![EDITORIAL OPEN
Digital medicine, on its way to being just plain medicine
npj Digital Medicine (2018)1:20175 ; doi:10.1038/
s41746-017-0005-1
There are already nearly 30,000 peer-reviewed English-language
scientific journals, producing an estimated 2.5 million articles a year.1
So why another, and why one focused specifically on digital
medicine?
To answer that question, we need to begin by defining what
“digital medicine” means: using digital tools to upgrade the
practice of medicine to one that is high-definition and far more
individualized. It encompasses our ability to digitize human beings
using biosensors that track our complex physiologic systems, but
also the means to process the vast data generated via algorithms,
cloud computing, and artificial intelligence. It has the potential to
democratize medicine, with smartphones as the hub, enabling
each individual to generate their own real world data and being
far more engaged with their health. Add to this new imaging
tools, mobile device laboratory capabilities, end-to-end digital
clinical trials, telemedicine, and one can see there is a remarkable
array of transformative technology which lays the groundwork for
a new form of healthcare.
As is obvious by its definition, the far-reaching scope of digital
medicine straddles many and widely varied expertise. Computer
scientists, healthcare providers, engineers, behavioral scientists,
ethicists, clinical researchers, and epidemiologists are just some of
the backgrounds necessary to move the field forward. But to truly
accelerate the development of digital medicine solutions in health
requires the collaborative and thoughtful interaction between
individuals from several, if not most of these specialties. That is the
primary goal of npj Digital Medicine: to serve as a cross-cutting
resource for everyone interested in this area, fostering collabora-
tions and accelerating its advancement.
Current systems of healthcare face multiple insurmountable
challenges. Patients are not receiving the kind of care they want
and need, caregivers are dissatisfied with their role, and in most
countries, especially the United States, the cost of care is
unsustainable. We are confident that the development of new
systems of care that take full advantage of the many capabilities
that digital innovations bring can address all of these major issues.
Researchers too, can take advantage of these leading-edge
technologies as they enable clinical research to break free of the
confines of the academic medical center and be brought into the
real world of participants’ lives. The continuous capture of multiple
interconnected streams of data will allow for a much deeper
refinement of our understanding and definition of most pheno-
types, with the discovery of novel signals in these enormous data
sets made possible only through the use of machine learning.
Our enthusiasm for the future of digital medicine is tempered by
the recognition that presently too much of the publicized work in
this field is characterized by irrational exuberance and excessive
hype. Many technologies have yet to be formally studied in a
clinical setting, and for those that have, too many began and
ended with an under-powered pilot program. In addition, there are
more than a few examples of digital “snake oil” with substantial
uptake prior to their eventual discrediting.2
Both of these practices
are barriers to advancing the field of digital medicine.
Our vision for npj Digital Medicine is to provide a reliable,
evidence-based forum for all clinicians, researchers, and even
patients, curious about how digital technologies can transform
every aspect of health management and care. Being open source,
as all medical research should be, allows for the broadest possible
dissemination, which we will strongly encourage, including
through advocating for the publication of preprints
And finally, quite paradoxically, we hope that npj Digital
Medicine is so successful that in the coming years there will no
longer be a need for this journal, or any journal specifically
focused on digital medicine. Because if we are able to meet our
primary goal of accelerating the advancement of digital medicine,
then soon, we will just be calling it medicine. And there are
already several excellent journals for that.
ACKNOWLEDGEMENTS
Supported by the National Institutes of Health (NIH)/National Center for Advancing
Translational Sciences grant UL1TR001114 and a grant from the Qualcomm Foundation.
ADDITIONAL INFORMATION
Competing interests:The authors declare no competing financial interests.
Publisher's note:Springer Nature remains neutral with regard to jurisdictional claims
in published maps and institutional affiliations.
Change history:The original version of this Article had an incorrect Article number
of 5 and an incorrect Publication year of 2017. These errors have now been corrected
in the PDF and HTML versions of the Article.
Steven R. Steinhubl1
and Eric J. Topol1
1
Scripps Translational Science Institute, 3344 North Torrey Pines
Court, Suite 300, La Jolla, CA 92037, USA
Correspondence: Steven R. Steinhubl (steinhub@scripps.edu) or
Eric J. Topol (etopol@scripps.edu)
REFERENCES
1. Ware, M. & Mabe, M. The STM report: an overview of scientific and scholarly journal
publishing 2015 [updated March]. http://digitalcommons.unl.edu/scholcom/92017
(2015).
2. Plante, T. B., Urrea, B. & MacFarlane, Z. T. et al. Validation of the instant blood
pressure smartphone App. JAMA Intern. Med. 176, 700–702 (2016).
Open Access This article is licensed under a Creative Commons
Attribution 4.0 International License, which permits use, sharing,
adaptation, distribution and reproduction in any medium or format, as long as you give
appropriate credit to the original author(s) and the source, provide a link to the Creative
Commons license, and indicate if changes were made. The images or other third party
material in this article are included in the article’s Creative Commons license, unless
indicated otherwise in a credit line to the material. If material is not included in the
article’s Creative Commons license and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly
from the copyright holder. To view a copy of this license, visit http://creativecommons.
org/licenses/by/4.0/.
© The Author(s) 2018
Received: 19 October 2017 Accepted: 25 October 2017
www.nature.com/npjdigitalmed
Published in partnership with the Scripps Translational Science Institute
디지털 의료의 미래는?
일상적인 의료가 되는 것](https://image.slidesharecdn.com/digitalhealth-190427130553/75/slide-21-2048.jpg)
![Fig 1. What can consumer wearables do? Heart rate can be measured with an oximeter built into a ring [3], muscle activity with an electromyographi
sensor embedded into clothing [4], stress with an electodermal sensor incorporated into a wristband [5], and physical activity or sleep patterns via an
accelerometer in a watch [6,7]. In addition, a female’s most fertile period can be identified with detailed body temperature tracking [8], while levels of me
attention can be monitored with a small number of non-gelled electroencephalogram (EEG) electrodes [9]. Levels of social interaction (also known to a
PLOS Medicine 2016](https://image.slidesharecdn.com/digitalhealth-190427130553/75/slide-60-2048.jpg)
![ARTICLE OPEN
Scalable and accurate deep learning with electronic health
records
Alvin Rajkomar 1,2
, Eyal Oren1
, Kai Chen1
, Andrew M. Dai1
, Nissan Hajaj1
, Michaela Hardt1
, Peter J. Liu1
, Xiaobing Liu1
, Jake Marcus1
,
Mimi Sun1
, Patrik Sundberg1
, Hector Yee1
, Kun Zhang1
, Yi Zhang1
, Gerardo Flores1
, Gavin E. Duggan1
, Jamie Irvine1
, Quoc Le1
,
Kurt Litsch1
, Alexander Mossin1
, Justin Tansuwan1
, De Wang1
, James Wexler1
, Jimbo Wilson1
, Dana Ludwig2
, Samuel L. Volchenboum3
,
Katherine Chou1
, Michael Pearson1
, Srinivasan Madabushi1
, Nigam H. Shah4
, Atul J. Butte2
, Michael D. Howell1
, Claire Cui1
,
Greg S. Corrado1
and Jeffrey Dean1
Predictive modeling with electronic health record (EHR) data is anticipated to drive personalized medicine and improve healthcare
quality. Constructing predictive statistical models typically requires extraction of curated predictor variables from normalized EHR
data, a labor-intensive process that discards the vast majority of information in each patient’s record. We propose a representation
of patients’ entire raw EHR records based on the Fast Healthcare Interoperability Resources (FHIR) format. We demonstrate that
deep learning methods using this representation are capable of accurately predicting multiple medical events from multiple
centers without site-specific data harmonization. We validated our approach using de-identified EHR data from two US academic
medical centers with 216,221 adult patients hospitalized for at least 24 h. In the sequential format we propose, this volume of EHR
data unrolled into a total of 46,864,534,945 data points, including clinical notes. Deep learning models achieved high accuracy for
tasks such as predicting: in-hospital mortality (area under the receiver operator curve [AUROC] across sites 0.93–0.94), 30-day
unplanned readmission (AUROC 0.75–0.76), prolonged length of stay (AUROC 0.85–0.86), and all of a patient’s final discharge
diagnoses (frequency-weighted AUROC 0.90). These models outperformed traditional, clinically-used predictive models in all cases.
We believe that this approach can be used to create accurate and scalable predictions for a variety of clinical scenarios. In a case
study of a particular prediction, we demonstrate that neural networks can be used to identify relevant information from the
patient’s chart.
npj Digital Medicine (2018)1:18 ; doi:10.1038/s41746-018-0029-1
INTRODUCTION
The promise of digital medicine stems in part from the hope that,
by digitizing health data, we might more easily leverage computer
information systems to understand and improve care. In fact,
routinely collected patient healthcare data are now approaching
the genomic scale in volume and complexity.1
Unfortunately,
most of this information is not yet used in the sorts of predictive
statistical models clinicians might use to improve care delivery. It
is widely suspected that use of such efforts, if successful, could
provide major benefits not only for patient safety and quality but
also in reducing healthcare costs.2–6
In spite of the richness and potential of available data, scaling
the development of predictive models is difficult because, for
traditional predictive modeling techniques, each outcome to be
predicted requires the creation of a custom dataset with specific
variables.7
It is widely held that 80% of the effort in an analytic
model is preprocessing, merging, customizing, and cleaning
datasets,8,9
not analyzing them for insights. This profoundly limits
the scalability of predictive models.
Another challenge is that the number of potential predictor
variables in the electronic health record (EHR) may easily number
in the thousands, particularly if free-text notes from doctors,
nurses, and other providers are included. Traditional modeling
approaches have dealt with this complexity simply by choosing a
very limited number of commonly collected variables to consider.7
This is problematic because the resulting models may produce
imprecise predictions: false-positive predictions can overwhelm
physicians, nurses, and other providers with false alarms and
concomitant alert fatigue,10
which the Joint Commission identified
as a national patient safety priority in 2014.11
False-negative
predictions can miss significant numbers of clinically important
events, leading to poor clinical outcomes.11,12
Incorporating the
entire EHR, including clinicians’ free-text notes, offers some hope
of overcoming these shortcomings but is unwieldy for most
predictive modeling techniques.
Recent developments in deep learning and artificial neural
networks may allow us to address many of these challenges and
unlock the information in the EHR. Deep learning emerged as the
preferred machine learning approach in machine perception
problems ranging from computer vision to speech recognition,
but has more recently proven useful in natural language
processing, sequence prediction, and mixed modality data
settings.13–17
These systems are known for their ability to handle
large volumes of relatively messy data, including errors in labels
Received: 26 January 2018 Revised: 14 March 2018 Accepted: 26 March 2018
1
Google Inc, Mountain View, CA, USA; 2
University of California, San Francisco, San Francisco, CA, USA; 3
University of Chicago Medicine, Chicago, IL, USA and 4
Stanford University,
Stanford, CA, USA
Correspondence: Alvin Rajkomar (alvinrajkomar@google.com)
These authors contributed equally: Alvin Rajkomar, Eyal Oren
www.nature.com/npjdigitalmed
Published in partnership with the Scripps Translational Science Institute
•2018년 1월 구글이 전자의무기록(EMR)을 분석하여, 환자 치료 결과를 예측하는 인공지능 발표
•환자가 입원 중에 사망할 것인지
•장기간 입원할 것인지
•퇴원 후에 30일 내에 재입원할 것인지
•퇴원 시의 진단명
•이번 연구의 특징: 확장성
•과거 다른 연구와 달리 EMR의 일부 데이터를 pre-processing 하지 않고,
•전체 EMR 를 통채로 모두 분석하였음: UCSF, UCM (시카고 대학병원)
•특히, 비정형 데이터인 의사의 진료 노트도 분석
Nat Digi Med 2018](https://image.slidesharecdn.com/digitalhealth-190427130553/75/slide-182-2048.jpg)
![AJR:209, December 2017 1
Since 1992, concerns regarding interob-
server variability in manual bone age esti-
mation [4] have led to the establishment of
several automatic computerized methods for
bone age estimation, including computer-as-
sisted skeletal age scores, computer-aided
skeletal maturation assessment systems, and
BoneXpert (Visiana) [5–14]. BoneXpert was
developed according to traditional machine-
learning techniques and has been shown to
have a good performance for patients of var-
ious ethnicities and in various clinical set-
tings [10–14]. The deep-learning technique
is an improvement in artificial neural net-
works. Unlike traditional machine-learning
techniques, deep-learning techniques allow
an algorithm to program itself by learning
from the images given a large dataset of la-
beled examples, thus removing the need to
specify rules [15].
Deep-learning techniques permit higher
levels of abstraction and improved predic-
tions from data. Deep-learning techniques
Computerized Bone Age
Estimation Using Deep Learning–
Based Program: Evaluation of the
Accuracy and Efficiency
Jeong Rye Kim1
Woo Hyun Shim1
Hee Mang Yoon1
Sang Hyup Hong1
Jin Seong Lee1
Young Ah Cho1
Sangki Kim2
Kim JR, Shim WH, Yoon MH, et al.
1
Department of Radiology and Research Institute of
Radiology, Asan Medical Center, University of Ulsan
College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu,
Seoul 05505, South Korea. Address correspondence to
H. M. Yoon (espoirhm@gmail.com).
2
Vuno Research Center, Vuno Inc., Seoul, South Korea.
Pediatric Imaging • Original Research
Supplemental Data
Available online at www.ajronline.org.
AJR 2017; 209:1–7
0361–803X/17/2096–1
© American Roentgen Ray Society
B
one age estimation is crucial for
developmental status determina-
tions and ultimate height predic-
tions in the pediatric population,
particularly for patients with growth disor-
ders and endocrine abnormalities [1]. Two
major left-hand wrist radiograph-based
methods for bone age estimation are current-
ly used: the Greulich-Pyle [2] and Tanner-
Whitehouse [3] methods. The former is much
more frequently used in clinical practice.
Greulich-Pyle–based bone age estimation is
performed by comparing a patient’s left-hand
radiograph to standard radiographs in the
Greulich-Pyle atlas and is therefore simple
and easily applied in clinical practice. How-
ever, the process of bone age estimation,
which comprises a simple comparison of
multiple images, can be repetitive and time
consuming and is thus sometimes burden-
some to radiologists. Moreover, the accuracy
depends on the radiologist’s experience and
tends to be subjective.
Keywords: bone age, children, deep learning, neural
network model
DOI:10.2214/AJR.17.18224
J. R. Kim and W. H. Shim contributed equally to this work.
Received March 12, 2017; accepted after revision
July 7, 2017.
S. Kim is employed by Vuno, Inc., which created the deep
learning–based automatic software system for bone
age determination. J. R. Kim, W. H. Shim, H. M. Yoon,
S. H. Hong, J. S. Lee, and Y. A. Cho are employed by
Asan Medical Center, which holds patent rights for the
deep learning–based automatic software system for
bone age assessment.
OBJECTIVE. The purpose of this study is to evaluate the accuracy and efficiency of a
new automatic software system for bone age assessment and to validate its feasibility in clini-
cal practice.
MATERIALS AND METHODS. A Greulich-Pyle method–based deep-learning tech-
nique was used to develop the automatic software system for bone age determination. Using
this software, bone age was estimated from left-hand radiographs of 200 patients (3–17 years
old) using first-rank bone age (software only), computer-assisted bone age (two radiologists
with software assistance), and Greulich-Pyle atlas–assisted bone age (two radiologists with
Greulich-Pyle atlas assistance only). The reference bone age was determined by the consen-
sus of two experienced radiologists.
RESULTS. First-rank bone ages determined by the automatic software system showed a
69.5% concordance rate and significant correlations with the reference bone age (r = 0.992;
p < 0.001). Concordance rates increased with the use of the automatic software system for
both reviewer 1 (63.0% for Greulich-Pyle atlas–assisted bone age vs 72.5% for computer-as-
sisted bone age) and reviewer 2 (49.5% for Greulich-Pyle atlas–assisted bone age vs 57.5% for
computer-assisted bone age). Reading times were reduced by 18.0% and 40.0% for reviewers
1 and 2, respectively.
CONCLUSION. Automatic software system showed reliably accurate bone age estima-
tions and appeared to enhance efficiency by reducing reading times without compromising
the diagnostic accuracy.
Kim et al.
Accuracy and Efficiency of Computerized Bone Age Estimation
Pediatric Imaging
Original Research
Downloadedfromwww.ajronline.orgbyFloridaAtlanticUnivon09/13/17fromIPaddress131.91.169.193.CopyrightARRS.Forpersonaluseonly;allrightsreserved
• 총 환자의 수: 200명
• 레퍼런스: 경험 많은 소아영상의학과 전문의 2명(18년, 4년 경력)의 컨센서스
• 의사A: 소아영상 세부전공한 영상의학 전문의 (500례 이상의 판독 경험)
• 의사B: 영상의학과 2년차 전공의 (판독법 하루 교육 이수 + 20례 판독)
• 인공지능: VUNO의 골연령 판독 딥러닝
AJR Am J Roentgenol. 2017 Dec;209(6):1374-1380.](https://image.slidesharecdn.com/digitalhealth-190427130553/75/slide-193-2048.jpg)
![This copy is for personal use only.
To order printed copies, contact reprints@rsna.org
This copy is for personal use only.
To order printed copies, contact reprints@rsna.org
ORIGINAL RESEARCH • THORACIC IMAGING
hest radiography, one of the most common diagnos- intraobserver agreements because of its limited spatial reso-
Development and Validation of Deep
Learning–based Automatic Detection
Algorithm for Malignant Pulmonary Nodules
on Chest Radiographs
Ju Gang Nam, MD* • Sunggyun Park, PhD* • Eui Jin Hwang, MD • Jong Hyuk Lee, MD • Kwang-Nam Jin, MD,
PhD • KunYoung Lim, MD, PhD • Thienkai HuyVu, MD, PhD • Jae Ho Sohn, MD • Sangheum Hwang, PhD • Jin
Mo Goo, MD, PhD • Chang Min Park, MD, PhD
From the Department of Radiology and Institute of Radiation Medicine, Seoul National University Hospital and College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul
03080, Republic of Korea (J.G.N., E.J.H., J.M.G., C.M.P.); Lunit Incorporated, Seoul, Republic of Korea (S.P.); Department of Radiology, Armed Forces Seoul Hospital,
Seoul, Republic of Korea (J.H.L.); Department of Radiology, Seoul National University Boramae Medical Center, Seoul, Republic of Korea (K.N.J.); Department of
Radiology, National Cancer Center, Goyang, Republic of Korea (K.Y.L.); Department of Radiology and Biomedical Imaging, University of California, San Francisco,
San Francisco, Calif (T.H.V., J.H.S.); and Department of Industrial & Information Systems Engineering, Seoul National University of Science and Technology, Seoul,
Republic of Korea (S.H.). Received January 30, 2018; revision requested March 20; revision received July 29; accepted August 6. Address correspondence to C.M.P.
(e-mail: cmpark.morphius@gmail.com).
Study supported by SNUH Research Fund and Lunit (06–2016–3000) and by Seoul Research and Business Development Program (FI170002).
*J.G.N. and S.P. contributed equally to this work.
Conflicts of interest are listed at the end of this article.
Radiology 2018; 00:1–11 • https://doi.org/10.1148/radiol.2018180237 • Content codes:
Purpose: To develop and validate a deep learning–based automatic detection algorithm (DLAD) for malignant pulmonary nodules
on chest radiographs and to compare its performance with physicians including thoracic radiologists.
Materials and Methods: For this retrospective study, DLAD was developed by using 43292 chest radiographs (normal radiograph–
to–nodule radiograph ratio, 34067:9225) in 34676 patients (healthy-to-nodule ratio, 30784:3892; 19230 men [mean age, 52.8
years; age range, 18–99 years]; 15446 women [mean age, 52.3 years; age range, 18–98 years]) obtained between 2010 and 2015,
which were labeled and partially annotated by 13 board-certified radiologists, in a convolutional neural network. Radiograph clas-
sification and nodule detection performances of DLAD were validated by using one internal and four external data sets from three
South Korean hospitals and one U.S. hospital. For internal and external validation, radiograph classification and nodule detection
performances of DLAD were evaluated by using the area under the receiver operating characteristic curve (AUROC) and jackknife
alternative free-response receiver-operating characteristic (JAFROC) figure of merit (FOM), respectively. An observer performance
test involving 18 physicians, including nine board-certified radiologists, was conducted by using one of the four external validation
data sets. Performances of DLAD, physicians, and physicians assisted with DLAD were evaluated and compared.
