규제 방식을 택할 것인가 • 규제기관은 DTx의 효능/부작용을 어떻게 심사할까 • 보험사는 DTx를 어떻게 바라볼까 • 수가를 받을 수 있는가/받아야 하는가 • 기존 약 대신에 의사가 처방할까 • 기존 약 대비 어떤 강점을 가져야 하나 • 의사들이 활용할 여건이 되는가 • EMR 속으로 어떻게 통합 • 진단/치료/관리 기준으로 활용 가능한가 (Real World Evidence)
- 101. 임상-인허가-보험-의사 처방-RWE-환자 사용 •DTx에도 RCT가 필요한가 • 임상 연구 디자인은 어떻게 해야 하나 • 효용/가치를 어떻게 증명할 것인가 • 인허가 없이 공산품으로 판매할 것인지, 의료기기 인허가를 받을 것인지 결정 • 인허가를 받겠다면, 어떤 규제 방식을 택할 것인가 • 규제기관은 DTx의 효능/부작용을 어떻게 심사할까 • 보험사는 DTx를 어떻게 바라볼까 • 수가를 받을 수 있는가/받아야 하는가 • 기존 약 대신에 의사가 처방할까 • 기존 약 대비 어떤 강점을 가져야 하나 • 의사들이 활용할 여건이 되는가 • EMR 속으로 어떻게 통합 • 진단/치료/관리 기준으로 활용 가능한가 • DTx를 처방 받으면 환자는 어떻게 느낄까 • 지속사용성: 지속해서 사용할까 • 디지털 리터러시: 사용성에 지장은 없는가 (Real World Evidence)
- 102. Analysis Target Discovery Analysis LeadDiscovery Clinical Trial Post Market Surveillance Digital Healthcare in Drug Development •개인 유전 정보 분석 •블록체인 기반 유전체 분석 •딥러닝 기반 후보 물질 •인공지능+제약사 •환자 모집 •데이터 측정: 웨어러블 •디지털 표현형 •원격 임상 시험 •SNS 기반의 PMS •블록체인 기반의 PMS + Digital Therapeutics
- 103. Feedback/Questions • E-mail: yoonsup.choi@gmail.com •Blog: http://www.yoonsupchoi.com • Facebook: Yoon Sup Choi • 유튜브: 최윤섭의 디지털 헬스케어