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의료의 미래, 디지털 헬스케어 + 의료 시장의 특성

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의료의 미래, 디지털 헬스케어 + 의료 시장의 특성

  1. 1. 의료의 미래, 디지털 헬스케어 Professor, SAHIST, Sungkyunkwan University Director, Digital Healthcare Institute Yoon Sup Choi, Ph.D.
  2. 2. “It's in Apple's DNA that technology alone is not enough. 
 It's technology married with liberal arts.”
  3. 3. The Convergence of IT, BT and Medicine
  4. 4. 최윤섭 지음 의료인공지능 표지디자인•최승협 컴퓨터 털 헬 치를 만드는 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
  5. 5. Inevitable Tsunami of Change
  6. 6. 대한영상의학회 춘계학술대회 2017.6
  7. 7. “Technology will replace 80% of doctors”
  8. 8. https://www.youtube.com/watch?time_continue=70&v=2HMPRXstSvQ “영상의학과 전문의를 양성하는 것을 당장 그만둬야 한다. 5년 안에 딥러닝이 영상의학과 전문의를 능가할 것은 자명하다.” Hinton on Radiology
  9. 9. https://rockhealth.com/reports/2018-year-end-funding-report-is-digital-health-in-a-bubble/ •2018년에는 $8.1B 가 투자되며 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
  10. 10. 2010 2011 2012 2013 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
  11. 11. 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
  12. 12. startuphealth.com/reports Firm 2017 YTD Deals 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.
  13. 13. •최근 3년 동안 Merck, 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
  14. 14. 헬스케어 넓은 의미의 건강 관리에는 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 암유전체, 질병위험도, 보인자, 약물 민감도 예) 웰니스, 조상 분석 헬스케어 관련 분야 구성도(ver 0.6) 의료 질병 예방, 치료, 처방, 관리 등 전문 의료 영역 원격의료 원격 환자 모니터링 원격진료 전화, 화상, 판독 디지털 치료제 당뇨 예방 앱 중독 치료 앱 ADHD 치료게임
  15. 15. 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
  16. 16. What is most important factor in digital medicine?
  17. 17. “Data! Data! Data!” he cried.“I can’t make bricks without clay!” - Sherlock Holmes,“The Adventure of the Copper Beeches”
  18. 18. 새로운 데이터가 새로운 방식으로 새로운 주체에 의해 측정, 저장, 통합, 분석된다. 데이터의 종류 데이터의 질적/양적 측면 웨어러블 기기 스마트폰 유전 정보 분석 인공지능 SNS 사용자/환자 대중
  19. 19. 디지털 헬스케어의 3단계 •Step 1. 데이터의 측정 •Step 2. 데이터의 통합 •Step 3. 데이터의 분석
  20. 20. Digital Healthcare Industry Landscape 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
  21. 21. Data Measurement Data Integration 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)
  22. 22. Step 1. 데이터의 측정
  23. 23. Smartphone: the origin of healthcare innovation
  24. 24. Smartphone: the origin of healthcare innovation
  25. 25. 2013? The election of Pope Benedict The Election of Pope Francis
  26. 26. The Election of Pope Francis The Election of Pope Benedict
  27. 27. Sci Transl Med 2015
  28. 28. 검이경 더마토스코프 안과질환 피부암 기생충 호흡기 심전도 수면 식단 활동량 발열 생리/임신
  29. 29. CellScope’s iPhone-enabled otoscope
  30. 30. CellScope’s iPhone-enabled otoscope
  31. 31. 한국에서는 불법한국에서는 불법
  32. 32. “왼쪽 귀에 대한 비디오를 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
  33. 33. First Derm 한국에서는 불법한국에서는 불법
  34. 34. SpiroSmart: spirometer using iPhone
  35. 35. AliveCor Heart Monitor (Kardia)
  36. 36. AliveCor Heart Monitor (Kardia)
  37. 37. “심장박동은 안정적이기 때문에, 
 당장 병원에 갈 필요는 없겠습니다. 
 그래도 이상이 있으면 전문의에게 
 진료를 받아보세요. “ 한국에서는 불법한국에서는 불법
  38. 38. 2015년 2017년
  39. 39. 30분-1시간 정도 일상적인 코골이가 있음 이걸 어떻게 믿나?
  40. 40. 녹음을 해줌. PGS와의 analytical validity의 증명?