Results: According to one internal and four external validation data sets, radiograph classification and nodule detection perfor-
mances of DLAD were a range of 0.92–0.99 (AUROC) and 0.831–0.924 (JAFROC FOM), respectively. DLAD showed a higher
AUROC and JAFROC FOM at the observer performance test than 17 of 18 and 15 of 18 physicians, respectively (P , .05), and
all physicians showed improved nodule detection performances with DLAD (mean JAFROC FOM improvement, 0.043; range,
0.006–0.190; P , .05).
Conclusion: This deep learning–based automatic detection algorithm outperformed physicians in radiograph classification and nod-
ule detection performance for malignant pulmonary nodules on chest radiographs, and it enhanced physicians’ performances when
used as a second reader.
©RSNA, 2018
Online supplemental material is available for this article.
• 43,292 chest PA (normal:nodule=34,067:9225)
• labeled/annotated by 13 board-certified radiologists.
• DLAD were validated 1 internal + 4 external datasets
• 서울대병원 / 보라매병원 / 국립암센터 / UCSF
• Classification / Lesion localization
• 인공지능 vs. 의사 vs. 인공지능+의사
• 다양한 수준의 의사와 비교
• Non-radiology / radiology residents
• Board-certified radiologist / Thoracic radiologists](https://image.slidesharecdn.com/digitalhealth-190427130553/75/slide-197-2048.jpg)
![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.](https://image.slidesharecdn.com/digitalhealth-190427130553/75/slide-206-2048.jpg)
![Fig 1. What can consumer wearables do? Heart rate can be measured with an oximeter built into a ring [3], muscle activity with an electromyographi
sensor embedded into clothing [4], stress with an electodermal sensor incorporated into a wristband [5], and physical activity or sleep patterns via an
accelerometer in a watch [6,7]. In addition, a female’s most fertile period can be identified with detailed body temperature tracking [8], while levels of me
attention can be monitored with a small number of non-gelled electroencephalogram (EEG) electrodes [9]. Levels of social interaction (also known to a
PLOS Medicine 2016](https://image.slidesharecdn.com/digitalhealth-190427130553/75/slide-225-2048.jpg)
![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
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
R E S E A R C H A R T I C L E
onNovember3,2016http://stm.sciencemag.org/Downloadedfrom](https://image.slidesharecdn.com/digitalhealth-190427130553/75/slide-228-2048.jpg)
![puted as new data became avail
when his or her score crossed t
dation set, the AUC obtained f
0.81 to 0.85) (Fig. 2). At a spec
of 0.33], TREWScore achieved a s
a median of 28.2 hours (IQR, 10
Identification of patients b
A critical event in the developme
related organ dysfunction (seve
been shown to increase after th
more than two-thirds (68.8%) o
were identified before any sepsi
tients were identified a median
(Fig. 3B).
Comparison of TREWScore
Weevaluatedtheperformanceof
methods for the purpose of provid
use of TREWScore. We first com
to MEWS, a general metric used
of catastrophic deterioration (17
oped for tracking sepsis, MEWS
tion of patients at risk for severe
Fig. 2. ROC for detection of septic shock before onset in the validation
set. The ROC curve for TREWScore is shown in blue, with the ROC curve for
MEWS in red. The sensitivity and specificity performance of the routine
screening criteria is indicated by the purple dot. Normal 95% CIs are shown
for TREWScore and MEWS. TPR, true-positive rate; FPR, false-positive rate.
R E S E A R C H A R T I C L E
A targeted real-time early warning score (TREWScore)
for septic shock
AUC=0.83
At a specificity of 0.67, TREWScore achieved a sensitivity of 0.85
and identified patients a median of 28.2 hours before onset.](https://image.slidesharecdn.com/digitalhealth-190427130553/75/slide-230-2048.jpg)
![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
byguestonJune28,2018http://jaha.ahajournals.org/Downloadedfrom](https://image.slidesharecdn.com/digitalhealth-190427130553/75/slide-237-2048.jpg)
Uploaded byYoon Sup Choi
의료의 미래, 디지털 헬스케어
The document summarizes the future of healthcare and digital healthcare. It introduces Professor Yoon Sup Choi, the director of the Digital Healthcare Institute at Sungkyunkwan University. It discusses how artificial intelligence is reshaping the conservative medical system and how quickly AI is developing and influencing healthcare. The convergence of information technology, biotechnology, and medicine is creating innovation that will transform medical education and clinical practice.
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의료의 미래, 디지털 헬스케어
- 1. 의료의 미래, 디지털헬스케어 Professor, SAHIST, Sungkyunkwan University Director, Digital Healthcare Institute Yoon Sup Choi, Ph.D.
- 2. Disclaimer 저는 위의 회사들과지분 관계, 자문 등으로 이해 관계가 있음을 밝힙니다. 스타트업 벤처캐피털
- 3. “It's in Apple'sDNA that technology alone is not enough. It's technology married with liberal arts.”
- 4. The Convergence ofIT, BT and Medicine
- 6. 최윤섭 지음 의료인공지능 표지디자인•최승협 컴퓨터 털 헬 치를만드는 것을 화두로 기업가, 엔젤투자가, 에반 의 대표적인 전문가로, 활 이 분야를 처음 소개한 장 포항공과대학교에서 컴 동 대학원 시스템생명공 취득하였다. 스탠퍼드대 조교수, KT 종합기술원 컨 구원 연구조교수 등을 거 저널에 10여 편의 논문을 국내 최초로 디지털 헬스 윤섭 디지털 헬스케어 연 국내 유일의 헬스케어 스 어 파트너스’의 공동 창업 스타트업을 의료 전문가 관대학교 디지털헬스학과 뷰노, 직토, 3billion, 서지 소울링, 메디히어, 모바일 자문을 맡아 한국에서도 고 있다. 국내 최초의 디 케어 이노베이션』에 활발 을 연재하고 있다. 저서로 와 『그렇게 나는 스스로 •블로그_ http://www •페이스북_ https://w •이메일_ yoonsup.c 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 최윤섭지음 의료인공지능 값 20,000원 ISBN 979-11-86269-99-2 최초의 책! 계 안팎에서 제기 고 있다. 현재 의 분 커버했다고 자 것인가, 어느 진료 제하고 효용과 안 누가 지는가, 의학 쉬운 언어로 깊이 들이 의료 인공지 적인 용어를 최대 서 다른 곳에서 접 를 접하게 될 것 너무나 빨리 발전 책에서 제시하는 술을 공부하며, 앞 란다. 의사 면허를 취득 저가 도움되면 좋 를 불러일으킬 것 화를 일으킬 수도 슈에 제대로 대응 분은 의학 교육의 예비 의사들은 샌 지능과 함께하는 레이닝 방식도 이 전에 진료실과 수 겠지만, 여러분들 도생하는 수밖에 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 레이닝 방식도 이 전에 진료실과 수 겠지만, 여러분들 도생하는 수밖에 소울링, 메디히어, 모바일 자문을 맡아 한국에서도 고 있다. 국내 최초의 디 케어 이노베이션』에 활발 을 연재하고 있다. 저서로 와 『그렇게 나는 스스로 •블로그_ http://www •페이스북_ https://w •이메일_ yoonsup.c
- 8. Inevitable Tsunami ofChange
- 9.
- 10. Vinod Khosla Founder, 1stCEO of Sun Microsystems Partner of KPCB, CEO of KhoslaVentures LegendaryVenture Capitalist in SiliconValley
- 11. “Technology will replace80% of doctors”
- 12. https://www.youtube.com/watch?time_continue=70&v=2HMPRXstSvQ “영상의학과 전문의를 양성하는것을 당장 그만둬야 한다. 5년 안에 딥러닝이 영상의학과 전문의를 능가할 것은 자명하다.” Hinton on Radiology
- 13. https://rockhealth.com/reports/2018-year-end-funding-report-is-digital-health-in-a-bubble/ •2018년에는 $8.1B 가투자되며 역대 최대 규모를 또 한 번 갱신 (전년 대비 42.% 증가) •총 368개의 딜 (전년 359 대비 소폭 증가): 개별 딜의 규모가 커졌음 •전체 딜의 절반이 seed 혹은 series A 투자였음 •‘초기 기업들이 역대 최고로 큰 규모의 투자를’, ‘역대 가장 자주’ 받고 있음
- 14. 2010 2011 20122013 2014 2015 2016 2017 2018 Q1 Q2 Q3 Q4 153 283 476 647 608 568 684 851 765 FUNDING SNAPSHOT: YEAR OVER YEAR 5 Deal Count $1.4B $1.7B $1.7B $627M $603M$459M $8.2B $6.2B $7.1B $2.9B $2.3B$2.0B $1.2B $11.7B $2.3B Funding surpassed 2017 numbers by almost $3B, making 2018 the fourth consecutive increase in capital investment and largest since we began tracking digital health funding in 2010. Deal volume decreased from Q3 to Q4, but deal sizes spiked, with $3B invested in Q4 alone. Average deal size in 2018 was $21M, a $6M increase from 2017. $3.0B $14.6B DEALS & FUNDING INVESTORS SEGMENT DETAIL Source: StartUp Health Insights | startuphealth.com/insights Note: Report based on public data through 12/31/18 on seed (incl. accelerator), venture, corporate venture, and private equity funding only. © 2019 StartUp Health LLC •글로벌 투자 추이를 보더라도, 2018년 역대 최대 규모: $14.6B •2015년 이후 4년 연속 증가 중 https://hq.startuphealth.com/posts/startup-healths-2018-insights-funding-report-a-record-year-for-digital-health
- 15.
- 16. 5% 8% 24% 27% 36% Life Science &Health Mobile Enterprise & Data Consumer Commerce 9% 13% 23% 24% 31% Life Science & Health Consumer Enterprise Data & AI Others 2014 2015 Investment of GoogleVentures in 2014-2015
- 17. startuphealth.com/reports Firm 2017 YTDDeals Stage Early Mid Late 1 7 1 7 2 6 2 6 3 5 3 5 3 5 3 5 THE TOP INVESTORS OF 2017 YTD We are seeing huge strides in new investors pouring money into the digital health market, however all the top 10 investors of 2017 year to date are either maintaining or increasing their investment activity. Source: StartUp Health Insights | startuphealth.com/insights Note: Report based on public data on seed, venture, corporate venture and private equity funding only. © 2017 StartUp Health LLC DEALS & FUNDING GEOGRAPHY INVESTORSMOONSHOTS 20 •Google Ventures와 Khosla Ventures가 각각 7개로 공동 1위, •GE Ventures와 Accel Partners가 6건으로 공동 2위를 기록 •GV 가 투자한 기업 •virtual fitness membership network를 만드는 뉴욕의 ClassPass •Remote clinical trial 회사인 Science 37 •Digital specialty prescribing platform ZappRx 등에 투자. •Khosla Ventures 가 투자한 기업 •single-molecule 검사 장비를 만드는 TwoPoreGuys •Mabu라는 AI-powered patient engagement robot 을 만드는 Catalia Health에 투자.
- 18. •최근 3년 동안Merck, J&J, GSK 등의 제약사들의 디지털 헬스케어 분야 투자 급증 •2015-2016년 총 22건의 deal (=2010-2014년의 5년간 투자 건수와 동일) •Merck 가 가장 활발: 2009년부터 Global Health Innovation Fund 를 통해 24건 투자 ($5-7M) •GSK 의 경우 2014년부터 6건 (via VC arm, SR One): including Propeller Health
- 20. 헬스케어넓은 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 개인 유전정보분석 예) 암유전체, 질병위험도, 보인자, 약물 민감도 예) 웰니스, 조상 분석 헬스케어 관련 분야 구성도(ver 0.3) 의료 질병 예방, 치료, 처방, 관리 등 전문 의료 영역 원격의료 원격진료
- 21. EDITORIAL OPEN Digital medicine,on its way to being just plain medicine npj Digital Medicine (2018)1:20175 ; doi:10.1038/ s41746-017-0005-1 There are already nearly 30,000 peer-reviewed English-language scientific journals, producing an estimated 2.5 million articles a year.1 So why another, and why one focused specifically on digital medicine? To answer that question, we need to begin by defining what “digital medicine” means: using digital tools to upgrade the practice of medicine to one that is high-definition and far more individualized. It encompasses our ability to digitize human beings using biosensors that track our complex physiologic systems, but also the means to process the vast data generated via algorithms, cloud computing, and artificial intelligence. It has the potential to democratize medicine, with smartphones as the hub, enabling each individual to generate their own real world data and being far more engaged with their health. Add to this new imaging tools, mobile device laboratory capabilities, end-to-end digital clinical trials, telemedicine, and one can see there is a remarkable array of transformative technology which lays the groundwork for a new form of healthcare. As is obvious by its definition, the far-reaching scope of digital medicine straddles many and widely varied expertise. Computer scientists, healthcare providers, engineers, behavioral scientists, ethicists, clinical researchers, and epidemiologists are just some of the backgrounds necessary to move the field forward. But to truly accelerate the development of digital medicine solutions in health requires the collaborative and thoughtful interaction between individuals from several, if not most of these specialties. That is the primary goal of npj Digital Medicine: to serve as a cross-cutting resource for everyone interested in this area, fostering collabora- tions and accelerating its advancement. Current systems of healthcare face multiple insurmountable challenges. Patients are not receiving the kind of care they want and need, caregivers are dissatisfied with their role, and in most countries, especially the United States, the cost of care is unsustainable. We are confident that the development of new systems of care that take full advantage of the many capabilities that digital innovations bring can address all of these major issues. Researchers too, can take advantage of these leading-edge technologies as they enable clinical research to break free of the confines of the academic medical center and be brought into the real world of participants’ lives. The continuous capture of multiple interconnected streams of data will allow for a much deeper refinement of our understanding and definition of most pheno- types, with the discovery of novel signals in these enormous data sets made possible only through the use of machine learning. Our enthusiasm for the future of digital medicine is tempered by the recognition that presently too much of the publicized work in this field is characterized by irrational exuberance and excessive hype. Many technologies have yet to be formally studied in a clinical setting, and for those that have, too many began and ended with an under-powered pilot program. In addition, there are more than a few examples of digital “snake oil” with substantial uptake prior to their eventual discrediting.2 Both of these practices are barriers to advancing the field of digital medicine. Our vision for npj Digital Medicine is to provide a reliable, evidence-based forum for all clinicians, researchers, and even patients, curious about how digital technologies can transform every aspect of health management and care. Being open source, as all medical research should be, allows for the broadest possible dissemination, which we will strongly encourage, including through advocating for the publication of preprints And finally, quite paradoxically, we hope that npj Digital Medicine is so successful that in the coming years there will no longer be a need for this journal, or any journal specifically focused on digital medicine. Because if we are able to meet our primary goal of accelerating the advancement of digital medicine, then soon, we will just be calling it medicine. And there are already several excellent journals for that. ACKNOWLEDGEMENTS Supported by the National Institutes of Health (NIH)/National Center for Advancing Translational Sciences grant UL1TR001114 and a grant from the Qualcomm Foundation. ADDITIONAL INFORMATION Competing interests:The authors declare no competing financial interests. Publisher's note:Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Change history:The original version of this Article had an incorrect Article number of 5 and an incorrect Publication year of 2017. These errors have now been corrected in the PDF and HTML versions of the Article. Steven R. Steinhubl1 and Eric J. Topol1 1 Scripps Translational Science Institute, 3344 North Torrey Pines Court, Suite 300, La Jolla, CA 92037, USA Correspondence: Steven R. Steinhubl (steinhub@scripps.edu) or Eric J. Topol (etopol@scripps.edu) REFERENCES 1. Ware, M. & Mabe, M. The STM report: an overview of scientific and scholarly journal publishing 2015 [updated March]. http://digitalcommons.unl.edu/scholcom/92017 (2015). 2. Plante, T. B., Urrea, B. & MacFarlane, Z. T. et al. Validation of the instant blood pressure smartphone App. JAMA Intern. Med. 176, 700–702 (2016). Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons. org/licenses/by/4.0/. © The Author(s) 2018 Received: 19 October 2017 Accepted: 25 October 2017 www.nature.com/npjdigitalmed Published in partnership with the Scripps Translational Science Institute 디지털 의료의 미래는? 일상적인 의료가 되는 것
- 22. What is mostimportant factor in digital medicine?
- 23. “Data! Data! Data!”he cried.“I can’t make bricks without clay!” - Sherlock Holmes,“The Adventure of the Copper Beeches”
- 25. 새로운 데이터가 새로운 방식으로 새로운주체에 의해 측정, 저장, 통합, 분석된다. 데이터의 종류 데이터의 질적/양적 측면 웨어러블 기기 스마트폰 유전 정보 분석 인공지능 SNS 사용자/환자 대중
- 26. 디지털 헬스케어의 3단계 •Step1. 데이터의 측정 •Step 2. 데이터의 통합 •Step 3. 데이터의 분석
- 27. Digital Healthcare IndustryLandscape Data Measurement Data Integration Data Interpretation Treatment Smartphone Gadget/Apps DNA Artificial Intelligence 2nd Opinion Wearables / IoT (ver. 3) EMR/EHR 3D Printer Counseling Data Platform Accelerator/early-VC Telemedicine Device On Demand (O2O) VR Digital Healthcare Institute Diretor, Yoon Sup Choi, Ph.D. yoonsup.choi@gmail.com
- 28. Data Measurement DataIntegration Data Interpretation Treatment Smartphone Gadget/Apps DNA Artificial Intelligence 2nd Opinion Device On Demand (O2O) Wearables / IoT Digital Healthcare Institute Diretor, Yoon Sup Choi, Ph.D. yoonsup.choi@gmail.com EMR/EHR 3D Printer Counseling Data Platform Accelerator/early-VC VR Telemedicine Digital Healthcare Industry Landscape (ver. 3)
- 29.
- 30. Smartphone: the originof healthcare innovation
- 31. Smartphone: the originof healthcare innovation
- 32. 2013? The election ofPope Benedict The Election of Pope Francis
- 33. The Election ofPope Francis The Election of Pope Benedict
- 34.
- 37. Sci Transl Med2015
- 38. 검이경 더마토스코프 안과질환피부암 기생충 호흡기 심전도 수면 식단 활동량 발열 생리/임신
- 39.
- 40.
- 41.
- 42. “왼쪽 귀에 대한비디오를 보면 고막 뒤 에 액체가 보인다. 고막은 특별히 부어 있 거나 모양이 이상하지는 않다. 그러므로 심 한 염증이 있어보이지는 않는다. 네가 스쿠버 다이빙 하면서 압력평형에 어 려움을 느꼈다는 것을 감안한다면, 고막의 움직임을 테스트 할 수 있는 의사에게 직 접 진찰 받는 것도 좋겠다. ...” 한국에서는 불법한국에서는 불법
- 43.
- 45. AliveCor Heart Monitor(Kardia)
- 46. AliveCor Heart Monitor(Kardia)
- 49. “심장박동은 안정적이기 때문에, 당장 병원에 갈 필요는 없겠습니다. 그래도 이상이 있으면 전문의에게 진료를 받아보세요. “ 한국에서는 불법한국에서는 불법
- 52.
- 54. 30분-1시간 정도 일상적인코골이가 있음 이걸 어떻게 믿나?
- 55. 녹음을 해줌. PGS와의analytical validity의 증명?
- 56.
- 59. http://www.rolls-royce.com/about/our-technology/enabling-technologies/engine-health-management.aspx#sense 250 sensors tomonitor the “health” of the GE turbines
- 60. Fig 1. Whatcan consumer wearables do? Heart rate can be measured with an oximeter built into a ring [3], muscle activity with an electromyographi sensor embedded into clothing [4], stress with an electodermal sensor incorporated into a wristband [5], and physical activity or sleep patterns via an accelerometer in a watch [6,7]. In addition, a female’s most fertile period can be identified with detailed body temperature tracking [8], while levels of me attention can be monitored with a small number of non-gelled electroencephalogram (EEG) electrodes [9]. Levels of social interaction (also known to a PLOS Medicine 2016
- 61. Hype or Hope? Source:Gartner
- 62.
- 64.
- 65. https://clinicaltrials.gov/ct2/results?term=fitbit&Search=Search •의료기기가 아님에도 Fitbit은 이미 임상 연구에 폭넓게 사용되고 있음 •Fitbit 이 장려하지 않았음에도, 임상 연구자들이 자발적으로 사용 •Fitbit 을 이용한 임상 연구 수는 계속 증가하는 추세 (16.3(80), 16.8(113), 17.7(173))
- 67. •Fitbit이 임상연구에 활용되는것은 크게 두 가지 경우 •Fitbit 자체가 intervention이 되어서 활동량이나 치료 효과를 증진시킬 수 있는지 여부 •연구 참여자의 활동량을 모니터링 하기 위한 수단 •1. Fitbit으로 환자의 활동량을 증가시키기 위한 연구들 •Fitbit이 소아 비만 환자의 활동량을 증가시키는지 여부를 연구 •Fitbit이 위소매절제술을 받은 환자들의 활동량을 증가시키는지 여부 •Fitbit이 젊은 낭성 섬유증 (cystic fibrosis) 환자의 활동량을 증가시키는지 여부 •Fitbit이 암 환자의 신체 활동량을 증가시키기 위한 동기부여가 되는지 여부 •2. Fitbit으로 임상 연구에 참여하는 환자의 활동량을 모니터링 •항암 치료를 받은 환자들의 건강과 예후를 평가하는데 fitbit을 사용 •현금이 자녀/부모의 활동량을 증가시키는지 파악하기 위해 fitbit을 사용 •Brain tumor 환자의 삶의 질 측정을 위해 다른 survey 결과와 함께 fitbit을 사용 •말초동맥 질환(Peripheral Artery Disease) 환자의 활동량을 평가하기 위해
- 70. Cardiogram •실리콘밸리의 Cardiogram 은애플워치로 측정한 심박수 데이터를 바탕으로 서비스 •2016년 10월 Andressen Horowitz 에서 $2m의 투자 유치
- 71. https://blog.cardiogr.am/what-do-normal-and-abnormal-heart-rhythms-look-like-on-apple-watch-7b33b4a8ecfa •Cardiogram은 심박수에 운동,수면, 감정, 의료적인 상태가 반영된다고 주장 •특히, 심박 데이터를 기반으로 심방세동(atrial fibrillation)과 심방 조동(atrial flutter)의 detection 시도 Cardiogram
- 72. •Cardiogram은 심박 데이터만으로심방세동을 detection할 수 있다고 주장 •“Irregularly irregular” •high absolute variability (a range of 30+ bpm) •a higher fraction missing measurements •a lack of periodicity in heart rate variability •심방세동 특유의 불규칙적인 리듬을 detection 하는 정도로 생각하면 될 듯 •“불규칙적인 리듬을 가지는 (심방세동이 아닌) 다른 부정맥과 구분 가능한가?” (쉽지 않을듯) •따라서, 심박으로 detection한 환자를 심전도(ECG)로 confirm 하는 것이 필요 Cardiogram for A.Fib
- 73. Passive Detection ofAtrial Fibrillation Using a Commercially Available Smartwatch Geoffrey H. Tison, MD, MPH; José M. Sanchez, MD; Brandon Ballinger, BS; Avesh Singh, MS; Jeffrey E. Olgin, MD; Mark J. Pletcher, MD, MPH; Eric Vittinghoff, PhD; Emily S. Lee, BA; Shannon M. Fan, BA; Rachel A. Gladstone, BA; Carlos Mikell, BS; Nimit Sohoni, BS; Johnson Hsieh, MS; Gregory M. Marcus, MD, MAS IMPORTANCE Atrial fibrillation (AF) affects 34 million people worldwide and is a leading cause of stroke. A readily accessible means to continuously monitor for AF could prevent large numbers of strokes and death. OBJECTIVE To develop and validate a deep neural network to detect AF using smartwatch data. DESIGN, SETTING, AND PARTICIPANTS In this multinational cardiovascular remote cohort study coordinated at the University of California, San Francisco, smartwatches were used to obtain heart rate and step count data for algorithm development. A total of 9750 participants enrolled in the Health eHeart Study and 51 patients undergoing cardioversion at the University of California, San Francisco, were enrolled between February 2016 and March 2017. A deep neural network was trained using a method called heuristic pretraining in which the network approximated representations of the R-R interval (ie, time between heartbeats) without manual labeling of training data. Validation was performed against the reference standard 12-lead electrocardiography (ECG) in a separate cohort of patients undergoing cardioversion. A second exploratory validation was performed using smartwatch data from ambulatory individuals against the reference standard of self-reported history of persistent AF. Data were analyzed from March 2017 to September 2017. MAIN OUTCOMES AND MEASURES The sensitivity, specificity, and receiver operating characteristic C statistic for the algorithm to detect AF were generated based on the reference standard of 12-lead ECG–diagnosed AF. RESULTS Of the 9750 participants enrolled in the remote cohort, including 347 participants with AF, 6143 (63.0%) were male, and the mean (SD) age was 42 (12) years. There were more than 139 million heart rate measurements on which the deep neural network was trained. The deep neural network exhibited a C statistic of 0.97 (95% CI, 0.94-1.00; P < .001) to detect AF against the reference standard 12-lead ECG–diagnosed AF in the external validation cohort of 51 patients undergoing cardioversion; sensitivity was 98.0% and specificity was 90.2%. In an exploratory analysis relying on self-report of persistent AF in ambulatory participants, the C statistic was 0.72 (95% CI, 0.64-0.78); sensitivity was 67.7% and specificity was 67.6%. CONCLUSIONS AND RELEVANCE This proof-of-concept study found that smartwatch photoplethysmography coupled with a deep neural network can passively detect AF but with some loss of sensitivity and specificity against a criterion-standard ECG. Further studies will help identify the optimal role for smartwatch-guided rhythm assessment. JAMA Cardiol. doi:10.1001/jamacardio.2018.0136 Published online March 21, 2018. Editorial Supplemental content and Audio Author Affiliations: Division of Cardiology, Department of Medicine, University of California, San Francisco (Tison, Sanchez, Olgin, Lee, Fan, Gladstone, Mikell, Marcus); Cardiogram Incorporated, San Francisco, California (Ballinger, Singh, Sohoni, Hsieh); Department of Epidemiology and Biostatistics, University of California, San Francisco (Pletcher, Vittinghoff). Corresponding Author: Gregory M. Marcus, MD, MAS, Division of Cardiology, Department of Medicine, University of California, San Francisco, 505 Parnassus Ave, M1180B, San Francisco, CA 94143- 0124 (marcusg@medicine.ucsf.edu). Research JAMA Cardiology | Original Investigation (Reprinted) E1 © 2018 American Medical Association. All rights reserved.
- 74. Passive Detection ofAtrial Fibrillation Using a Commercially Available Smartwatch Geoffrey H. Tison, MD, MPH; José M. Sanchez, MD; Brandon Ballinger, BS; Avesh Singh, MS; Jeffrey E. Olgin, MD; Mark J. Pletcher, MD, MPH; Eric Vittinghoff, PhD; Emily S. Lee, BA; Shannon M. Fan, BA; Rachel A. Gladstone, BA; Carlos Mikell, BS; Nimit Sohoni, BS; Johnson Hsieh, MS; Gregory M. Marcus, MD, MAS IMPORTANCE Atrial fibrillation (AF) affects 34 million people worldwide and is a leading cause of stroke. A readily accessible means to continuously monitor for AF could prevent large numbers of strokes and death. OBJECTIVE To develop and validate a deep neural network to detect AF using smartwatch data. DESIGN, SETTING, AND PARTICIPANTS In this multinational cardiovascular remote cohort study coordinated at the University of California, San Francisco, smartwatches were used to obtain heart rate and step count data for algorithm development. A total of 9750 participants enrolled in the Health eHeart Study and 51 patients undergoing cardioversion at the University of California, San Francisco, were enrolled between February 2016 and March 2017. A deep neural network was trained using a method called heuristic pretraining in which the network approximated representations of the R-R interval (ie, time between heartbeats) without manual labeling of training data. Validation was performed against the reference standard 12-lead electrocardiography (ECG) in a separate cohort of patients undergoing cardioversion. A second exploratory validation was performed using smartwatch data from ambulatory individuals against the reference standard of self-reported history of persistent AF. Data were analyzed from March 2017 to September 2017. MAIN OUTCOMES AND MEASURES The sensitivity, specificity, and receiver operating characteristic C statistic for the algorithm to detect AF were generated based on the reference standard of 12-lead ECG–diagnosed AF. RESULTS Of the 9750 participants enrolled in the remote cohort, including 347 participants with AF, 6143 (63.0%) were male, and the mean (SD) age was 42 (12) years. There were more than 139 million heart rate measurements on which the deep neural network was trained. The deep neural network exhibited a C statistic of 0.97 (95% CI, 0.94-1.00; P < .001) to detect AF against the reference standard 12-lead ECG–diagnosed AF in the external validation cohort of 51 patients undergoing cardioversion; sensitivity was 98.0% and specificity was 90.2%. In an exploratory analysis relying on self-report of persistent AF in ambulatory participants, the C statistic was 0.72 (95% CI, 0.64-0.78); sensitivity was 67.7% and specificity was 67.6%. CONCLUSIONS AND RELEVANCE This proof-of-concept study found that smartwatch photoplethysmography coupled with a deep neural network can passively detect AF but with some loss of sensitivity and specificity against a criterion-standard ECG. Further studies will help identify the optimal role for smartwatch-guided rhythm assessment. JAMA Cardiol. doi:10.1001/jamacardio.2018.0136 Published online March 21, 2018. Editorial Supplemental content and Audio Author Affiliations: Division of Cardiology, Department of Medicine, University of California, San Francisco (Tison, Sanchez, Olgin, Lee, Fan, Gladstone, Mikell, Marcus); Cardiogram Incorporated, San Francisco, California (Ballinger, Singh, Sohoni, Hsieh); Department of Epidemiology and Biostatistics, University of California, San Francisco (Pletcher, Vittinghoff). Corresponding Author: Gregory M. Marcus, MD, MAS, Division of Cardiology, Department of Medicine, University of California, San Francisco, 505 Parnassus Ave, M1180B, San Francisco, CA 94143- 0124 (marcusg@medicine.ucsf.edu). Research JAMA Cardiology | Original Investigation (Reprinted) E1 © 2018 American Medical Association. All rights reserved. tion from the participant (dependent on user adherence) and by the episodic nature of data obtained. A Samsung Simband (Samsung) exhibited high sensitivity and specificity for AF de- 32 costs associated with the care of those patients, the potential reduction in stroke could ultimately provide cost savings. SeveralfactorsmakedetectionofAFfromambulatorydata Figure 2. Accuracy of Detecting Atrial Fibrillation in the Cardioversion Cohort 100 80 60 40 20 0 0 10080 Sensitivity,% 1 –Specificity, % 604020 Cardioversion cohortA 100 80 60 40 20 0 0 10080 Sensitivity,% 1 –Specificity, % 604020 Ambulatory subset of remote cohortB A, Receiver operating characteristic curve among 51 individuals undergoing in-hospital cardioversion. The curve demonstrates a C statistic of 0.97 (95% CI, 0.94-1.00), and the point on the curve indicates a sensitivity of 98.0% and a specificity of 90.2%. B, Receiver operating characteristic curve among 1617 individuals in the ambulatory subset of the remote cohort. The curve demonstrates a C statistic of 0.72 (95% CI, 0.64-0.78), and the point on the curve indicates a sensitivity of 67.7% and a specificity of 67.6%. Table 3. Performance Characteristics of Deep Neural Network in Validation Cohortsa Cohort % AUCSensitivity Specificity PPV NPV Cardioversion cohort (sedentary) 98.0 90.2 90.9 97.8 0.97 Subset of remote cohort (ambulatory) 67.7 67.6 7.9 98.1 0.72 Abbreviations: AUC, area under the receiver operating characteristic curve; NPV, negative predictive value; PPV, positive predictive value. a In the cardioversion cohort, the atrial fibrillation reference standard was 12-lead electrocardiography diagnosis; in the remote cohort, the atrial fibrillation reference standard was limited to self-reported history of persistent atrial fibrillation. Research Original Investigation Passive Detection of Atrial Fibrillation Using a Commercially Available Smartwatch AUC=0.98 AUC=0.72 • In external validation using standard 12-lead ECG, algorithm performance achieved a C statistic of 0.97. • The passive detection of AF from free-living smartwatch data has substantial clinical implications. • Importantly, the accuracy of detecting self-reported AF in an ambulatory setting was more modest (C statistic of 0.72)
- 75. 애플워치4: 심전도, 부정맥,낙상 측정 FDA 의료기기 인허가 •De Novo 의료기기로 인허가 받음 (새로운 종류의 의료기기) •9월에 발표하였으나, 부정맥 관련 기능은 12월에 활성화 •미국 애플워치에서만 가능하고, 한국은안 됨 (미국에서 구매한 경우, 한국 앱스토어 ID로 가능)
- 79. • 애플워치4 부정맥(심방세동) 측정 기능 • ‘진단’이나 기존 환자의 ‘관리’ 목적이 아니라, • ‘측정’ 목적 • 기존에 진단 받지 않은 환자 중에, • 심방세동이 있는 사람을 확인하여 병원으로 연결 • 정확성을 정말 철저하게 검증했는가? • 애플워치에 의해서 측정된 심방세동의 20% 정도가 • 패치 형태의 ECG 모니터에서 측정되지 않음 • 즉, false alarm 이 많을 수 있음 • 불필요한 병원 방문, 검사, 의료 비용 발생 등을 우려하고 있음
- 80. https://www.scripps.edu/science-and-medicine/translational-institute/about/news/oran-ecg-app/index.html?fbclid=IwAR02Z8SG679-svCkyxBhv3S1JUOSFQlI6UCvNu3wvUgyRmc1r2ft963MFmM • 애플워치4의 심방세동측정 기능의 ‘위험성’ 경고 • 일반인을 대상의 측정에서 false positive의 위험 • (실제로는 심방세동 없는데, 있는 것으로 잘못 나온 케이스) • False positive가 많은 PSA 검사와 비교하여 설명 • 특히, 애플워치는 PSA와 달리 장기적인 정확성 데이터조차 없음 • 의료기기 인허가를 받기는 했으나, • 애플워치4가 얼마나 정확한지는 아무도 모름..
- 81. •American College ofCardiology’s 68th Annual Scientific Session •전체 임상 참여자 중에서 irregular pusle notification 받은 사람은 불과 0.5% •애플워치와 ECG patch를 동시에 사용한 결과 71%의 positive predictive value. •irregular pusle notification 받은 사람 중 84%가 그 시점에 심방세동을 가짐 •f/u으로 그 다음 일주일 동안 ECG patch를 착용한 사람 중 34%가 심방세동을 발견 •Irregular pusle notification 받은 사람 중에 실제로 병원에 간 사람은 57% (전체 환자군의 0.3%)
- 82.
- 85. Ingestible Sensor, ProteusDigital Health
- 86. Ingestible Sensor, ProteusDigital Health
- 88.
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- 92. 2003 Human GenomeProject 13 years (676 weeks) $2,700,000,000 2007 Dr. CraigVenter’s genome 4 years (208 weeks) $100,000,000 2008 Dr. James Watson’s genome 4 months (16 weeks) $1,000,000 2009 (Nature Biotechnology) 4 weeks $48,000 2013 1-2 weeks ~$5,000
- 93. The $1000 Genomeis Already Here!
- 94. • 2017년 1월NovaSeq 5000, 6000 발표 • 몇년 내로 $100로 WES 를 실현하겠다고 공언 • 2일에 60명의 WES 가능 (한 명당 한 시간 이하)
- 96. Results within 6-8weeksA little spit is all it takes! DTC Genetic TestingDirect-To-Consumer
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- 101. Traits 음주 후 얼굴이붉어지는가 쓴 맛을 감지할 수 있나 귀지 유형 눈 색깔 곱슬머리 여부 유당 분해 능력 말라리아 저항성 대머리가 될 가능성 근육 퍼포먼스 혈액형 노로바이러스 저항성 HIV 저항성 흡연 중독 가능성
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- 104. 23andMe Chronicle $115m 펀딩 (유니콘등극) 100만 명 돌파 2006 23andMe 창업 20162007 2012 2013 2014 2015 구글 벤처스 360만 달러 투자 2008 $99 로 가격 인하 FDA 판매 중지 명령 영국에서 DTC 서비스 시작 FDA 블룸증후군 DTC 서비스 허가 FDA에 블룸증후군 테스트 승인 요청 FDA에 510(k) 제출 FDA 510(k) 철회 보인자 등 DTC 서비스 재개 ($199) 캐나다에서 DTC 서비스 시작 Genetech, pFizer가 23andMe 데이터 구입 자체 신약 개 발 계획 발표 120만 명 돌파 $399 로 가격 인하Business Regulation 애플 리서치키트와 데이터 수집 협력 50만 명 돌파 30만 명 돌파 TV 광고 시작 2017 FDA의 질병위험도 검사 DTC 서비스 허가 + 관련 규제 면제 프로세스 확립 Digital Healthcare Institute Director,Yoon Sup Choi, PhD yoonsup.choi@gmail.com FDA Pre-Cert FDA Gottlieb 국장, 질병 위험도 유전자 DTC 서비스의 Pre-Cert 발의 BRCA 1/2 DTC 검사 허용 2018 FDA, 질병 위험도 유전자 DTC서비스의 Pre-Cert 발효 200만 명 돌파 500만 명 돌파 GSK에서 $300M 투자 유치 2019 1000만 명 돌파
- 105. •개별 제품이 아닌제조사 기반의 규제를 유전자 DTC 검사에도 적용하는 방안 •Gottlieb 국장: •“23andMe의 규제 과정을 거치면서 FDA도 많이 배웠다” •질병 위험도 DTC 검사를 '한 번' 인허가 받은 회사의 후속 검사는 규제 면제 추진 •한국의 유전자 DTC 규제 방식과의 괴리는 더욱 커질 전망
- 106. •질병 위험도 유전자분석 DTC 서비스에 대해서 Pre-Cert 를 적용 시작 (18. 6. 5) •최초 한 번"만 99% 이상의 analytical validity 를 증명하면, •이 회사는 정확한 유전 정보 분석 서비스를 만들 수 있는 것으로 인정하여, •이후의 서비스는 출시 전 인허가가 면제 •다만 민감할 수 있는 4가지 종류의 분석에 대해서는 이 규제 완화에서 제외 •산전 진단 •(예방적 스크리닝이나 치료법 결정으로 이어지는) 암 발병 가능성 검사 •약물 유전체 검사 •우성유전질환 유전인자 검사
- 107. 한국 DTC 유전정보분석 제한적 허용 (2016.6.30) • 「비의료기관 직접 유전자검사 실시 허용 관련 고시 제정, 6.30일시행」 • 2015년 12월「생명윤리 및 안전에 관한 법률」개정(‘15.12.29개정, ’16.6.30시행) 과 제9차 무역투자진흥회의(’16.2월) 시 발표한 규제 개선의 후속조치 일환으로 추진 • 민간 유전자검사 업체에서는 혈당, 혈압, 피부노화, 체질량지수 등 12개 검사항목과 관련된 46개 유전자를 직접 검사 가능 http://www.mohw.go.kr/m/noticeView.jsp?MENU_ID=0403&cont_seq=333112&page=1 검사항목 (유전자수) 유전자명 1 체질량지수(3) FTO, MC4R, BDNF 2 중성지방농도(8) GCKR, DOCK7, ANGPTL3, BAZ1B, TBL2, MLXIPL, LOC105375745, TRIB1 3 콜레스테롤(8) CELSR2, SORT1, HMGCR, ABO, ABCA1, MYL2, LIPG, CETP 4 혈 당(8) CDKN2A/B, G6PC2, GCK, GCKR, GLIS3, MTNR1B, DGKB-TMEM195, SLC30A8 5 혈 압(8) NPR3, ATP2B1, NT5C2, CSK, HECTD4, GUCY1A3, CYP17A1, FGF5 6 색소 침착(2) OCA2, MC1R 7 탈 모(3) chr20p11(rs1160312, rs2180439), IL2RA, HLA-DQB1 8 모발 굵기(1) EDAR 9 피부 노화(1) AGER 10 피부 탄력(1) MMP1 11 비타민C농도(1) SLC23A1(SVCT1) 12 카페인대사(2) AHR, CYP1A1-CYP1A2
- 108. https://www.23andme.com/slideshow/research/ 고객의 자발적인 참여에의한 유전학 연구 깍지를 끼면 어느 쪽 엄지가 위로 오는가? 아침형 인간? 저녁형 인간? 빛에 노출되었을 때 재채기를 하는가? 근육의 퍼포먼스 쓴 맛 인식 능력 음주 후 얼굴이 붉어지나? 유당 분해 효소 결핍? 고객의 81%가 10개 이상의 질문에 자발적 답변 매주 1 million 개의 data point 축적 The More Data, The Higher Accuracy!