  41. 41. 녹음을 해줌. PGS와의 analytical validity의 증명?
  42. 42. PGHD Patients Generated Health Data
  43. 43. Wearable Devices
  44. 44. http://www.rolls-royce.com/about/our-technology/enabling-technologies/engine-health-management.aspx#sense 250 sensors to monitor the “health” of the GE turbines
  45. 45. 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
  46. 46. Hype or Hope? Source: Gartner
  47. 47. Fitbit
  48. 48. Apple Watch
  49. 49. 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)
  50. 50. •American College of Cardiology’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%)
  51. 51. It’s already here.
  52. 52. Google’s Smart Contact Lens
  53. 53. Ingestible Sensor, Proteus Digital Health
  54. 54. Ingestible Sensor, Proteus Digital Health
  55. 55. 헬스케어 웨어러블 딜레마 지속 사용성 사용자 효용 당뇨병 패러독스 NO 행동을 변화시켜야 하는가? 재정적 효용 의료적 효용 오락적 효용 “돈을 준다”“병이 낫는다” “재미있다” 정확성 정확성만으로 계속 사용하지는 않는다 의료적 사용을 위해서는 정확해야 한다 “계속 사용하는가” “쓰면 뭐가 좋은가” YES 효용이 번거로움을 크게 능가하는가? NO YES 일단 사용을 해야만 효용을 기대할 수 있다 “쓰면 좋은 걸 알지만, 그래도 안 쓴다” 최윤섭디지털헬스케어연구소 소장 최윤섭, PhD yoonsup.choi@gmail.com www.yoonsupchoi.com 심미적 효용 “예쁘다” 사회적 효용 “친구를 사귄다” 편의적 효용 “결제가 쉽다” 보험사가 참고하려면 정확해야 한다
  56. 56. n n- ng n es h- n ne ne ct d n- at s- or e, ts n a- gs d ch Nat Biotech 2015
  57. 57. Personal Genome Analysis
  58. 58. 가타카 (1997)
  59. 59. 가타카 (1997)
  60. 60. 2003 Human Genome Project 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
  61. 61. The $1000 Genome is Already Here!
  62. 62. • 2017년 1월 NovaSeq 5000, 6000 발표 • 몇년 내로 $100로 WES 를 실현하겠다고 공언 • 2일에 60명의 WES 가능 (한 명당 한 시간 이하)
  63. 63. Results within 6-8 weeksA little spit is all it takes! DTC Genetic TestingDirect-To-Consumer
  64. 64. Health Risks
  65. 65. Health Risks
  66. 66. Health Risks
  67. 67. Drug Response
  68. 68. Traits 음주 후 얼굴이 붉어지는가 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
  69. 69. Ancestry Composition
  70. 70. 1,000,000 2,000,000 2007-11 2011-06 2011-10 2012-04 2012-10 2013-04 2013-06 2013-09 2013-12 2014-10 2015-02 2015-06 2016-02 2017-04 2017-11 2018-04 3,000,000 5,000,000 2019-03 10,000,000 Customer growth of 23andMe
  71. 71. 23andMe Chronicle $115m 펀딩 (유니콘 등극) 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
  72. 72. •질병 위험도 유전자 분석 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
  73. 73. 한국 DTC 유전정보 분석 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
  74. 74. https://www.23andme.com/slideshow/research/ 고객의 자발적인 참여에 의한 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!
  75. 75. January 13, 2015January 6, 2015 Data Business
  76. 76. Step1. 데이터의 측정 •스마트폰 •웨어러블 디바이스 •개인 유전 정보 분석 환자 유래의 의료 데이터 (PGHD)
  77. 77. Step 2. 데이터의 통합
  78. 78. Sci Transl Med 2015
  79. 79. Google Fit
  80. 80. Samsung SAMI
  81. 81. Epic MyChart Epic EHR Dexcom CGM Patients/User Devices EH Hospit Whitings + Apple Watch Apps HealthKit
  82. 82. Hospital B Hospital C Hospital A
  83. 83. Hospital A Hospital B Hospital C interoperability
  84. 84. Hospital B Hospital C Hospital A
  85. 85. •2018년 1월에 출시 당시, 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
  86. 86. Step 3. 데이터의 분석
  87. 87. Data Overload
  88. 88. How to Analyze and Interpret the Big Data?
  89. 89. and/or Two ways to get insights from the big data
  90. 90. 원격의료 • ‘명시적’으로, ‘전면적’으로 ‘금지’된 곳은 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?
  91. 91. 원격 의료 원격 진료 원격 환자 모니터링 화상 진료 전화 진료 2차 소견 용어 정리 데이터 판독 원격 수술
  92. 92. •원격 진료: 화상 진료 •원격 진료: 2차 소견 •원격 진료: 애플리케이션 •원격 환자 모니터링 원격 의료에도 종류가 많다.
  93. 93. •원격 진료: 화상 진료 •원격 진료: 2차 소견 •원격 진료: 애플리케이션 •원격 환자 모니터링 원격 의료에도 종류가 많다.
  94. 94. Telemedicine
  95. 95. Average Time to Appointment (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
  96. 96. 0 125 250 375 500 2013 2014 2015 2016 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
  97. 97. •원격 진료: 화상 진료 •원격 진료: 2차 소견 •원격 진료: 애플리케이션 •원격 환자 모니터링 원격 의료에도 종류가 많다.
  98. 98. Epic MyChart Epic EHR Dexcom CGM Patients/User Devices EHR Hospital Whitings + Apple Watch Apps HealthKit
  99. 99. transfer from Share2 to 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
  100. 100. 의료계 일각에서는 원격 환자 모니터링의 합법화를 요구하기도
  101. 101. No choice but to bring AI into the medicine
  102. 102. Martin Duggan,“IBM Watson Health - Integrated Care & the Evolution to Cognitive Computing”
  103. 103. Copyright 2016 American Medical Association. All rights reserved. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs Varun Gulshan, PhD; Lily Peng, MD, PhD; Marc Coram, PhD; Martin C. Stumpe, PhD; Derek Wu, BS; Arunachalam Narayanaswamy, PhD; Subhashini Venugopalan, MS; Kasumi Widner, MS; Tom Madams, MEng; Jorge Cuadros, OD, PhD; Ramasamy Kim, OD, DNB; Rajiv Raman, MS, DNB; Philip C. Nelson, BS; Jessica L. Mega, MD, MPH; Dale R. Webster, PhD IMPORTANCE Deep learning is a family of computational methods that allow an algorithm to program itself by learning from a large set of examples that demonstrate the desired behavior, removing the need to specify rules explicitly. Application of these methods to medical imaging requires further assessment and validation. OBJECTIVE To apply deep learning to create an algorithm for automated detection of diabetic retinopathy and diabetic macular edema in retinal fundus photographs. DESIGN AND SETTING A specific type of neural network optimized for image classification called a deep convolutional neural network was trained using a retrospective development data set of 128 175 retinal images, which were graded 3 to 7 times for diabetic retinopathy, diabetic macular edema, and image gradability by a panel of 54 US licensed ophthalmologists and ophthalmology senior residents between May and December 2015. The resultant algorithm was validated in January and February 2016 using 2 separate data sets, both graded by at least 7 US board-certified ophthalmologists with high intragrader consistency. EXPOSURE Deep learning–trained algorithm. MAIN OUTCOMES AND MEASURES The sensitivity and specificity of the algorithm for detecting referable diabetic retinopathy (RDR), defined as moderate and worse diabetic retinopathy, referable diabetic macular edema, or both, were generated based on the reference standard of the majority decision of the ophthalmologist panel. The algorithm was evaluated at 2 operating points selected from the development set, one selected for high specificity and another for high sensitivity. RESULTS TheEyePACS-1datasetconsistedof9963imagesfrom4997patients(meanage,54.4 years;62.2%women;prevalenceofRDR,683/8878fullygradableimages[7.8%]);the Messidor-2datasethad1748imagesfrom874patients(meanage,57.6years;42.6%women; prevalenceofRDR,254/1745fullygradableimages[14.6%]).FordetectingRDR,thealgorithm hadanareaunderthereceiveroperatingcurveof0.991(95%CI,0.988-0.993)forEyePACS-1and 0.990(95%CI,0.986-0.995)forMessidor-2.Usingthefirstoperatingcutpointwithhigh specificity,forEyePACS-1,thesensitivitywas90.3%(95%CI,87.5%-92.7%)andthespecificity was98.1%(95%CI,97.8%-98.5%).ForMessidor-2,thesensitivitywas87.0%(95%CI,81.1%- 91.0%)andthespecificitywas98.5%(95%CI,97.7%-99.1%).Usingasecondoperatingpoint withhighsensitivityinthedevelopmentset,forEyePACS-1thesensitivitywas97.5%and specificitywas93.4%andforMessidor-2thesensitivitywas96.1%andspecificitywas93.9%. CONCLUSIONS AND RELEVANCE In this evaluation of retinal fundus photographs from adults with diabetes, an algorithm based on deep machine learning had high sensitivity and specificity for detecting referable