- 109. January 13, 2015January6, 2015 Data Business
- 110. •신약 표적 발굴:더 안전하고 효과적으로 •표적 치료에 효능을 보일 환자군의 선별에 도움 •임상시험 환자 리크루팅에 활용 •GSK의 파킨슨 신약: LRRK2 variant 환자군 •LRRK2 variant: 파킨슨 환자 100명 당 1명 보유 •23andMe는 이미 LRRK2 variant 250명 보유 GSK에 독점적 DB 접근권을 주고, $300m의 투자 유치
- 111.
- 112. Digital Phenotype: Your smartphoneknows if you are depressed Ginger.io
- 113. Digital Phenotype: Your smartphoneknows if you are depressed J Med Internet Res. 2015 Jul 15;17(7):e175. The correlation analysis between the features and the PHQ-9 scores revealed that 6 of the 10 features were significantly correlated to the scores: • strong correlation: circadian movement, normalized entropy, location variance • correlation: phone usage features, usage duration and usage frequency
- 114. the manifestations ofdisease by providing a more comprehensive and nuanced view of the experience of illness. Through the lens of the digital phenotype, an individual’s interaction The digital phenotype Sachin H Jain, Brian W Powers, Jared B Hawkins & John S Brownstein In the coming years, patient phenotypes captured to enhance health and wellness will extend to human interactions with digital technology. In 1982, the evolutionary biologist Richard Dawkins introduced the concept of the “extended phenotype”1, the idea that pheno- types should not be limited just to biological processes, such as protein biosynthesis or tissue growth, but extended to include all effects that a gene has on its environment inside or outside ofthebodyoftheindividualorganism.Dawkins stressed that many delineations of phenotypes are arbitrary. Animals and humans can modify their environments, and these modifications andassociatedbehaviorsareexpressionsofone’s genome and, thus, part of their extended phe- notype. In the animal kingdom, he cites damn buildingbybeaversasanexampleofthebeaver’s extended phenotype1. Aspersonaltechnologybecomesincreasingly embedded in human lives, we think there is an important extension of Dawkins’s theory—the notion of a ‘digital phenotype’. Can aspects of ourinterfacewithtechnologybesomehowdiag- nosticand/orprognosticforcertainconditions? Can one’s clinical data be linked and analyzed together with online activity and behavior data to create a unified, nuanced view of human dis- ease?Here,wedescribetheconceptofthedigital phenotype. Although several disparate studies have touched on this notion, the framework for medicine has yet to be described. We attempt to define digital phenotype and further describe the opportunities and challenges in incorporat- ing these data into healthcare. Jan. 2013 0.000 0.002 0.004 Density 0.006 July 2013 Jan. 2014 July 2014 User 1 User 2 User 3 User 4 User 5 User 6 User 7 Date Figure 1 Timeline of insomnia-related tweets from representative individuals. Density distributions (probability density functions) are shown for seven individual users over a two-year period. Density on the y axis highlights periods of relative activity for each user. A representative tweet from each user is shown as an example. npg©2015NatureAmerica,Inc.Allrightsreserved. http://www.nature.com/nbt/journal/v33/n5/full/nbt.3223.html
- 115. ers, Jared BHawkins & John S Brownstein phenotypes captured to enhance health and wellness will extend to human interactions with st Richard pt of the hat pheno- biological sis or tissue effects that or outside m.Dawkins phenotypes can modify difications onsofone’s ended phe- cites damn hebeaver’s ncreasingly there is an heory—the aspects of ehowdiag- Jan. 2013 0.000 0.002 0.004 Density 0.006 July 2013 Jan. 2014 July 2014 User 1 User 2 User 3 User 4 User 5 User 6 User 7 Date Figure 1 Timeline of insomnia-related tweets from representative individuals. Density distributions (probability density functions) are shown for seven individual users over a two-year period. Density on the y axis highlights periods of relative activity for each user. A representative tweet from each user is Your twitter knows if you cannot sleep Timeline of insomnia-related tweets from representative individuals. Nat. Biotech. 2015
- 116. Reece & Danforth,“Instagram photos reveal predictive markers of depression” (2016) higher Hue (bluer) lower Saturation (grayer) lower Brightness (darker)
- 117. Rao (MVR) (24) . Results Both Alldata and Prediagnosis models were decisively superior to a null model . Alldata predictors were significant with 99% probability.57.5;(KAll =1 K 49.8) Pre = 1 7 Prediagnosis and Alldata confidence levels were largely identical, with two exceptions: Prediagnosis Brightness decreased to 90% confidence, and Prediagnosis posting frequency dropped to 30% confidence, suggesting a null predictive value in the latter case. Increased hue, along with decreased brightness and saturation, predicted depression. This means that photos posted by depressed individuals tended to be bluer, darker, and grayer (see Fig. 2). The more comments Instagram posts received, the more likely they were posted by depressed participants, but the opposite was true for likes received. In the Alldata model, higher posting frequency was also associated with depression. Depressed participants were more likely to post photos with faces, but had a lower average face count per photograph than healthy participants. Finally, depressed participants were less likely to apply Instagram filters to their posted photos. Fig. 2. Magnitude and direction of regression coefficients in Alldata (N=24,713) and Prediagnosis (N=18,513) models. Xaxis values represent the adjustment in odds of an observation belonging to depressed individuals, per Reece & Danforth, “Instagram photos reveal predictive markers of depression” (2016) Fig. 1. Comparison of HSV values. Right photograph has higher Hue (bluer), lower Saturation (grayer), and lower Brightness (darker) than left photograph. Instagram photos posted by depressed individuals had HSV values shifted towards those in the right photograph, compared with photos posted by healthy individuals. Units of observation In determining the best time span for this analysis, we encountered a difficult question: When and for how long does depression occur? A diagnosis of depression does not indicate the persistence of a depressive state for every moment of every day, and to conduct analysis using an individual’s entire posting history as a single unit of observation is therefore rather specious. At the other extreme, to take each individual photograph as units of observation runs the risk of being too granular. DeChoudhury et al. (5) looked at all of a given user’s posts in a single day, and aggregated those data into perperson, perday units of observation. We adopted this precedent of “userdays” as a unit of analysis . 5 Statistical framework We used Bayesian logistic regression with uninformative priors to determine the strength of individual predictors. Two separate models were trained. The Alldata model used all collected data to address Hypothesis 1. The Prediagnosis model used all data collected from higher Hue (bluer) lower Saturation (grayer) lower Brightness (darker) Digital Phenotype: Your Instagram knows if you are depressed
- 118. Digital Phenotype: Your Instagramknows if you are depressed Reece & Danforth, “Instagram photos reveal predictive markers of depression” (2016) . In particular, depressedχ2 07.84, p .17e 64;( All = 9 = 9 − 1 13.80, p .87e 44)χ2Pre = 8 = 2 − 1 participants were less likely than healthy participants to use any filters at all. When depressed participants did employ filters, they most disproportionately favored the “Inkwell” filter, which converts color photographs to blackandwhite images. Conversely, healthy participants most disproportionately favored the Valencia filter, which lightens the tint of photos. Examples of filtered photographs are provided in SI Appendix VIII. Fig. 3. Instagram filter usage among depressed and healthy participants. Bars indicate difference between observed and expected usage frequencies, based on a Chisquared analysis of independence. Blue bars indicate disproportionate use of a filter by depressed compared to healthy participants, orange bars indicate the reverse.
- 119. Digital Phenotype: Your Instagramknows if you are depressed Reece & Danforth, “Instagram photos reveal predictive markers of depression” (2016) VIII. Instagram filter examples Fig. S8. Examples of Inkwell and Valencia Instagram filters. Inkwell converts color photos to blackandwhite, Valencia lightens tint. Depressed participants most favored Inkwell compared to healthy participants, Healthy participants
- 120. Mindstrong Health • 스마트폰사용 패턴을 바탕으로 • 인지능력, 우울증, 조현병, 양극성 장애, PTSD 등을 측정 • 미국 국립정신건강연구소 소장인 Tomas Insel 이 공동 설립 • 아마존의 제프 베조스 투자
- 121. BRIEF COMMUNICATION OPEN Digitalbiomarkers 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 Published in partnership with the Scripps Translational Science Institute • 총 45가지 스마트폰 사용 패턴: 타이핑, 스크롤, 화면 터치 • 스페이스바 누른 후, 다음 문자 타이핑하는 행동 • 백스페이스를 누른 후, 그 다음 백스페이스 • 주소록에서 사람을 찾는 행동 양식 • 스마트폰 사용 패턴과 인지 능력의 상관 관계 • 20-30대 피험자 27명 • Working Memory, Language, Dexterity etc
- 122. BRIEF COMMUNICATION OPEN Digitalbiomarkers 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 Published in partnership with the Scripps Translational Science Institute Fig. 1 A blue square represents a participant test Z-score normed to the 27 participant scores and a red circle represents the digital biomarker prediction Z-score normed to the 27 predictions. Test scores and predictions shown are 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 Digital biomarkers of cognitive function P Dagum 2 1234567890():,; • 스마트폰 사용 패턴과 인지 능력의 높은 상관 관계 • 파란색: 표준 인지 능력 테스트 결과 • 붉은색: 마인드 스트롱의 스마트폰 사용 패턴
- 123. Step1. 데이터의 측정 •스마트폰 •웨어러블디바이스 •개인 유전 정보 분석 •디지털 표현형 환자 유래의 의료 데이터 (PGHD)
- 124.
- 126. Sci Transl Med2015
- 128.
- 129.
- 130. Epic MyChart EpicEHR Dexcom CGM Patients/User Devices EH Hospit Whitings + Apple Watch Apps HealthKit
- 133.
- 134. Hospital A HospitalB Hospital C interoperability
- 135.
- 136. •2018년 1월에 출시당시, 존스홉킨스, UC샌디에고 등 12개의 병원에 연동 •(2019년 2월 현재) 1년 만에 200개 이상의 병원에 연동 •VA와도 연동된다고 밝힘 (with 9 million veterans) •2008년 구글 헬스는 3년 동안 12개 병원에 연동에 그쳤음
- 137.
- 139.
- 140. How to Analyzeand Interpret the Big Data?
- 141. and/or Two ways toget insights from the big data
- 142. 원격의료 • 명시적으로 ‘금지’된곳은 한국 밖에 없는 듯 • 해외에서는 새로운 서비스의 상당수가 원격의료 기능 포함 • 글로벌 100대 헬스케어 서비스 중 39개가 원격의료 포함 • 다른 모델과 결합하여 갈수록 새로운 모델이 만들어지는 중 • 스마트폰, 웨어러블, IoT, 인공지능, 챗봇 등과 결합 • 10년 뒤 한국 의료에서는?
- 143. 원격 의료 원격 진료 원격환자 모니터링 화상 진료 전화 진료 2차 소견 용어 정리 데이터 판독 원격 수술
- 144. •원격 진료: 화상진료 •원격 진료: 2차 소견 •원격 진료: 애플리케이션 •원격 환자 모니터링 원격 의료에도 종류가 많다.
- 145. •원격 진료: 화상진료 •원격 진료: 2차 소견 •원격 진료: 애플리케이션 •원격 환자 모니터링 원격 의료에도 종류가 많다.
- 146.
- 150. Average Time toAppointment (Familiy Medicine) Boston LA Portland Miami Atlanta Denver Detroit New York Seattle Houston Philadelphia Washington DC San Diego Dallas Minneapolis Total 0 30 60 90 120 20.3 10 8 24 30 9 17 8 24 14 14 9 7 8 59 63 19.5 10 5 7 14 21 19 23 26 16 16 24 12 13 20 66 29.3 days 8 days 12 days 13 days 17 days 17 days 21 days 26 days 26 days 27 days 27 days 27 days 28 days 39 days 42 days 109 days 2017 2014 2009
- 154. 0 125 250 375 500 2013 2014 20152016 2017 2018 417.9 233.3 123 77.4 44 20 0 550 1100 1650 2200 2013 2014 2015 2016 2017 2018 2,036 1,461 952 575 299 127 0 6 12 18 24 2013 2014 2015 2016 2017 2018 22.8 19.6 17.5 11.5 8.1 6.2 Revenue ($m) Visits (k) Members (m) Growth of Teladoc
- 155. •원격 진료: 화상진료 •원격 진료: 2차 소견 •원격 진료: 애플리케이션 •원격 환자 모니터링 원격 의료에도 종류가 많다.
- 156.
- 157.
- 158.
- 159. “왼쪽 귀에 대한비디오를 보면 고막 뒤 에 액체가 보인다. 고막은 특별히 부어 있 거나 모양이 이상하지는 않다. 그러므로 심 한 염증이 있어보이지는 않는다. 네가 스쿠버 다이빙 하면서 압력평형에 어 려움을 느꼈다는 것을 감안한다면, 고막의 움직임을 테스트 할 수 있는 의사에게 직 접 진찰 받는 것도 좋겠다. ...” 한국에서는 불법한국에서는 불법
- 160. AliveCor Heart Monitor(Kardia)
- 162. “심장박동은 안정적이기 때문에, 당장 병원에 갈 필요는 없겠습니다. 그래도 이상이 있으면 전문의에게 진료를 받아보세요. “ 한국에서는 불법한국에서는 불법
- 163.
- 164. •원격 진료: 화상진료 •원격 진료: 2차 소견 •원격 진료: 애플리케이션 •원격 환자 모니터링 원격 의료에도 종류가 많다.
- 165. Epic MyChart EpicEHR Dexcom CGM Patients/User Devices EHR Hospital Whitings + Apple Watch Apps HealthKit
- 166. transfer from Share2to HealthKit as mandated by Dexcom receiver Food and Drug Administration device classification. Once the glucose values reach HealthKit, they are passively shared with the Epic MyChart app (https://www.epic.com/software-phr.php). The MyChart patient portal is a component of the Epic EHR and uses the same data- base, and the CGM values populate a standard glucose flowsheet in the patient’s chart. This connection is initially established when a pro- vider places an order in a patient’s electronic chart, resulting in a re- quest to the patient within the MyChart app. Once the patient or patient proxy (parent) accepts this connection request on the mobile device, a communication bridge is established between HealthKit and MyChart enabling population of CGM data as frequently as every 5 Participation required confirmation of Bluetooth pairing of the CGM re- ceiver to a mobile device, updating the mobile device with the most recent version of the operating system, Dexcom Share2 app, Epic MyChart app, and confirming or establishing a username and password for all accounts, including a parent’s/adolescent’s Epic MyChart account. Setup time aver- aged 45–60 minutes in addition to the scheduled clinic visit. During this time, there was specific verbal and written notification to the patients/par- ents that the diabetes healthcare team would not be actively monitoring or have real-time access to CGM data, which was out of scope for this pi- lot. The patients/parents were advised that they should continue to contact the diabetes care team by established means for any urgent questions/ concerns. Additionally, patients/parents were advised to maintain updates Figure 1: Overview of the CGM data communication bridge architecture. BRIEFCOMMUNICATION Kumar R B, et al. J Am Med Inform Assoc 2016;0:1–6. doi:10.1093/jamia/ocv206, Brief Communication byguestonApril7,2016http://jamia.oxfordjournals.org/Downloadedfrom •Apple HealthKit, Dexcom CGM기기를 통해 지속적으로 혈당을 모니터링한 데이터를 EHR과 통합 •당뇨환자의 혈당관리를 향상시켰다는 연구결과 •Stanford Children’s Health와 Stanford 의대에서 10명 type 1 당뇨 소아환자 대상으로 수행 (288 readings /day) •EHR 기반 데이터분석과 시각화는 데이터 리뷰 및 환자커뮤니케이션을 향상 •환자가 내원하여 진료하는 기존 방식에 비해 실시간 혈당변화에 환자가 대응 JAMIA 2016 Remote Patients Monitoring via Dexcom-HealthKit-Epic-Stanford
- 167. 의료계 일각에서는 원격환자 모니터링의 합법화를 요구하기도
- 169. No choice butto bring AI into the medicine
- 170. Martin Duggan,“IBM WatsonHealth - Integrated Care & the Evolution to Cognitive Computing”
- 171. •복잡한 의료 데이터의분석 및 insight 도출 •영상 의료/병리 데이터의 분석/판독 •연속 데이터의 모니터링 및 예방/예측 의료 인공지능의 세 유형
- 172. •복잡한 의료 데이터의분석 및 insight 도출 •영상 의료/병리 데이터의 분석/판독 •연속 데이터의 모니터링 및 예방/예측 의료 인공지능의 세 유형
- 173. Jeopardy! 2011년 인간 챔피언두 명 과 퀴즈 대결을 벌여서 압도적인 우승을 차지
- 176. 메이요 클리닉 협력 (임상시험 매칭) 전남대병원 도입 인도 마니팔 병원 WFO 도입 식약처 인공지능 가이드라인 초안 메드트로닉과 혈당관리 앱 시연 2011 2012 2013 2014 2015 뉴욕 MSK암센터 협력 (폐암) MD앤더슨 협력 (백혈병) MD앤더슨 파일럿 결과 발표 @ASCO 왓슨 펀드, 웰톡에 투자 뉴욕게놈센터 협력 (교모세포종 분석) GeneMD, 왓슨 모바일 디벨로퍼 챌린지 우승 클리블랜드 클리닉 협력 (암 유전체 분석) 한국 IBM 왓슨 사업부 신설 Watson Health 출범 피텔, 익스플로리스 인수 J&J, 애플, 메드트로닉 협력 에픽 시스템즈, 메이요클리닉 제휴 (EHR 분석) 동경대 도입 ( WFO) 왓슨 펀드, 모더나이징 메디슨 투자 학계/의료계 산업계 패쓰웨이 지노믹스 OME 클로즈드 알파 서비스 시작 트루븐 헬스 인수 애플 리서치 키트 통한 수면 연구 시작 2017 가천대 길병원 도입 메드트로닉 Sugar.IQ 출시 제약사 테바와 제휴 태국 범룽랏 국제 병원, WFO 도입 머지 헬스케어 인수 2016 언더 아머 제휴 브로드 연구소 협력 발표 (유전체 분석-항암제 내 성) 마니팔 병원의 WFO 정확성 발표 대구가톨릭병원 대구동산병원 도입 부산대병원 도입 왓슨 펀드, 패쓰웨이 지노믹스 투자 제퍼디! 우승 조선대병원 도입 한국 왓슨 컨소시움 출범 쥬피터 메디컬 센터 도입 식약처 인공지능 가이드라인 메이요 클리닉 임상시험매칭 결과발표 2018 건양대병원 도입 IBM Watson Health Chronicle WFO 최초 논문
- 177. 메이요 클리닉 협력 (임상시험 매칭) 전남대병원 도입 인도 마니팔 병원 WFO 도입 식약처 인공지능 가이드라인 초안 메드트로닉과 혈당관리 앱 시연 2011 2012 2013 2014 2015 뉴욕 MSK암센터 협력 (폐암) MD앤더슨 협력 (백혈병) MD앤더슨 파일럿 결과 발표 @ASCO 왓슨 펀드, 웰톡에 투자 뉴욕게놈센터 협력 (교모세포종 분석) GeneMD, 왓슨 모바일 디벨로퍼 챌린지 우승 클리블랜드 클리닉 협력 (암 유전체 분석) 한국 IBM 왓슨 사업부 신설 Watson Health 출범 피텔, 익스플로리스 인수 J&J, 애플, 메드트로닉 협력 에픽 시스템즈, 메이요클리닉 제휴 (EHR 분석) 동경대 도입 ( WFO) 왓슨 펀드, 모더나이징 메디슨 투자 학계/의료계 산업계 패쓰웨이 지노믹스 OME 클로즈드 알파 서비스 시작 트루븐 헬스 인수 애플 리서치 키트 통한 수면 연구 시작 2017 가천대 길병원 도입 메드트로닉 Sugar.IQ 출시 제약사 테바와 제휴 태국 범룽랏 국제 병원, WFO 도입 머지 헬스케어 인수 2016 언더 아머 제휴 브로드 연구소 협력 발표 (유전체 분석-항암제 내 성) 마니팔 병원의 WFO 정확성 발표 부산대병원 도입 왓슨 펀드, 패쓰웨이 지노믹스 투자 제퍼디! 우승 조선대병원 도입 한국 왓슨 컨소시움 출범 쥬피터 메디컬 센터 도입 식약처 인공지능 가이드라인 메이요 클리닉 임상시험매칭 결과발표 2018 건양대병원 도입 IBM Watson Health Chronicle WFO 최초 논문 대구가톨릭병원 대구동산병원 도입
- 178. Annals of Oncology(2016) 27 (suppl_9): ix179-ix180. 10.1093/annonc/mdw601 Validation study to assess performance of IBM cognitive computing system Watson for oncology with Manipal multidisciplinary tumour board for 1000 consecutive cases: An Indian experience •인도 마니팔 병원의 1,000명의 암환자 에 대해 의사와 WFO의 권고안의 ‘일치율’을 비교 •유방암 638명, 대장암 126명, 직장암 124명, 폐암 112명 •의사-왓슨 일치율 •추천(50%), 고려(28%), 비추천(17%) •의사의 진료안 중 5%는 왓슨의 권고안으로 제시되지 않음 •일치율이 암의 종류마다 달랐음 •직장암(85%), 폐암(17.8%) •삼중음성 유방암(67.9%), HER2 음성 유방암 (35%)
- 179. WFO in ASCO2017 •가천대 길병원의 대장암과 위암 환자에 왓슨 적용 결과 • 대장암 환자(stage II-IV) 340명 • 진행성 위암 환자 185명 (Retrospective) • 의사와의 일치율 • 대장암 환자: 73% • 보조 (adjuvant) 항암치료를 받은 250명: 85% • 전이성 환자 90명: 40% • 위암 환자: 49% • Trastzumab/FOLFOX 가 국민 건강 보험 수가를 받지 못함 • S-1(tegafur, gimeracil and oteracil)+cisplatin): • 국내는 매우 루틴; 미국에서는 X
- 180. 잠정적 결론 •왓슨 포온콜로지와 의사의 일치율: •암종별로 다르다. •같은 암종에서도 병기별로 다르다. •같은 암종에 대해서도 병원별/국가별로 다르다. •시간이 흐름에 따라 달라질 가능성이 있다.
- 181. 원칙이 필요하다 •어떤 환자의경우, 왓슨에게 의견을 물을 것인가? •왓슨을 (암종별로) 얼마나 신뢰할 것인가? •왓슨의 의견을 환자에게 공개할 것인가? •왓슨과 의료진의 판단이 다른 경우 어떻게 할 것인가? •왓슨에게 보험 급여를 매길 수 있는가? 이러한 기준에 따라 의료의 질/치료효과가 달라질 수 있으나, 현재 개별 병원이 개별적인 기준으로 활용하게 됨
- 182. ARTICLE OPEN Scalable andaccurate deep learning with electronic health records Alvin Rajkomar 1,2 , Eyal Oren1 , Kai Chen1 , Andrew M. Dai1 , Nissan Hajaj1 , Michaela Hardt1 , Peter J. Liu1 , Xiaobing Liu1 , Jake Marcus1 , Mimi Sun1 , Patrik Sundberg1 , Hector Yee1 , Kun Zhang1 , Yi Zhang1 , Gerardo Flores1 , Gavin E. Duggan1 , Jamie Irvine1 , Quoc Le1 , Kurt Litsch1 , Alexander Mossin1 , Justin Tansuwan1 , De Wang1 , James Wexler1 , Jimbo Wilson1 , Dana Ludwig2 , Samuel L. Volchenboum3 , Katherine Chou1 , Michael Pearson1 , Srinivasan Madabushi1 , Nigam H. Shah4 , Atul J. Butte2 , Michael D. Howell1 , Claire Cui1 , Greg S. Corrado1 and Jeffrey Dean1 Predictive modeling with electronic health record (EHR) data is anticipated to drive personalized medicine and improve healthcare quality. Constructing predictive statistical models typically requires extraction of curated predictor variables from normalized EHR data, a labor-intensive process that discards the vast majority of information in each patient’s record. We propose a representation of patients’ entire raw EHR records based on the Fast Healthcare Interoperability Resources (FHIR) format. We demonstrate that deep learning methods using this representation are capable of accurately predicting multiple medical events from multiple centers without site-specific data harmonization. We validated our approach using de-identified EHR data from two US academic medical centers with 216,221 adult patients hospitalized for at least 24 h. In the sequential format we propose, this volume of EHR data unrolled into a total of 46,864,534,945 data points, including clinical notes. Deep learning models achieved high accuracy for tasks such as predicting: in-hospital mortality (area under the receiver operator curve [AUROC] across sites 0.93–0.94), 30-day unplanned readmission (AUROC 0.75–0.76), prolonged length of stay (AUROC 0.85–0.86), and all of a patient’s final discharge diagnoses (frequency-weighted AUROC 0.90). These models outperformed traditional, clinically-used predictive models in all cases. We believe that this approach can be used to create accurate and scalable predictions for a variety of clinical scenarios. In a case study of a particular prediction, we demonstrate that neural networks can be used to identify relevant information from the patient’s chart. npj Digital Medicine (2018)1:18 ; doi:10.1038/s41746-018-0029-1 INTRODUCTION The promise of digital medicine stems in part from the hope that, by digitizing health data, we might more easily leverage computer information systems to understand and improve care. In fact, routinely collected patient healthcare data are now approaching the genomic scale in volume and complexity.1 Unfortunately, most of this information is not yet used in the sorts of predictive statistical models clinicians might use to improve care delivery. It is widely suspected that use of such efforts, if successful, could provide major benefits not only for patient safety and quality but also in reducing healthcare costs.2–6 In spite of the richness and potential of available data, scaling the development of predictive models is difficult because, for traditional predictive modeling techniques, each outcome to be predicted requires the creation of a custom dataset with specific variables.7 It is widely held that 80% of the effort in an analytic model is preprocessing, merging, customizing, and cleaning datasets,8,9 not analyzing them for insights. This profoundly limits the scalability of predictive models. Another challenge is that the number of potential predictor variables in the electronic health record (EHR) may easily number in the thousands, particularly if free-text notes from doctors, nurses, and other providers are included. Traditional modeling approaches have dealt with this complexity simply by choosing a very limited number of commonly collected variables to consider.7 This is problematic because the resulting models may produce imprecise predictions: false-positive predictions can overwhelm physicians, nurses, and other providers with false alarms and concomitant alert fatigue,10 which the Joint Commission identified as a national patient safety priority in 2014.11 False-negative predictions can miss significant numbers of clinically important events, leading to poor clinical outcomes.11,12 Incorporating the entire EHR, including clinicians’ free-text notes, offers some hope of overcoming these shortcomings but is unwieldy for most predictive modeling techniques. Recent developments in deep learning and artificial neural networks may allow us to address many of these challenges and unlock the information in the EHR. Deep learning emerged as the preferred machine learning approach in machine perception problems ranging from computer vision to speech recognition, but has more recently proven useful in natural language processing, sequence prediction, and mixed modality data settings.13–17 These systems are known for their ability to handle large volumes of relatively messy data, including errors in labels Received: 26 January 2018 Revised: 14 March 2018 Accepted: 26 March 2018 1 Google Inc, Mountain View, CA, USA; 2 University of California, San Francisco, San Francisco, CA, USA; 3 University of Chicago Medicine, Chicago, IL, USA and 4 Stanford University, Stanford, CA, USA Correspondence: Alvin Rajkomar (alvinrajkomar@google.com) These authors contributed equally: Alvin Rajkomar, Eyal Oren www.nature.com/npjdigitalmed Published in partnership with the Scripps Translational Science Institute •2018년 1월 구글이 전자의무기록(EMR)을 분석하여, 환자 치료 결과를 예측하는 인공지능 발표 •환자가 입원 중에 사망할 것인지 •장기간 입원할 것인지 •퇴원 후에 30일 내에 재입원할 것인지 •퇴원 시의 진단명 •이번 연구의 특징: 확장성 •과거 다른 연구와 달리 EMR의 일부 데이터를 pre-processing 하지 않고, •전체 EMR 를 통채로 모두 분석하였음: UCSF, UCM (시카고 대학병원) •특히, 비정형 데이터인 의사의 진료 노트도 분석 Nat Digi Med 2018
- 183. Nat Digi Med2018 clinically-used predictive models. Because we were inte understanding whether deep learning could scale to valid predictions across divergent healthcare domains, w single data structure to make predictions for an importan outcome (death), a standard measure of quality of ca missions), a measure of resource utilization (length of sta measure of understanding of a patient’s problems (diagn Second, using the entirety of a patient’s chart fo prediction does more than promote scalability, it expos data with which to make an accurate prediction. For pr made at discharge, our deep learning models consider than 46 billion pieces of EHR data and achieved more predictions, earlier in the hospital stay, than did tr models. To the best of our knowledge, our models outperform EHR models in the medical literature for predicting (0.92–0.94 vs 0.91),42 unexpected readmission (0.75– 0.69),43 and increased length of stay (0.85–0.86 vs 0.77). comparisons to other studies are difficult45 because of underlying study designs,23,46–57 incomplete definitions o and outcomes,58,59 restrictions on disease-specific cohort use of data unavailable in real-time.63,65,66 Theref implemented baselines based on the HOSPITAL score,67 score, and Liu’s model44 on our data, and demonstrat better performance. We are not aware of a study that pr many ICD codes as this study, but our micro-F1 score exce shown on the smaller MIMIC-III dataset when predictin diagnoses (0.40 vs 0.28).68 The clinical impact of this impr is suggested, for example, by the improvement of numbe to evaluate for inpatient mortality: the deep learning mod fire half the number of alerts of a traditional predictive resulting in many fewer false positives. However, the novelty of the approach does not lie s token is considered as a potential predictor by the deep learning model. The line within the boxplot represents the median, represents the interquartile range (IQR), and the whiskers are 1.5 times the IQR. The number of tokens increased steadily from adm discharge. At discharge, the median number of tokens for Hospital A was 86,477 and for Hospital B was 122,961 Table 2. Prediction accuracy of each task made at different time points Hospital A Hospital B Inpatient mortality, AUROCa (95% CI) 24 h before admission 0.87 (0.85–0.89) 0.81 (0.79–0.83) At admission 0.90 (0.88–0.92) 0.90 (0.86–0.91) 24 h after admission 0.95 (0.94–0.96) 0.93 (0.92–0.94) Baseline (aEWSb ) at 24 h after admission 0.85 (0.81–0.89) 0.86 (0.83–0.88) 30-day readmission, AUROC (95% CI) At admission 0.73 (0.71–0.74) 0.72 (0.71–0.73) At 24 h after admission 0.74 (0.72–0.75) 0.73 (0.72–0.74) At discharge 0.77 (0.75–0.78) 0.76 (0.75–0.77) Baseline (mHOSPITALc ) at discharge 0.70 (0.68–0.72) 0.68 (0.67–0.69) Length of stay at least 7 days, AUROC (95% CI) At admission 0.81 (0.80–0.82) 0.80 (0.80–0.81) At 24 h after admission 0.86 (0.86–0.87) 0.85 (0.85–0.86) Baseline (Liud ) at 24 h after admission 0.76 (0.75–0.77) 0.74 (0.73–0.75) Discharge diagnoses (weighted AUROC) At admission 0.87 0.86 At 24 h after admission 0.89 0.88 At discharge 0.90 0.90 a Area under the receiver operator curve b Augmented Early Warning System score c Modified HOSPITAL score for readmission d Modified Liu score for long length of stay •2018년 1월 구글이 전자의무기록(EMR)을 분석하여, 환자 치료 결과를 예측하는 인공지능 발표 •환자가 입원 중에 사망할 것인지 •장기간 입원할 것인지 •퇴원 후에 30일 내에 재입원할 것인지 •퇴원 시의 진단명 •이번 연구의 특징: 확장성 •과거 다른 연구와 달리 EMR의 일부 데이터를 pre-processing 하지 않고, •전체 EMR 를 통채로 모두 분석하였음: UCSF, UCM (시카고 대학병원) •특히, 비정형 데이터인 의사의 진료 노트도 분석
- 184. •복잡한 의료 데이터의분석 및 insight 도출 •영상 의료/병리 데이터의 분석/판독 •연속 데이터의 모니터링 및 예방/예측 의료 인공지능의 세 유형
- 185.
- 186. 인공지능 기계학습 딥러닝 전문가 시스템 사이버네틱스 … 인공신경망 결정트리 서포트 벡터머신 … 컨볼루션 신경망 (CNN) 순환신경망(RNN) … 인공지능과 딥러닝의 관계
- 188. REVIEW ARTICLE |FOCUS https://doi.org/10.1038/s41591-018-0300-7 Department of Molecular Medicine, Scripps Research, La Jolla, CA, USA. e-mail: etopol@scripps.edu M edicine is at the crossroad of two major trends. The first is a failed business model, with increasing expenditures and jobs allocated to healthcare, but with deteriorating key outcomes, including reduced life expectancy and high infant, child- hood, and maternal mortality in the United States1,2 . This exem- plifies a paradox that is not at all confined to American medicine: investment of more human capital with worse human health out- comes. The second is the generation of data in massive quantities, from sources such as high-resolution medical imaging, biosensors with continuous output of physiologic metrics, genome sequenc- ing, and electronic medical records. The limits on analysis of such data by humans alone have clearly been exceeded, necessitating an increased reliance on machines. Accordingly, at the same time that there is more dependence than ever on humans to provide healthcare, algorithms are desperately needed to help. Yet the inte- gration of human and artificial intelligence (AI) for medicine has barely begun. Looking deeper, there are notable, longstanding deficiencies in healthcare that are responsible for its path of diminishing returns. These include a large number of serious diagnostic errors, mis- takes in treatment, an enormous waste of resources, inefficiencies in workflow, inequities, and inadequate time between patients and clinicians3,4 . Eager for improvement, leaders in healthcare and com- puter scientists have asserted that AI might have a role in address- ing all of these problems. That might eventually be the case, but researchers are at the starting gate in the use of neural networks to ameliorate the ills of the practice of medicine. In this Review, I have gathered much of the existing base of evidence for the use of AI in medicine, laying out the opportunities and pitfalls. Artificial intelligence for clinicians Almost every type of clinician, ranging from specialty doctor to paramedic, will be using AI technology, and in particular deep learning, in the future. This largely involved pattern recognition using deep neural networks (DNNs) (Box 1) that can help interpret medical scans, pathology slides, skin lesions, retinal images, electro- cardiograms, endoscopy, faces, and vital signs. The neural net inter- pretation is typically compared with physicians’ assessments using a plot of true-positive versus false-positive rates, known as a receiver operating characteristic (ROC), for which the area under the curve (AUC) is used to express the level of accuracy (Box 1). Radiology. One field that has attracted particular attention for application of AI is radiology5 . Chest X-rays are the most common type of medical scan, with more than 2 billion performed worldwide per year. In one study, the accuracy of one algorithm, based on a 121-layer convolutional neural network, in detecting pneumonia in over 112,000 labeled frontal chest X-ray images was compared with that of four radiologists, and the conclusion was that the algorithm outperformed the radiologists. However, the algorithm’s AUC of 0.76, although somewhat better than that for two previously tested DNN algorithms for chest X-ray interpretation5 , is far from optimal. In addition, the test used in this study is not necessarily comparable with the daily tasks of a radiologist, who will diagnose much more than pneumonia in any given scan. To further validate the conclu- sions of this study, a comparison with results from more than four radiologists should be made. A team at Google used an algorithm that analyzed the same image set as in the previously discussed study to make 14 different diagnoses, resulting in AUC scores that ranged from 0.63 for pneumonia to 0.87 for heart enlargement or a collapsed lung6 . More recently, in another related study, it was shown that a DNN that is currently in use in hospitals in India for interpretation of four different chest X-ray key findings was at least as accurate as four radiologists7 . For the narrower task of detecting cancerous pulmonary nodules on a chest X-ray, a DNN that retro- spectively assessed scans from over 34,000 patients achieved a level of accuracy exceeding 17 of 18 radiologists8 . It can be difficult for emergency room doctors to accurately diagnose wrist fractures, but a DNN led to marked improvement, increasing sensitivity from 81% to 92% and reducing misinterpretation by 47% (ref. 9 ). Similarly, DNNs have been applied across a wide variety of medical scans, including bone films for fractures and estimation of aging10–12 , classification of tuberculosis13 , and vertebral compression fractures14 ; computed tomography (CT) scans for lung nodules15 , liver masses16 , pancreatic cancer17 , and coronary calcium score18 ; brain scans for evidence of hemorrhage19 , head trauma20 , and acute referrals21 ; magnetic resonance imaging22 ; echocardiograms23,24 ; and mammographies25,26 . A unique imaging-recognition study focusing on the breadth of acute neurologic events, such as stroke or head trauma, was carried out on over 37,000 head CT 3-D scans, which the algorithm analyzed for 13 different anatomical find- ings versus gold-standard labels (annotated by expert radiologists) and achieved an AUC of 0.73 (ref. 27 ). A simulated prospective, double-blind, randomized control trial was conducted with real cases from the dataset and showed that the deep-learning algorithm could interpret scans 150 times faster than radiologists (1.2 versus 177seconds). But the conclusion that the algorithm’s diagnostic accuracyinscreeningacuteneurologicscanswaspoorerthanhuman High-performance medicine: the convergence of human and artificial intelligence Eric J. Topol The use of artificial intelligence, and the deep-learning subtype in particular, has been enabled by the use of labeled big data, along with markedly enhanced computing power and cloud storage, across all sectors. In medicine, this is beginning to have an impact at three levels: for clinicians, predominantly via rapid, accurate image interpretation; for health systems, by improving workflow and the potential for reducing medical errors; and for patients, by enabling them to process their own data to promote health. The current limitations, including bias, privacy and security, and lack of transparency, along with the future directions of these applications will be discussed in this article. Over time, marked improvements in accuracy, productivity, and workflow will likely be actualized, but whether that will be used to improve the patient–doctor relationship or facilitate its erosion remains to be seen. REVIEW ARTICLE | FOCUS https://doi.org/10.1038/s41591-018-0300-7 NATURE MEDICINE | VOL 25 | JANUARY 2019 | 44–56 | www.nature.com/naturemedicine44 an ed as tio rit da of al an (T m ap D an be la Table 1 | Peer-reviewed publications of AI algorithms compared with doctors Specialty Images Publication Radiology/ neurology CT head, acute neurological events Titano et al. 27 CT head for brain hemorrhage Arbabshirani et al.19 CT head for trauma Chilamkurthy et al.20 CXR for metastatic lung nodules Nam et al.8 CXR for multiple findings Singh et al.7 Mammography for breast density Lehman et al.26 Wrist X-ray* Lindsey et al.9 Pathology Breast cancer Ehteshami Bejnordi et al.41 Lung cancer (+driver mutation) Coudray et al.33 Brain tumors (+methylation) Capper et al.45 Breast cancer metastases* Steiner et al.35 Breast cancer metastases Liu et al.34 Dermatology Skin cancers Esteva et al.47 Melanoma Haenssle et al.48 Skin lesions Han et al.49 Ophthalmology Diabetic retinopathy Gulshan et al.51 Diabetic retinopathy* Abramoff et al.31 Diabetic retinopathy* Kanagasingam et al.32 Congenital cataracts Long et al.38 Retinal diseases (OCT) De Fauw et al.56 Macular degeneration Burlina et al.52 Retinopathy of prematurity Brown et al.60 AMD and diabetic retinopathy Kermany et al.53 Gastroenterology Polyps at colonoscopy* Mori et al.36 Polyps at colonoscopy Wang et al.37 Cardiology Echocardiography Madani et al.23 Echocardiography Zhang et al.24 T C A A iC Z B N ID Ic Im V A M A A
- 189.