diabetic retinopathy. Further research is necessary to determine the feasibility of applying this algorithm in the clinical setting and to determine whether use of the algorithm could lead to improved care and outcomes compared with current ophthalmologic assessment. JAMA. doi:10.1001/jama.2016.17216 Published online November 29, 2016. Editorial Supplemental content Author Affiliations: Google Inc, Mountain View, California (Gulshan, Peng, Coram, Stumpe, Wu, Narayanaswamy, Venugopalan, Widner, Madams, Nelson, Webster); Department of Computer Science, University of Texas, Austin (Venugopalan); EyePACS LLC, San Jose, California (Cuadros); School of Optometry, Vision Science Graduate Group, University of California, Berkeley (Cuadros); Aravind Medical Research Foundation, Aravind Eye Care System, Madurai, India (Kim); Shri Bhagwan Mahavir Vitreoretinal Services, Sankara Nethralaya, Chennai, Tamil Nadu, India (Raman); Verily Life Sciences, Mountain View, California (Mega); Cardiovascular Division, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts (Mega). Corresponding Author: Lily Peng, MD, PhD, Google Research, 1600 Amphitheatre Way, Mountain View, CA 94043 (lhpeng@google.com). Research JAMA | Original Investigation | INNOVATIONS IN HEALTH CARE DELIVERY (Reprinted) E1 Copyright 2016 American Medical Association. All rights reserved. Downloaded From: http://jamanetwork.com/ on 12/02/2016 안과 LETTERS https://doi.org/10.1038/s41591-018-0335-9 1 Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou, China. 2 Institute for Genomic Medicine, Institute of Engineering in Medicine, and Shiley Eye Institute, University of California, San Diego, La Jolla, CA, USA. 3 Hangzhou YITU Healthcare Technology Co. Ltd, Hangzhou, China. 4 Department of Thoracic Surgery/Oncology, First Affiliated Hospital of Guangzhou Medical University, China State Key Laboratory and National Clinical Research Center for Respiratory Disease, Guangzhou, China. 5 Guangzhou Kangrui Co. Ltd, Guangzhou, China. 6 Guangzhou Regenerative Medicine and Health Guangdong Laboratory, Guangzhou, China. 7 Veterans Administration Healthcare System, San Diego, CA, USA. 8 These authors contributed equally: Huiying Liang, Brian Tsui, Hao Ni, Carolina C. S. Valentim, Sally L. Baxter, Guangjian Liu. *e-mail: kang.zhang@gmail.com; xiahumin@hotmail.com Artificial intelligence (AI)-based methods have emerged as powerful tools to transform medical care. Although machine learning classifiers (MLCs) have already demonstrated strong performance in image-based diagnoses, analysis of diverse and massive electronic health record (EHR) data remains chal- lenging. Here, we show that MLCs can query EHRs in a manner similar to the hypothetico-deductive reasoning used by physi- cians and unearth associations that previous statistical meth- ods have not found. Our model applies an automated natural language processing system using deep learning techniques to extract clinically relevant information from EHRs. In total, 101.6 million data points from 1,362,559 pediatric patient visits presenting to a major referral center were analyzed to train and validate the framework. Our model demonstrates high diagnostic accuracy across multiple organ systems and is comparable to experienced pediatricians in diagnosing com- mon childhood diseases. Our study provides a proof of con- cept for implementing an AI-based system as a means to aid physicians in tackling large amounts of data, augmenting diag- nostic evaluations, and to provide clinical decision support in cases of diagnostic uncertainty or complexity. Although this impact may be most evident in areas where healthcare provid- ers are in relative shortage, the benefits of such an AI system are likely to be universal. Medical information has become increasingly complex over time. The range of disease entities, diagnostic testing and biomark- ers, and treatment modalities has increased exponentially in recent years. Subsequently, clinical decision-making has also become more complex and demands the synthesis of decisions from assessment of large volumes of