- 190. •손 엑스레이 영상을판독하여 환자의 골연령 (뼈 나이)를 계산해주는 인공지능 • 기존에 의사는 그룰리히-파일(Greulich-Pyle)법 등으로 표준 사진과 엑스레이를 비교하여 판독 • 인공지능은 참조표준영상에서 성별/나이별 패턴을 찾아서 유사성을 확률로 표시 + 표준 영상 검색 •의사가 성조숙증이나 저성장을 진단하는데 도움을 줄 수 있음
- 191. - 1 - 보도 자 료 국내에서 개발한 인공지능(AI) 기반 의료기기 첫 허가 - 인공지능 기술 활용하여 뼈 나이 판독한다 - 식품의약품안전처 처장 류영진 는 국내 의료기기업체 주 뷰노가 개발한 인공지능 기술이 적용된 의료영상분석장치소프트웨어 뷰노메드 본에이지 를 월 일 허가했다고 밝혔습니다 이번에 허가된 뷰노메드 본에이지 는 인공지능 이 엑스레이 영상을 분석하여 환자의 뼈 나이를 제시하고 의사가 제시된 정보 등으로 성조숙증이나 저성장을 진단하는데 도움을 주는 소프트웨어입니다 그동안 의사가 환자의 왼쪽 손 엑스레이 영상을 참조표준영상 과 비교하면서 수동으로 뼈 나이를 판독하던 것을 자동화하여 판독시간을 단축하였습니다 이번 허가 제품은 년 월부터 빅데이터 및 인공지능 기술이 적용된 의료기기의 허가 심사 가이드라인 적용 대상으로 선정되어 임상시험 설계에서 허가까지 맞춤 지원하였습니다 뷰노메드 본에이지 는 환자 왼쪽 손 엑스레이 영상을 분석하여 의 료인이 환자 뼈 나이를 판단하는데 도움을 주기 위한 목적으로 허가되었습니다 - 2 - 분석은 인공지능이 촬영된 엑스레이 영상의 패턴을 인식하여 성별 남자 개 여자 개 로 분류된 뼈 나이 모델 참조표준영상에서 성별 나이별 패턴을 찾아 유사성을 확률로 표시하면 의사가 확률값 호르몬 수치 등의 정보를 종합하여 성조숙증이나 저성장을 진단합 니다 임상시험을 통해 제품 정확도 성능 를 평가한 결과 의사가 판단한 뼈 나이와 비교했을 때 평균 개월 차이가 있었으며 제조업체가 해당 제품 인공지능이 스스로 인지 학습할 수 있도록 영상자료를 주기적으로 업데이트하여 의사와의 오차를 좁혀나갈 수 있도록 설계되었습니다 인공지능 기반 의료기기 임상시험계획 승인건수는 이번에 허가받은 뷰노메드 본에이지 를 포함하여 현재까지 건입니다 임상시험이 승인된 인공지능 기반 의료기기는 자기공명영상으로 뇌경색 유형을 분류하는 소프트웨어 건 엑스레이 영상을 통해 폐결절 진단을 도와주는 소프트웨어 건 입니다 참고로 식약처는 인공지능 가상현실 프린팅 등 차 산업과 관련된 의료기기 신속한 개발을 지원하기 위하여 제품 연구 개발부터 임상시험 허가에 이르기까지 전 과정을 맞춤 지원하는 차세대 프로젝트 신개발 의료기기 허가도우미 등을 운영하고 있 습니다 식약처는 이번 제품 허가를 통해 개개인의 뼈 나이를 신속하게 분석 판정하는데 도움을 줄 수 있을 것이라며 앞으로도 첨단 의료기기 개발이 활성화될 수 있도록 적극적으로 지원해 나갈 것이라고 밝혔습니다
- 192. 저는 뷰노의 자문을맡고 있으며, 지분 관계가 있음을 밝힙니다
- 193. AJR:209, December 20171 Since 1992, concerns regarding interob- server variability in manual bone age esti- mation [4] have led to the establishment of several automatic computerized methods for bone age estimation, including computer-as- sisted skeletal age scores, computer-aided skeletal maturation assessment systems, and BoneXpert (Visiana) [5–14]. BoneXpert was developed according to traditional machine- learning techniques and has been shown to have a good performance for patients of var- ious ethnicities and in various clinical set- tings [10–14]. The deep-learning technique is an improvement in artificial neural net- works. Unlike traditional machine-learning techniques, deep-learning techniques allow an algorithm to program itself by learning from the images given a large dataset of la- beled examples, thus removing the need to specify rules [15]. Deep-learning techniques permit higher levels of abstraction and improved predic- tions from data. Deep-learning techniques Computerized Bone Age Estimation Using Deep Learning– Based Program: Evaluation of the Accuracy and Efficiency Jeong Rye Kim1 Woo Hyun Shim1 Hee Mang Yoon1 Sang Hyup Hong1 Jin Seong Lee1 Young Ah Cho1 Sangki Kim2 Kim JR, Shim WH, Yoon MH, et al. 1 Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, South Korea. Address correspondence to H. M. Yoon (espoirhm@gmail.com). 2 Vuno Research Center, Vuno Inc., Seoul, South Korea. Pediatric Imaging • Original Research Supplemental Data Available online at www.ajronline.org. AJR 2017; 209:1–7 0361–803X/17/2096–1 © American Roentgen Ray Society B one age estimation is crucial for developmental status determina- tions and ultimate height predic- tions in the pediatric population, particularly for patients with growth disor- ders and endocrine abnormalities [1]. Two major left-hand wrist radiograph-based methods for bone age estimation are current- ly used: the Greulich-Pyle [2] and Tanner- Whitehouse [3] methods. The former is much more frequently used in clinical practice. Greulich-Pyle–based bone age estimation is performed by comparing a patient’s left-hand radiograph to standard radiographs in the Greulich-Pyle atlas and is therefore simple and easily applied in clinical practice. How- ever, the process of bone age estimation, which comprises a simple comparison of multiple images, can be repetitive and time consuming and is thus sometimes burden- some to radiologists. Moreover, the accuracy depends on the radiologist’s experience and tends to be subjective. Keywords: bone age, children, deep learning, neural network model DOI:10.2214/AJR.17.18224 J. R. Kim and W. H. Shim contributed equally to this work. Received March 12, 2017; accepted after revision July 7, 2017. S. Kim is employed by Vuno, Inc., which created the deep learning–based automatic software system for bone age determination. J. R. Kim, W. H. Shim, H. M. Yoon, S. H. Hong, J. S. Lee, and Y. A. Cho are employed by Asan Medical Center, which holds patent rights for the deep learning–based automatic software system for bone age assessment. OBJECTIVE. The purpose of this study is to evaluate the accuracy and efficiency of a new automatic software system for bone age assessment and to validate its feasibility in clini- cal practice. MATERIALS AND METHODS. A Greulich-Pyle method–based deep-learning tech- nique was used to develop the automatic software system for bone age determination. Using this software, bone age was estimated from left-hand radiographs of 200 patients (3–17 years old) using first-rank bone age (software only), computer-assisted bone age (two radiologists with software assistance), and Greulich-Pyle atlas–assisted bone age (two radiologists with Greulich-Pyle atlas assistance only). The reference bone age was determined by the consen- sus of two experienced radiologists. RESULTS. First-rank bone ages determined by the automatic software system showed a 69.5% concordance rate and significant correlations with the reference bone age (r = 0.992; p < 0.001). Concordance rates increased with the use of the automatic software system for both reviewer 1 (63.0% for Greulich-Pyle atlas–assisted bone age vs 72.5% for computer-as- sisted bone age) and reviewer 2 (49.5% for Greulich-Pyle atlas–assisted bone age vs 57.5% for computer-assisted bone age). Reading times were reduced by 18.0% and 40.0% for reviewers 1 and 2, respectively. CONCLUSION. Automatic software system showed reliably accurate bone age estima- tions and appeared to enhance efficiency by reducing reading times without compromising the diagnostic accuracy. Kim et al. Accuracy and Efficiency of Computerized Bone Age Estimation Pediatric Imaging Original Research Downloadedfromwww.ajronline.orgbyFloridaAtlanticUnivon09/13/17fromIPaddress131.91.169.193.CopyrightARRS.Forpersonaluseonly;allrightsreserved • 총 환자의 수: 200명 • 레퍼런스: 경험 많은 소아영상의학과 전문의 2명(18년, 4년 경력)의 컨센서스 • 의사A: 소아영상 세부전공한 영상의학 전문의 (500례 이상의 판독 경험) • 의사B: 영상의학과 2년차 전공의 (판독법 하루 교육 이수 + 20례 판독) • 인공지능: VUNO의 골연령 판독 딥러닝 AJR Am J Roentgenol. 2017 Dec;209(6):1374-1380.
- 194. 40 50 60 70 80 인공지능 의사 A의사 B 69.5% 63% 49.5% 정확도(%) 영상의학과 펠로우 (소아영상 세부전공) 영상의학과 2년차 전공의 인공지능 vs 의사 AJR Am J Roentgenol. 2017 Dec;209(6):1374-1380. • 총 환자의 수: 200명 • 의사A: 소아영상 세부전공한 영상의학 전문의 (500례 이상의 판독 경험) • 의사B: 영상의학과 2년차 전공의 (판독법 하루 교육 이수 + 20례 판독) • 레퍼런스: 경험 많은 소아영상의학과 전문의 2명(18년, 4년 경력)의 컨센서스 • 인공지능: VUNO의 골연령 판독 딥러닝 골연령 판독에 인간 의사와 인공지능의 시너지 효과 Digital Healthcare Institute Director,Yoon Sup Choi, PhD yoonsup.choi@gmail.com
- 195. 40 50 60 70 80 인공지능 의사 A의사 B 40 50 60 70 80 의사 A + 인공지능 의사 B + 인공지능 69.5% 63% 49.5% 72.5% 57.5% 정확도(%) 영상의학과 펠로우 (소아영상 세부전공) 영상의학과 2년차 전공의 인공지능 vs 의사 인공지능 + 의사 AJR Am J Roentgenol. 2017 Dec;209(6):1374-1380. • 총 환자의 수: 200명 • 의사A: 소아영상 세부전공한 영상의학 전문의 (500례 이상의 판독 경험) • 의사B: 영상의학과 2년차 전공의 (판독법 하루 교육 이수 + 20례 판독) • 레퍼런스: 경험 많은 소아영상의학과 전문의 2명(18년, 4년 경력)의 컨센서스 • 인공지능: VUNO의 골연령 판독 딥러닝 골연령 판독에 인간 의사와 인공지능의 시너지 효과 Digital Healthcare Institute Director,Yoon Sup Choi, PhD yoonsup.choi@gmail.com
- 196. 총 판독 시간(m) 0 50 100 150 200 w/o AI w/ AI 0 50 100 150 200 w/o AI w/ AI 188m 154m 180m 108m saving 40% of time saving 18% of time 의사 A 의사 B 골연령 판독에서 인공지능을 활용하면 판독 시간의 절감도 가능 • 총 환자의 수: 200명 • 의사A: 소아영상 세부전공한 영상의학 전문의 (500례 이상의 판독 경험) • 의사B: 영상의학과 2년차 전공의 (판독법 하루 교육 이수 + 20례 판독) • 레퍼런스: 경험 많은 소아영상의학과 전문의 2명(18년, 4년 경력)의 컨센서스 • 인공지능: VUNO의 골연령 판독 딥러닝 AJR Am J Roentgenol. 2017 Dec;209(6):1374-1380. Digital Healthcare Institute Director,Yoon Sup Choi, PhD yoonsup.choi@gmail.com
- 197. This copy isfor personal use only. To order printed copies, contact reprints@rsna.org This copy is for personal use only. To order printed copies, contact reprints@rsna.org ORIGINAL RESEARCH • THORACIC IMAGING hest radiography, one of the most common diagnos- intraobserver agreements because of its limited spatial reso- Development and Validation of Deep Learning–based Automatic Detection Algorithm for Malignant Pulmonary Nodules on Chest Radiographs Ju Gang Nam, MD* • Sunggyun Park, PhD* • Eui Jin Hwang, MD • Jong Hyuk Lee, MD • Kwang-Nam Jin, MD, PhD • KunYoung Lim, MD, PhD • Thienkai HuyVu, MD, PhD • Jae Ho Sohn, MD • Sangheum Hwang, PhD • Jin Mo Goo, MD, PhD • Chang Min Park, MD, PhD From the Department of Radiology and Institute of Radiation Medicine, Seoul National University Hospital and College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea (J.G.N., E.J.H., J.M.G., C.M.P.); Lunit Incorporated, Seoul, Republic of Korea (S.P.); Department of Radiology, Armed Forces Seoul Hospital, Seoul, Republic of Korea (J.H.L.); Department of Radiology, Seoul National University Boramae Medical Center, Seoul, Republic of Korea (K.N.J.); Department of Radiology, National Cancer Center, Goyang, Republic of Korea (K.Y.L.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (T.H.V., J.H.S.); and Department of Industrial & Information Systems Engineering, Seoul National University of Science and Technology, Seoul, Republic of Korea (S.H.). Received January 30, 2018; revision requested March 20; revision received July 29; accepted August 6. Address correspondence to C.M.P. (e-mail: cmpark.morphius@gmail.com). Study supported by SNUH Research Fund and Lunit (06–2016–3000) and by Seoul Research and Business Development Program (FI170002). *J.G.N. and S.P. contributed equally to this work. Conflicts of interest are listed at the end of this article. Radiology 2018; 00:1–11 • https://doi.org/10.1148/radiol.2018180237 • Content codes: Purpose: To develop and validate a deep learning–based automatic detection algorithm (DLAD) for malignant pulmonary nodules on chest radiographs and to compare its performance with physicians including thoracic radiologists. Materials and Methods: For this retrospective study, DLAD was developed by using 43292 chest radiographs (normal radiograph– to–nodule radiograph ratio, 34067:9225) in 34676 patients (healthy-to-nodule ratio, 30784:3892; 19230 men [mean age, 52.8 years; age range, 18–99 years]; 15446 women [mean age, 52.3 years; age range, 18–98 years]) obtained between 2010 and 2015, which were labeled and partially annotated by 13 board-certified radiologists, in a convolutional neural network. Radiograph clas- sification and nodule detection performances of DLAD were validated by using one internal and four external data sets from three South Korean hospitals and one U.S. hospital. For internal and external validation, radiograph classification and nodule detection performances of DLAD were evaluated by using the area under the receiver operating characteristic curve (AUROC) and jackknife alternative free-response receiver-operating characteristic (JAFROC) figure of merit (FOM), respectively. An observer performance test involving 18 physicians, including nine board-certified radiologists, was conducted by using one of the four external validation data sets. Performances of DLAD, physicians, and physicians assisted with DLAD were evaluated and compared. Results: According to one internal and four external validation data sets, radiograph classification and nodule detection perfor- mances of DLAD were a range of 0.92–0.99 (AUROC) and 0.831–0.924 (JAFROC FOM), respectively. DLAD showed a higher AUROC and JAFROC FOM at the observer performance test than 17 of 18 and 15 of 18 physicians, respectively (P , .05), and all physicians showed improved nodule detection performances with DLAD (mean JAFROC FOM improvement, 0.043; range, 0.006–0.190; P , .05). Conclusion: This deep learning–based automatic detection algorithm outperformed physicians in radiograph classification and nod- ule detection performance for malignant pulmonary nodules on chest radiographs, and it enhanced physicians’ performances when used as a second reader. ©RSNA, 2018 Online supplemental material is available for this article. • 43,292 chest PA (normal:nodule=34,067:9225) • labeled/annotated by 13 board-certified radiologists. • DLAD were validated 1 internal + 4 external datasets • 서울대병원 / 보라매병원 / 국립암센터 / UCSF • Classification / Lesion localization • 인공지능 vs. 의사 vs. 인공지능+의사 • 다양한 수준의 의사와 비교 • Non-radiology / radiology residents • Board-certified radiologist / Thoracic radiologists
- 198. Nam et al Figure1: Images in a 78-year-old female patient with a 1.9-cm part-solid nodule at the left upper lobe. (a) The nodule was faintly visible on the chest radiograph (arrowheads) and was detected by 11 of 18 observers. (b) At contrast-enhanced CT examination, biopsy confirmed lung adeno- carcinoma (arrow). (c) DLAD reported the nodule with a confidence level of 2, resulting in its detection by an additional five radiologists and an elevation in its confidence by eight radiologists. Figure 2: Images in a 64-year-old male patient with a 2.2-cm lung adenocarcinoma at the left upper lobe. (a) The nodule was faintly visible on the chest radiograph (arrowheads) and was detected by seven of 18 observers. (b) Biopsy confirmed lung adenocarcinoma in the left upper lobe on contrast-enhanced CT image (arrow). (c) DLAD reported the nodule with a confidence level of 2, resulting in its detection by an additional two radiologists and an elevated confidence level of the nodule by two radiologists.
- 199. Deep Learning AutomaticDetection Algorithm for Malignant Pulmonary Nodules Table 3: Patient Classification and Nodule Detection at the Observer Performance Test Observer Test 1 DLAD versus Test 1 (P Value) Test 2 Test 1 versus Test 2 (P Value) Radiograph Classification (AUROC) Nodule Detection (JAFROC FOM) Radiograph Classification Nodule Detection Radiograph Classification (AUROC) Nodule Detection (JAFROC FOM) Radiograph Classification Nodule Detection Nonradiology physicians Observer 1 0.77 0.716 ,.001 ,.001 0.91 0.853 ,.001 ,.001 Observer 2 0.78 0.657 ,.001 ,.001 0.90 0.846 ,.001 ,.001 Observer 3 0.80 0.700 ,.001 ,.001 0.88 0.783 ,.001 ,.001 Group 0.691 ,.001* 0.828 ,.001* Radiology residents Observer 4 0.78 0.767 ,.001 ,.001 0.80 0.785 .02 .03 Observer 5 0.86 0.772 .001 ,.001 0.91 0.837 .02 ,.001 Observer 6 0.86 0.789 .05 .002 0.86 0.799 .08 .54 Observer 7 0.84 0.807 .01 .003 0.91 0.843 .003 .02 Observer 8 0.87 0.797 .10 .003 0.90 0.845 .03 .001 Observer 9 0.90 0.847 .52 .12 0.92 0.867 .04 .03 Group 0.790 ,.001* 0.867 ,.001* Board-certified radiologists Observer 10 0.87 0.836 .05 .01 0.90 0.865 .004 .002 Observer 11 0.83 0.804 ,.001 ,.001 0.84 0.817 .03 .04 Observer 12 0.88 0.817 .18 .005 0.91 0.841 .01 .01 Observer 13 0.91 0.824 ..99 .02 0.92 0.836 .51 .24 Observer 14 0.88 0.834 .14 .03 0.88 0.840 .87 .23 Group 0.821 .02* 0.840 .01* Thoracic radiologists Observer 15 0.94 0.856 .15 .21 0.96 0.878 .08 .03 Observer 16 0.92 0.854 .60 .17 0.93 0.872 .34 .02 Observer 17 0.86 0.820 .02 .01 0.88 0.838 .14 .12 Observer 18 0.84 0.800 ,.001 ,.001 0.87 0.827 .02 .02 Group 0.833 .08* 0.854 ,.001* Note.—Observer 4 had 1 year of experience; observers 5 and 6 had 2 years of experience; observers 7–9 had 3 years of experience; observers 10–12 had 7 years of experience; observers 13 and 14 had 8 years of experience; observer 15 had 26 years of experience; observer 16 had 13 years of experience; and observers 17 and 18 had 9 years of experience. Observers 1–3 were 4th-year residents from obstetrics and gynecolo- 의사 인공지능 vs. 의사만 (p value) 의사+인공지능 의사 vs. 의사+인공지능 (p value) 영상의학과 1년차 전공의 영상의학과 2년차 전공의 영상의학과 3년차 전공의 산부인과 4년차 전공의 정형외과 4년차 전공의 내과 4년차 전공의 영상의학과 전문의 7년 경력 8년 경력 영상의학과 전문의 (흉부) 26년 경력 13년 경력 9년 경력 영상의학과 전공의 비영상의학과 의사
- 200. Deep Learning AutomaticDetection Algorithm for Malignant Pulmonary Nodules Table 3: Patient Classification and Nodule Detection at the Observer Performance Test Observer Test 1 DLAD versus Test 1 (P Value) Test 2 Test 1 versus Test 2 (P Value) Radiograph Classification (AUROC) Nodule Detection (JAFROC FOM) Radiograph Classification Nodule Detection Radiograph Classification (AUROC) Nodule Detection (JAFROC FOM) Radiograph Classification Nodule Detection Nonradiology physicians Observer 1 0.77 0.716 ,.001 ,.001 0.91 0.853 ,.001 ,.001 Observer 2 0.78 0.657 ,.001 ,.001 0.90 0.846 ,.001 ,.001 Observer 3 0.80 0.700 ,.001 ,.001 0.88 0.783 ,.001 ,.001 Group 0.691 ,.001* 0.828 ,.001* Radiology residents Observer 4 0.78 0.767 ,.001 ,.001 0.80 0.785 .02 .03 Observer 5 0.86 0.772 .001 ,.001 0.91 0.837 .02 ,.001 Observer 6 0.86 0.789 .05 .002 0.86 0.799 .08 .54 Observer 7 0.84 0.807 .01 .003 0.91 0.843 .003 .02 Observer 8 0.87 0.797 .10 .003 0.90 0.845 .03 .001 Observer 9 0.90 0.847 .52 .12 0.92 0.867 .04 .03 Group 0.790 ,.001* 0.867 ,.001* Board-certified radiologists Observer 10 0.87 0.836 .05 .01 0.90 0.865 .004 .002 Observer 11 0.83 0.804 ,.001 ,.001 0.84 0.817 .03 .04 Observer 12 0.88 0.817 .18 .005 0.91 0.841 .01 .01 Observer 13 0.91 0.824 ..99 .02 0.92 0.836 .51 .24 Observer 14 0.88 0.834 .14 .03 0.88 0.840 .87 .23 Group 0.821 .02* 0.840 .01* Thoracic radiologists Observer 15 0.94 0.856 .15 .21 0.96 0.878 .08 .03 Observer 16 0.92 0.854 .60 .17 0.93 0.872 .34 .02 Observer 17 0.86 0.820 .02 .01 0.88 0.838 .14 .12 Observer 18 0.84 0.800 ,.001 ,.001 0.87 0.827 .02 .02 Group 0.833 .08* 0.854 ,.001* Note.—Observer 4 had 1 year of experience; observers 5 and 6 had 2 years of experience; observers 7–9 had 3 years of experience; observers 10–12 had 7 years of experience; observers 13 and 14 had 8 years of experience; observer 15 had 26 years of experience; observer 16 had 13 years of experience; and observers 17 and 18 had 9 years of experience. Observers 1–3 were 4th-year residents from obstetrics and gynecolo- 의사 인공지능 vs. 의사만 (p value) 의사+인공지능 의사 vs. 의사+인공지능 (p value) 영상의학과 1년차 전공의 영상의학과 2년차 전공의 영상의학과 3년차 전공의 산부인과 4년차 전공의 정형외과 4년차 전공의 내과 4년차 전공의 영상의학과 전문의 7년 경력 8년 경력 영상의학과 전문의 (흉부) 26년 경력 13년 경력 9년 경력 영상의학과 전공의 비영상의학과 의사 •인공지능을 second reader로 활용하면 정확도가 개선 •classification: 17 of 18 명이 개선 (15 of 18, P<0.05) •nodule detection: 18 of 18 명이 개선 (14 of 18, P<0.05)
- 201. Deep Learning AutomaticDetection Algorithm for Malignant Pulmonary Nodules Table 3: Patient Classification and Nodule Detection at the Observer Performance Test Observer Test 1 DLAD versus Test 1 (P Value) Test 2 Test 1 versus Test 2 (P Value) Radiograph Classification (AUROC) Nodule Detection (JAFROC FOM) Radiograph Classification Nodule Detection Radiograph Classification (AUROC) Nodule Detection (JAFROC FOM) Radiograph Classification Nodule Detection Nonradiology physicians Observer 1 0.77 0.716 ,.001 ,.001 0.91 0.853 ,.001 ,.001 Observer 2 0.78 0.657 ,.001 ,.001 0.90 0.846 ,.001 ,.001 Observer 3 0.80 0.700 ,.001 ,.001 0.88 0.783 ,.001 ,.001 Group 0.691 ,.001* 0.828 ,.001* Radiology residents Observer 4 0.78 0.767 ,.001 ,.001 0.80 0.785 .02 .03 Observer 5 0.86 0.772 .001 ,.001 0.91 0.837 .02 ,.001 Observer 6 0.86 0.789 .05 .002 0.86 0.799 .08 .54 Observer 7 0.84 0.807 .01 .003 0.91 0.843 .003 .02 Observer 8 0.87 0.797 .10 .003 0.90 0.845 .03 .001 Observer 9 0.90 0.847 .52 .12 0.92 0.867 .04 .03 Group 0.790 ,.001* 0.867 ,.001* Board-certified radiologists Observer 10 0.87 0.836 .05 .01 0.90 0.865 .004 .002 Observer 11 0.83 0.804 ,.001 ,.001 0.84 0.817 .03 .04 Observer 12 0.88 0.817 .18 .005 0.91 0.841 .01 .01 Observer 13 0.91 0.824 ..99 .02 0.92 0.836 .51 .24 Observer 14 0.88 0.834 .14 .03 0.88 0.840 .87 .23 Group 0.821 .02* 0.840 .01* Thoracic radiologists Observer 15 0.94 0.856 .15 .21 0.96 0.878 .08 .03 Observer 16 0.92 0.854 .60 .17 0.93 0.872 .34 .02 Observer 17 0.86 0.820 .02 .01 0.88 0.838 .14 .12 Observer 18 0.84 0.800 ,.001 ,.001 0.87 0.827 .02 .02 Group 0.833 .08* 0.854 ,.001* Note.—Observer 4 had 1 year of experience; observers 5 and 6 had 2 years of experience; observers 7–9 had 3 years of experience; observers 10–12 had 7 years of experience; observers 13 and 14 had 8 years of experience; observer 15 had 26 years of experience; observer 16 had 13 years of experience; and observers 17 and 18 had 9 years of experience. Observers 1–3 were 4th-year residents from obstetrics and gynecolo- 의사 인공지능 vs. 의사만 (p value) 의사+인공지능 의사 vs. 의사+인공지능 (p value) 영상의학과 1년차 전공의 영상의학과 2년차 전공의 영상의학과 3년차 전공의 산부인과 4년차 전공의 정형외과 4년차 전공의 내과 4년차 전공의 영상의학과 전문의 7년 경력 8년 경력 영상의학과 전문의 (흉부) 26년 경력 13년 경력 9년 경력 영상의학과 전공의 비영상의학과 의사 인공지능 0.91 0.885
- 202. Deep Learning AutomaticDetection Algorithm for Malignant Pulmonary Nodules Table 3: Patient Classification and Nodule Detection at the Observer Performance Test Observer Test 1 DLAD versus Test 1 (P Value) Test 2 Test 1 versus Test 2 (P Value) Radiograph Classification (AUROC) Nodule Detection (JAFROC FOM) Radiograph Classification Nodule Detection Radiograph Classification (AUROC) Nodule Detection (JAFROC FOM) Radiograph Classification Nodule Detection Nonradiology physicians Observer 1 0.77 0.716 ,.001 ,.001 0.91 0.853 ,.001 ,.001 Observer 2 0.78 0.657 ,.001 ,.001 0.90 0.846 ,.001 ,.001 Observer 3 0.80 0.700 ,.001 ,.001 0.88 0.783 ,.001 ,.001 Group 0.691 ,.001* 0.828 ,.001* Radiology residents Observer 4 0.78 0.767 ,.001 ,.001 0.80 0.785 .02 .03 Observer 5 0.86 0.772 .001 ,.001 0.91 0.837 .02 ,.001 Observer 6 0.86 0.789 .05 .002 0.86 0.799 .08 .54 Observer 7 0.84 0.807 .01 .003 0.91 0.843 .003 .02 Observer 8 0.87 0.797 .10 .003 0.90 0.845 .03 .001 Observer 9 0.90 0.847 .52 .12 0.92 0.867 .04 .03 Group 0.790 ,.001* 0.867 ,.001* Board-certified radiologists Observer 10 0.87 0.836 .05 .01 0.90 0.865 .004 .002 Observer 11 0.83 0.804 ,.001 ,.001 0.84 0.817 .03 .04 Observer 12 0.88 0.817 .18 .005 0.91 0.841 .01 .01 Observer 13 0.91 0.824 ..99 .02 0.92 0.836 .51 .24 Observer 14 0.88 0.834 .14 .03 0.88 0.840 .87 .23 Group 0.821 .02* 0.840 .01* Thoracic radiologists Observer 15 0.94 0.856 .15 .21 0.96 0.878 .08 .03 Observer 16 0.92 0.854 .60 .17 0.93 0.872 .34 .02 Observer 17 0.86 0.820 .02 .01 0.88 0.838 .14 .12 Observer 18 0.84 0.800 ,.001 ,.001 0.87 0.827 .02 .02 Group 0.833 .08* 0.854 ,.001* Note.—Observer 4 had 1 year of experience; observers 5 and 6 had 2 years of experience; observers 7–9 had 3 years of experience; observers 10–12 had 7 years of experience; observers 13 and 14 had 8 years of experience; observer 15 had 26 years of experience; observer 16 had 13 years of experience; and observers 17 and 18 had 9 years of experience. Observers 1–3 were 4th-year residents from obstetrics and gynecolo- 의사 인공지능 vs. 의사만 (p value) 의사+인공지능 의사 vs. 의사+인공지능 (p value) 영상의학과 1년차 전공의 영상의학과 2년차 전공의 영상의학과 3년차 전공의 산부인과 4년차 전공의 정형외과 4년차 전공의 내과 4년차 전공의 영상의학과 전문의 7년 경력 8년 경력 영상의학과 전문의 (흉부) 26년 경력 13년 경력 9년 경력 영상의학과 전공의 비영상의학과 의사 인공지능 0.91 0.885 •“인공지능 혼자” 한 것이 “영상의학과 전문의+인공지능”보다 대부분 더 정확 •classification: 9명 중 6명보다 나음 •nodule detection: 9명 전원보다 나음
- 203. 당뇨성 망막병증 판독인공지능
- 204. 당뇨성 망막병증 • 당뇨병의대표적 합병증: 당뇨병력이 30년 이상 환자 90% 발병 • 안과 전문의들이 안저(안구의 안쪽)를 사진으로 찍어서 판독 • 망막 내 미세혈관 생성, 출혈, 삼출물 정도를 파악하여 진단
- 205. Case Study: TensorFlowin Medicine - Retinal Imaging (TensorFlow Dev Summit 2017)
- 206. 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.
- 207. 안저 판독 인공지능의개발 • CNN으로 후향적으로 128,175개의 안저 이미지 학습 • 미국의 안과전문의 54명이 3-7회 판독한 데이터 • 우수한 안과전문의들 7-8명의 판독 결과와 인공지능의 판독 결과 비교 • EyePACS-1 (9,963 개), Messidor-2 (1,748 개)a) Fullscreen mode b) Hit reset to reload this image. This will reset all of the grading. c) Comment box for other pathologies you see eFigure 2. Screenshot of the Second Screen of the Grading Tool, Which Asks Graders to Assess the Image for DR, DME and Other Notable Conditions or Findings
- 208. • EyePACS-1 과 Messidor-2의 AUC = 0.991, 0.990 • 7-8명의 안과 전문의와 민감도와 특이도가 동일한 수준 • F-score: 0.95 (vs. 인간 의사는 0.91) Additional sensitivity analyses were conducted for sev- effects of data set size on algorithm performance were exam- Figure 2. Validation Set Performance for Referable Diabetic Retinopathy 100 80 60 40 20 0 0 70 80 85 95 90 75 0 5 10 15 20 25 30 100806040 Sensitivity,% 1 – Specificity, % 20 EyePACS-1: AUC, 99.1%; 95% CI, 98.8%-99.3%A 100 High-sensitivity operating point High-specificity operating point 100 80 60 40 20 0 0 70 80 85 95 90 75 0 5 10 15 20 25 30 100806040 Sensitivity,% 1 – Specificity, % 20 Messidor-2: AUC, 99.0%; 95% CI, 98.6%-99.5%B 100 High-specificity operating point High-sensitivity operating point Performance of the algorithm (black curve) and ophthalmologists (colored circles) for the presence of referable diabetic retinopathy (moderate or worse diabetic retinopathy or referable diabetic macular edema) on A, EyePACS-1 (8788 fully gradable images) and B, Messidor-2 (1745 fully gradable images). The black diamonds on the graph correspond to the sensitivity and specificity of the algorithm at the high-sensitivity and high-specificity operating points. In A, for the high-sensitivity operating point, specificity was 93.4% (95% CI, 92.8%-94.0%) and sensitivity was 97.5% (95% CI, 95.8%-98.7%); for the high-specificity operating point, specificity was 98.1% (95% CI, 97.8%-98.5%) and sensitivity was 90.3% (95% CI, 87.5%-92.7%). In B, for the high-sensitivity operating point, specificity was 93.9% (95% CI, 92.4%-95.3%) and sensitivity was 96.1% (95% CI, 92.4%-98.3%); for the high-specificity operating point, specificity was 98.5% (95% CI, 97.7%-99.1%) and sensitivity was 87.0% (95% CI, 81.1%-91.0%). There were 8 ophthalmologists who graded EyePACS-1 and 7 ophthalmologists who graded Messidor-2. AUC indicates area under the receiver operating characteristic curve. Research Original Investigation Accuracy of a Deep Learning Algorithm for Detection of Diabetic Retinopathy 안저 판독 인공지능의 정확도
- 209. •2018년 4월 FDA는안저사진을 판독하여 당뇨성 망막병증(DR)을 진단하는 인공지능 시판 허가 •IDx-DR: 클라우드 기반의 소프트웨어로, Topcon NW400 로 찍은 사진을 판독 •의사의 개입 없이 안저 사진을 판독하여 DR 여부를 진단 •두 가지 답 중에 하나를 준다 •1) mild DR 이상이 detection 되었으니, 의사에게 가봐라 •2) mild DR 이상은 없는 것 같으니, 12개월 이후에 다시 검사 받아봐라 •임상시험 및 성능 •10개의 병원에서 멀티센터로 900명 환자의 데이터를 분석 •민감도와 특이도가 각각 87.4%, 89.5% (JAMA 논문의 구글 인공지능 보다 낮음) •FDA가 de novo premarket review pathway로 진행
- 210.
- 211. A B DC Benignwithout atypia / Atypic / DCIS (ductal carcinoma in situ) / Invasive Carcinoma Interpretation? Elmore etl al. JAMA 2015 Diagnostic Concordance Among Pathologists 유방암 병리 데이터 판독하기
- 212. Figure 4. ParticipatingPathologists’ Interpretations of Each of the 240 Breast Biopsy Test Cases 0 25 50 75 100 Interpretations, % 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60 62 64 66 68 70 72 Case Benign without atypia 72 Cases 2070 Total interpretations A 0 25 50 75 100 Interpretations, % 218 220 222 224 226 228 230 232 234 236 238 240 Case Invasive carcinoma 23 Cases 663 Total interpretations D 0 25 50 75 100 Interpretations, % 147 145 149 151 153 155 157 159 161 163 165 167 169 171 173 175 177 179 181 183 185 187 189 191 193 195 197 199 201 203 205 207 209 211 213 215 217 Case DCIS 73 Cases 2097 Total interpretations C 0 25 50 75 100 Interpretations, % 74 76 78 80 82 84 86 88 90 92 94 96 98 100 102 104 106 108 110 112 114 116 118 120 122 124 126 128 130 132 134 136 138 140 142 144 Case Atypia 72 Cases 2070 Total interpretations B Benign without atypia Atypia DCIS Invasive carcinoma Pathologist interpretation DCIS indicates ductal carcinoma in situ. Diagnostic Concordance in Interpreting Breast Biopsies Original Investigation Research Elmore etl al. JAMA 2015 유방암 판독에 대한 병리학과 전문의들의 불일치도
- 213. ISBI Grand Challengeon Cancer Metastases Detection in Lymph Node
- 215.
- 216. International Symposium onBiomedical Imaging 2016 H&E Image Processing Framework Train whole slide image sample sample training data normaltumor Test whole slide image overlapping image patches tumor prob. map 1.0 0.0 0.5 Convolutional Neural Network P(tumor)
- 217.
- 218. Clinical study onISBI dataset Error Rate Pathologist in competition setting 3.5% Pathologists in clinical practice (n = 12) 13% - 26% Pathologists on micro-metastasis(small tumors) 23% - 42% Beck Lab Deep Learning Model 0.65% Beck Lab’s deep learning model now outperforms pathologist Andrew Beck, Machine Learning for Healthcare, MIT 2017
- 219. 구글의 유방 병리판독 인공지능 • The localization score(FROC) for the algorithm reached 89%, which significantly exceeded the score of 73% for a pathologist with no time constraint.
- 220. 인공지능의 민감도 +인간의 특이도 Yun Liu et al. Detecting Cancer Metastases on Gigapixel Pathology Images (2017) • 구글의 인공지능은 민감도에서 큰 개선 (92.9%, 88.5%) •@8FP: FP를 8개까지 봐주면서, 달성할 수 있는 민감도 •FROC: FP를 슬라이드당 1/4, 1/2, 1, 2, 4, 8개를 허용한 민감도의 평균 •즉, FP를 조금 봐준다면, 인공지능은 매우 높은 민감도를 달성 가능 • 인간 병리학자는 민감도 73%에 반해, 특이도는 거의 100% 달성 •인간 병리학자와 인공지능 병리학자는 서로 잘하는 것이 다름 •양쪽이 협력하면 판독 효율성, 일관성, 민감도 등에서 개선 기대 가능
- 221. •복잡한 의료 데이터의분석 및 insight 도출 •영상 의료/병리 데이터의 분석/판독 •연속 데이터의 모니터링 및 예방/예측 의료 인공지능의 세 유형
- 224. http://www.rolls-royce.com/about/our-technology/enabling-technologies/engine-health-management.aspx#sense 250 sensors tomonitor the “health” of the GE turbines
- 225. Fig 1. Whatcan consumer wearables do? Heart rate can be measured with an oximeter built into a ring [3], muscle activity with an electromyographi sensor embedded into clothing [4], stress with an electodermal sensor incorporated into a wristband [5], and physical activity or sleep patterns via an accelerometer in a watch [6,7]. In addition, a female’s most fertile period can be identified with detailed body temperature tracking [8], while levels of me attention can be monitored with a small number of non-gelled electroencephalogram (EEG) electrodes [9]. Levels of social interaction (also known to a PLOS Medicine 2016
- 226.
- 228. S E PS 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 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 R E S E A R C H A R T I C L E onNovember3,2016http://stm.sciencemag.org/Downloadedfrom
- 230. puted as newdata became avail when his or her score crossed t dation set, the AUC obtained f 0.81 to 0.85) (Fig. 2). At a spec of 0.33], TREWScore achieved a s a median of 28.2 hours (IQR, 10 Identification of patients b A critical event in the developme related organ dysfunction (seve been shown to increase after th more than two-thirds (68.8%) o were identified before any sepsi tients were identified a median (Fig. 3B). Comparison of TREWScore Weevaluatedtheperformanceof methods for the purpose of provid use of TREWScore. We first com to MEWS, a general metric used of catastrophic deterioration (17 oped for tracking sepsis, MEWS tion of patients at risk for severe Fig. 2. ROC for detection of septic shock before onset in the validation set. The ROC curve for TREWScore is shown in blue, with the ROC curve for MEWS in red. The sensitivity and specificity performance of the routine screening criteria is indicated by the purple dot. Normal 95% CIs are shown for TREWScore and MEWS. TPR, true-positive rate; FPR, false-positive rate. R E S E A R C H A R T I C L E A targeted real-time early warning score (TREWScore) for septic shock AUC=0.83 At a specificity of 0.67, TREWScore achieved a sensitivity of 0.85 and identified patients a median of 28.2 hours before onset.
- 231. March 2019, theFuture of Individual Medicine @San Diego
- 233.
- 234. •미국에서 아이폰 앱으로출시 •사용이 얼마나 번거로울지가 관건 •어느 정도의 기간을 활용해야 효과가 있는가: 2주? 평생? •Food logging 등을 어떻게 할 것인가? •과금 방식도 아직 공개되지 않은듯
- 235.
- 236. ADA 2017, SanDiego, Courtesy of Taeho Kim (Seoul Medical Center)
- 237. An Algorithm Basedon 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 byguestonJune28,2018http://jaha.ahajournals.org/Downloadedfrom
- 238. •환자 수: 86,290 •cardiacarrest: 633 •Input: Heart rate, Respiratory rate, Body temperature, Systolic Blood Pressure (source: VUNO) Cardiac Arrest Prediction Accuracy
- 239. •대학병원 신속 대응팀에서처리 가능한 알림 수 (A, B 지점) 에서 더 큰 정확도 차이를 보임 •A: DEWS 33.0%, MEWS 0.3% •B: DEWS 42.7%, MEWS 4.0% (source: VUNO) APPH(Alarms Per Patients Per Hour) (source: VUNO) Less False Alarm
- 240. (source: VUNO) 시간에 따른DEWS 예측 변화
- 241. FOCUS | LETTERS https://doi.org/10.1038/s41591-018-0268-3 1 Departmentof Computer Science, Stanford University, Stanford, CA, USA. 2 iRhythm Technologies Inc., San Francisco, CA, USA. 3 Division of Cardiology, Department of Medicine, University of California San Francisco, San Francisco, CA, USA. 4 Department of Medicine and Center for Digital Health, Stanford University School of Medicine, Stanford, CA, USA. 5 Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA. 6 These authors contributed equally: Awni Y. Hannun, Pranav Rajpurkar, Masoumeh Haghpanahi, Geoffrey H. Tison. *e-mail: awni@cs.stanford.edu Computerized electrocardiogram (ECG) interpretation plays a critical role in the clinical ECG workflow1 . Widely available digital ECG data and the algorithmic paradigm of deep learn- ing2 present an opportunity to substantially improve the accu- racy and scalability of automated ECG analysis. However, a comprehensive evaluation of an end-to-end deep learning approach for ECG analysis across a wide variety of diagnostic classes has not been previously reported. Here, we develop a deep neural network (DNN) to classify 12 rhythm classes using 91,232 single-lead ECGs from 53,549 patients who used a single-lead ambulatory ECG monitoring device. When validated against an independent test dataset annotated by a consensus committee of board-certified practicing cardiolo- gists, the DNN achieved an average area under the receiver operating characteristic curve (ROC) of 0.97. The average F1 score, which is the harmonic mean of the positive predictive value and sensitivity, for the DNN (0.837) exceeded that of average cardiologists (0.780). With specificity fixed at the average specificity achieved by cardiologists, the sensitivity of the DNN exceeded the average cardiologist sensitivity for all rhythm classes. These findings demonstrate that an end- to-end deep learning approach can classify a broad range of distinct arrhythmias from single-lead ECGs with high diagnos- tic performance similar to that of cardiologists. If confirmed in clinical settings, this approach could reduce the rate of misdi- agnosed computerized ECG interpretations and improve the efficiency of expert human ECG interpretation by accurately triaging or prioritizing the most urgent conditions. The electrocardiogram is a fundamental tool in the everyday practice of clinical medicine, with more than 300 million ECGs obtained annually worldwide3 . The ECG is pivotal for diagnos- ing a wide spectrum of abnormalities from arrhythmias to acute coronary syndrome4 . Computer-aided interpretation has become increasingly important in the clinical ECG workflow since its intro- duction over 50years ago, serving as a crucial adjunct to physician interpretation in many clinical settings1 . However, existing com- mercial ECG interpretation algorithms still show substantial rates of misdiagnosis1,5–7 . The combination of widespread digitization of ECG data and the development of algorithmic paradigms that can benefit from large-scale processing of raw data presents an opportu- nity to reexamine the standard approach to algorithmic ECG analy- sis and may provide substantial improvements to automated ECG interpretation. Substantial algorithmic advances in the past five years have been driven largely by a specific class of models known as deep neural networks2 . DNNs are computational models consisting of multiple processing layers, with each layer being able to learn increasingly abstract, higher-level representations of the input data relevant to perform specific tasks. They have dramatically improved the state of the art in speech recognition8 , image recognition9 , strategy games such as Go10 , and in medical applications11,12 . The ability of DNNs to recognize patterns and learn useful features from raw input data without requiring extensive data preprocessing, feature engineer- ing or handcrafted rules2 makes them particularly well suited to interpret ECG data. Furthermore, since DNN performance tends to increase as the amount of training data increases2 , this approach is well positioned to take advantage of the widespread digitization of ECG data. A comprehensive evaluation of whether an end-to-end deep learning approach can be used to analyze raw ECG data to classify a broad range of diagnoses remains lacking. Much of the previous work to employ DNNs toward ECG interpretation has focused on single aspects of the ECG processing pipeline, such as noise reduc- tion13,14 or feature extraction15,16 , or has approached limited diag- nostic tasks, detecting only a handful of heartbeat types (normal, ventricular or supraventricular ectopic, fusion, and so on)17–20 or rhythm diagnoses (most commonly atrial fibrillation or ventric- ular tachycardia)21–25 . Lack of appropriate data has limited many efforts beyond these applications. Most prior efforts used data from the MIT-BIH Arrhythmia database (PhysioNet)26 , which is limited by the small number of patients and rhythm episodes present in the dataset. In this study, we constructed a large, novel ECG dataset that underwent expert annotation for a broad range of ECG rhythm classes. We developed a DNN to detect 12 rhythm classes from raw single-lead ECG inputs using a training dataset consisting of 91,232 ECG records from 53,549 patients. The DNN was designed to classify 10 arrhythmias as well as sinus rhythm and noise for a total of 12 output rhythm classes (Extended Data Fig. 1). ECG data were recorded by the Zio monitor, which is a Food and Drug Administration (FDA)-cleared, single-lead, patch-based ambula- tory ECG monitor27 that continuously records data from a single vector (modified Lead II) at 200Hz. The mean and median wear time of the Zio monitor in our dataset was 10.6 and 13.0days, respectively. Mean age was 69±16years and 43% were women. We validated the DNN on a test dataset that consisted of 328 ECG records collected from 328 unique patients, which was annotated by a consensus committee of expert cardiologists (see Methods). Mean age on the test dataset was 70±17years and 38% were women. The mean inter-annotator agreement on the test dataset was 72.8%. Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network Awni Y. Hannun 1,6 *, Pranav Rajpurkar 1,6 , Masoumeh Haghpanahi2,6 , Geoffrey H. Tison 3,6 , Codie Bourn2 , Mintu P. Turakhia4,5 and Andrew Y. Ng1 FOCUS | LETTERS https://doi.org/10.1038/s41591-018-0268-3 NATURE MEDICINE | VOL 25 | JANUARY 2019 | 65–69 | www.nature.com/naturemedicine 65 • 53,549명의 환자에게서 얻은 91,232 건의 single-lead ECG 데이터 • ZIO patch (FDA cleared, single led, ambulatory ECG monitor) • 총 12가지 종류의 부정맥으로 구분하는 DNN 개발 (34-layer network)
- 242. • 53,549명의 환자에게서얻은 91,232 건의 single-lead ECG 데이터 • ZIO patch (FDA cleared, single led, ambulatory ECG monitor) • 총 12가지 종류의 부정맥으로 구분하는 DNN 개발 (34-layer network) Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks
- 243. •Training •328명의 개별 환자로부터 얻은, 328 ECG •정답으로 세명의 cardiologist 로부터 얻은 consensus를 활용LETTERS | FOCUS NATURE MEDICINE Supplementary Table 1 shows the number of unique patients exhibiting each rhythm class. We first compared the performance of the DNN against the gold standard cardiologist consensus committee diagnoses by calculat- ing the AUC (Table 1a). Since the DNN algorithm was designed to make a rhythm class prediction approximately once per second (see Methods), we report performance both as assessed once every second—which we call “sequence-level” and consists of one rhythm class per interval—and once per record, which we call “set-level” scores on the 10% development dataset (n=8,761) were materially unchanged from the test dataset results, although they were slightly higher (Supplementary Tables 3 and 4). In addition, we retrained the DNN holding out an additional 10% of the training dataset as a second held-out test dataset (n=8,768); the AUC and F1 scores for all rhythms were materially unchanged (Supplementary Tables 5 and 6). We note that unlike the primary test dataset, which has gold- standard annotations from a committee of cardiologists, both sensi- tivity analysis datasets are annotated by certified ECG technicians. Table 1 | Diagnostic performance of the DNN and averaged individual cardiologists compared to the cardiologist committee consensus (n=328) Algorithm AUC (95% CI)a Algorithm F1 b Average cardiologist F1 Sequencea Setb Sequence Set Sequence Set Atrial fibrillation and flutter 0.973 (0.966–0.980) 0.965 (0.932–0.998) 0.801 0.831 0.677 0.686 AVB 0.988 (0.983–0.993) 0.981 (0.953–1.000) 0.828 0.808 0.772 0.761 Bigeminy 0.997 (0.991–1.000) 0.996 (0.976–1.000) 0.847 0.870 0.842 0.853 EAR 0.913 (0.889–0.937) 0.940 (0.870–1.000) 0.541 0.596 0.482 0.536 IVR 0.995 (0.989–1.000) 0.987 (0.959–1.000) 0.761 0.818 0.632 0.720 Junctional rhythm 0.987 (0.980–0.993) 0.979 (0.946–1.000) 0.664 0.789 0.692 0.679 Noise 0.981 (0.973–0.989) 0.947 (0.898–0.996) 0.844 0.761 0.768 0.685 Sinus rhythm 0.975 (0.971–0.979) 0.987 (0.976–0.998) 0.887 0.933 0.852 0.910 SVT 0.973 (0.960–0.985) 0.953 (0.903–1.000) 0.488 0.693 0.451 0.564 Trigeminy 0.998 (0.995–1.000) 0.997 (0.979–1.000) 0.907 0.864 0.842 0.812 Ventricular tachycardia 0.995 (0.980–1.000) 0.980 (0.934–1.000) 0.541 0.681 0.566 0.769 Wenckebach 0.978 (0.967–0.989) 0.977 (0.938–1.000) 0.702 0.780 0.591 0.738 Frequency-weighted average 0.978 0.977 0.807 0.837 0.753 0.780 a DNN algorithm area under the ROC compared to the cardiologist committee consensus. b DNN algorithm and averaged individual cardiologist F1 scores compared to the cardiologist committee consensus. Sequence-level describes the algorithm predictions that are made once every 256 input samples (approximately every 1.3s) and are compared against the gold-standard committee consensus at the same intervals. Set-level describes the unique set of algorithm predictions that are present in the 30-s record. Sequence AUC prediction, n=7,544; set AUC prediction, n=328. LETTERS | FOCUS https://doi.org/10.1038/s41591-018-0268-3LETTERS | FOCUS NATURE MEDICINE •Sequence level: 1초에 한번씩 예측치를 제공 (onset, offset) •Set level: 데이터 당 한 번의 결과치를 제공 (부정맥의 존재유무)
- 244. LETTERS | FOCUSNATURE MEDICINE Supplementary Table 1 shows the number of unique patients exhibiting each rhythm class. We first compared the performance of the DNN against the gold standard cardiologist consensus committee diagnoses by calculat- ing the AUC (Table 1a). Since the DNN algorithm was designed to make a rhythm class prediction approximately once per second (see Methods), we report performance both as assessed once every second—which we call “sequence-level” and consists of one rhythm class per interval—and once per record, which we call “set-level” scores on the 10% development dataset (n=8,761) were materially unchanged from the test dataset results, although they were slightly higher (Supplementary Tables 3 and 4). In addition, we retrained the DNN holding out an additional 10% of the training dataset as a second held-out test dataset (n=8,768); the AUC and F1 scores for all rhythms were materially unchanged (Supplementary Tables 5 and 6). We note that unlike the primary test dataset, which has gold- standard annotations from a committee of cardiologists, both sensi- tivity analysis datasets are annotated by certified ECG technicians. Table 1 | Diagnostic performance of the DNN and averaged individual cardiologists compared to the cardiologist committee consensus (n=328) Algorithm AUC (95% CI)a Algorithm F1 b Average cardiologist F1 Sequencea Setb Sequence Set Sequence Set Atrial fibrillation and flutter 0.973 (0.966–0.980) 0.965 (0.932–0.998) 0.801 0.831 0.677 0.686 AVB 0.988 (0.983–0.993) 0.981 (0.953–1.000) 0.828 0.808 0.772 0.761 Bigeminy 0.997 (0.991–1.000) 0.996 (0.976–1.000) 0.847 0.870 0.842 0.853 EAR 0.913 (0.889–0.937) 0.940 (0.870–1.000) 0.541 0.596 0.482 0.536 IVR 0.995 (0.989–1.000) 0.987 (0.959–1.000) 0.761 0.818 0.632 0.720 Junctional rhythm 0.987 (0.980–0.993) 0.979 (0.946–1.000) 0.664 0.789 0.692 0.679 Noise 0.981 (0.973–0.989) 0.947 (0.898–0.996) 0.844 0.761 0.768 0.685 Sinus rhythm 0.975 (0.971–0.979) 0.987 (0.976–0.998) 0.887 0.933 0.852 0.910 SVT 0.973 (0.960–0.985) 0.953 (0.903–1.000) 0.488 0.693 0.451 0.564 Trigeminy 0.998 (0.995–1.000) 0.997 (0.979–1.000) 0.907 0.864 0.842 0.812 Ventricular tachycardia 0.995 (0.980–1.000) 0.980 (0.934–1.000) 0.541 0.681 0.566 0.769 Wenckebach 0.978 (0.967–0.989) 0.977 (0.938–1.000) 0.702 0.780 0.591 0.738 Frequency-weighted average 0.978 0.977 0.807 0.837 0.753 0.780 a DNN algorithm area under the ROC compared to the cardiologist committee consensus. b DNN algorithm and averaged individual cardiologist F1 scores compared to the cardiologist committee consensus. Sequence-level describes the algorithm predictions that are made once every 256 input samples (approximately every 1.3s) and are compared against the gold-standard committee consensus at the same intervals. Set-level describes the unique set of algorithm predictions that are present in the 30-s record. Sequence AUC prediction, n=7,544; set AUC prediction, n=328. LETTERS | FOCUS https://doi.org/10.1038/s41591-018-0268-3LETTERS | FOCUS NATURE MEDICINE •12종류의 부정맥 모두에서 AUC 0.91 이상 달성 •Sequence level 에서는 하나 (EAR)을 제외하고 AUC 0.97 이상 달성 •Class weighted average AUC •Sequence level=0.978 ; Set level=0.977 •Validation •6명의 독립적인 cardiologist 의 평균적인 실력과 비교 •F1 score를 기준으로 비교 (precision과 recall의 조화평균)
- 245. •Validation •6명의 독립적인 cardiologist의 평균적인 실력과 비교 •F1 score를 기준으로 비교 (precision과 recall의 조화평균)LETTERS | FOCUS NATURE MEDICINE Supplementary Table 1 shows the number of unique patients exhibiting each rhythm class. We first compared the performance of the DNN against the gold standard cardiologist consensus committee diagnoses by calculat- ing the AUC (Table 1a). Since the DNN algorithm was designed to make a rhythm class prediction approximately once per second (see Methods), we report performance both as assessed once every second—which we call “sequence-level” and consists of one rhythm class per interval—and once per record, which we call “set-level” scores on the 10% development dataset (n=8,761) were materially unchanged from the test dataset results, although they were slightly higher (Supplementary Tables 3 and 4). In addition, we retrained the DNN holding out an additional 10% of the training dataset as a second held-out test dataset (n=8,768); the AUC and F1 scores for all rhythms were materially unchanged (Supplementary Tables 5 and 6). We note that unlike the primary test dataset, which has gold- standard annotations from a committee of cardiologists, both sensi- tivity analysis datasets are annotated by certified ECG technicians. Table 1 | Diagnostic performance of the DNN and averaged individual cardiologists compared to the cardiologist committee consensus (n=328) Algorithm AUC (95% CI)a Algorithm F1 b Average cardiologist F1 Sequencea Setb Sequence Set Sequence Set Atrial fibrillation and flutter 0.973 (0.966–0.980) 0.965 (0.932–0.998) 0.801 0.831 0.677 0.686 AVB 0.988 (0.983–0.993) 0.981 (0.953–1.000) 0.828 0.808 0.772 0.761 Bigeminy 0.997 (0.991–1.000) 0.996 (0.976–1.000) 0.847 0.870 0.842 0.853 EAR 0.913 (0.889–0.937) 0.940 (0.870–1.000) 0.541 0.596 0.482 0.536 IVR 0.995 (0.989–1.000) 0.987 (0.959–1.000) 0.761 0.818 0.632 0.720 Junctional rhythm 0.987 (0.980–0.993) 0.979 (0.946–1.000) 0.664 0.789 0.692 0.679 Noise 0.981 (0.973–0.989) 0.947 (0.898–0.996) 0.844 0.761 0.768 0.685 Sinus rhythm 0.975 (0.971–0.979) 0.987 (0.976–0.998) 0.887 0.933 0.852 0.910 SVT 0.973 (0.960–0.985) 0.953 (0.903–1.000) 0.488 0.693 0.451 0.564 Trigeminy 0.998 (0.995–1.000) 0.997 (0.979–1.000) 0.907 0.864 0.842 0.812 Ventricular tachycardia 0.995 (0.980–1.000) 0.980 (0.934–1.000) 0.541 0.681 0.566 0.769 Wenckebach 0.978 (0.967–0.989) 0.977 (0.938–1.000) 0.702 0.780 0.591 0.738 Frequency-weighted average 0.978 0.977 0.807 0.837 0.753 0.780 a DNN algorithm area under the ROC compared to the cardiologist committee consensus. b DNN algorithm and averaged individual cardiologist F1 scores compared to the cardiologist committee consensus. Sequence-level describes the algorithm predictions that are made once every 256 input samples (approximately every 1.3s) and are compared against the gold-standard committee consensus at the same intervals. Set-level describes the unique set of algorithm predictions that are present in the 30-s record. Sequence AUC prediction, n=7,544; set AUC prediction, n=328. LETTERS | FOCUS https://doi.org/10.1038/s41591-018-0268-3LETTERS | FOCUS NATURE MEDICINE •Set level average F1 score: 전반적으로 인공지능이 더 나은 퍼포먼스 •DNN (0.837) > cardiologist (0.780) •DNN과 cardiologist 는 비슷한 추이의 F1 score를 보임 •VT, EAR 등에 대해서는 모두 낮음
- 246. ehensive demonstration ofa deep classification across a broad range rtant ECG rhythm diagnoses. Our hted AUC of 0.97, with higher aver- than cardiologists. These findings DNN approach has the potential acy of algorithmic ECG interpreta- mputational advances compel us to to automated ECG interpretation. aches whose performance improves uch as deep learning2 , can leverage CG data and provide clear oppor- ideal of a learning health care sys- this study of a dataset large enough learning approach to predict mul- nd our validation against the high sus committee. (Most cardiologists bnormalities.) We believe this is the ndard, since cardiologists perform y all clinical settings. the paradigm shift represented by nable a new approach to automated oach to automated ECG interpreta- across a series of steps that include raction, feature selection/reduction, hand-engineered heuristics and deri- developed with the ultimate aim to rhythm, such as atrial fibrillation31,32 . In contrast, DNNs enable an approach that is fundamentally different since a single algorithm can accomplish all of these steps ‘end-to-end’ without requiring class-specific feature extraction; in other words, the DNN can accept the raw ECG data as input and output diagnostic Table 2 | DNN algorithm and cardiologist sensitivity compared to the cardiologist committee consensus, with specificity fixed at the average specificity level achieved by cardiologists Specificity Average cardiologist sensitivity DNN algorithm sensitivity Atrial fibrillation and flutter 0.941 0.710 0.861 AVB 0.981 0.731 0.858 Bigeminy 0.996 0.829 0.921 EAR 0.993 0.380 0.445 IVR 0.991 0.611 0.867 Junctional rhythm 0.984 0.634 0.729 Noise 0.983 0.749 0.803 Sinus rhythm 0.859 0.901 0.950 SVT 0.983 0.408 0.487 Ventricular tachycardia 0.996 0.652 0.702 Wenckebach 0.986 0.541 0.651 raged cardiologist performance is indicated by the green dot. The line represents the ROC (a) or precision-recall curve 7,544 where each of the 328 30-s ECGs received 23 sequence-level predictions. 2019 | 65–69 | www.nature.com/naturemedicine 67 • Cardiologist 와 DNN의 sensitivity 비교 • DNN의 경우: specificity를 cardiologist와 동일하게 설정한 경우의 sensitivity • 12 종류의 부정맥 모두에 DNN이 더 높은 sensitivity를 보임
- 247. •복잡한 의료 데이터의분석 및 insight 도출 •영상 의료/병리 데이터의 분석/판독 •연속 데이터의 모니터링 및 예방/예측 의료 인공지능의 세 유형
- 248. 디지털 헬스케어의 3단계 •Step1. 데이터의 측정 •Step 2. 데이터의 통합 •Step 3. 데이터의 분석
- 250. Feedback/Questions • E-mail: yoonsup.choi@gmail.com • Blog: http://www.yoonsupchoi.com • Facebook: 최윤섭 디지털 헬스케어 연구소