data representing clinical information. In the current digital age, the electronic health record (EHR) represents a massive repository of electronic data points representing a diverse array of clinical information1–3 . Artificial intelligence (AI) methods have emerged as potentially powerful tools to mine EHR data to aid in disease diagnosis and management, mimicking and perhaps even augmenting the clinical decision-making of human physicians1 . To formulate a diagnosis for any given patient, physicians fre- quently use hypotheticodeductive reasoning. Starting with the chief complaint, the physician then asks appropriately targeted questions relating to that complaint. From this initial small feature set, the physician forms a differential diagnosis and decides what features (historical questions, physical exam findings, laboratory testing, and/or imaging studies) to obtain next in order to rule in or rule out the diagnoses in the differential diagnosis set. The most use- ful features are identified, such that when the probability of one of the diagnoses reaches a predetermined level of acceptability, the process is stopped, and the diagnosis is accepted. It may be pos- sible to achieve an acceptable level of certainty of the diagnosis with only a few features without having to process the entire feature set. Therefore, the physician can be considered a classifier of sorts. In this study, we designed an AI-based system using machine learning to extract clinically relevant features from EHR notes to mimic the clinical reasoning of human physicians. In medicine, machine learning methods have already demonstrated strong per- formance in image-based diagnoses, notably in radiology2 , derma- tology4 , and ophthalmology5–8 , but analysis of EHR data presents a number of difficult challenges. These challenges include the vast quantity of data, high dimensionality, data sparsity, and deviations Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence Huiying Liang1,8 , Brian Y. Tsui 2,8 , Hao Ni3,8 , Carolina C. S. Valentim4,8 , Sally L. Baxter 2,8 , Guangjian Liu1,8 , Wenjia Cai 2 , Daniel S. Kermany1,2 , Xin Sun1 , Jiancong Chen2 , Liya He1 , Jie Zhu1 , Pin Tian2 , Hua Shao2 , Lianghong Zheng5,6 , Rui Hou5,6 , Sierra Hewett1,2 , Gen Li1,2 , Ping Liang3 , Xuan Zang3 , Zhiqi Zhang3 , Liyan Pan1 , Huimin Cai5,6 , Rujuan Ling1 , Shuhua Li1 , Yongwang Cui1 , Shusheng Tang1 , Hong Ye1 , Xiaoyan Huang1 , Waner He1 , Wenqing Liang1 , Qing Zhang1 , Jianmin Jiang1 , Wei Yu1 , Jianqun Gao1 , Wanxing Ou1 , Yingmin Deng1 , Qiaozhen Hou1 , Bei Wang1 , Cuichan Yao1 , Yan Liang1 , Shu Zhang1 , Yaou Duan2 , Runze Zhang2 , Sarah Gibson2 , Charlotte L. Zhang2 , Oulan Li2 , Edward D. Zhang2 , Gabriel Karin2 , Nathan Nguyen2 , Xiaokang Wu1,2 , Cindy Wen2 , Jie Xu2 , Wenqin Xu2 , Bochu Wang2 , Winston Wang2 , Jing Li1,2 , Bianca Pizzato2 , Caroline Bao2 , Daoman Xiang1 , Wanting He1,2 , Suiqin He2 , Yugui Zhou1,2 , Weldon Haw2,7 , Michael Goldbaum2 , Adriana Tremoulet2 , Chun-Nan Hsu 2 , Hannah Carter2 , Long Zhu3 , Kang Zhang 1,2,7 * and Huimin Xia 1 * NATURE MEDICINE | www.nature.com/naturemedicine 소아청소년과 ARTICLES https://doi.org/10.1038/s41591-018-0177-5 1 Applied Bioinformatics Laboratories, New York University School of Medicine, New York, NY, USA. 2 Skirball Institute, Department of Cell Biology, New York University School of Medicine, New York, NY, USA. 3 Department of Pathology, New York University School of Medicine, New York, NY, USA. 4 School of Mechanical Engineering, National Technical University of Athens, Zografou, Greece. 5 Institute for Systems Genetics, New York University School of Medicine, New York, NY, USA. 6 Department of Biochemistry and Molecular Pharmacology, New York University School of Medicine, New York, NY, USA. 7 Center for Biospecimen Research and Development, New York University, New York, NY, USA. 8 Department of Population Health and the Center for Healthcare Innovation and Delivery Science, New York University School of Medicine, New York, NY, USA. 9 These authors contributed equally to this work: Nicolas Coudray, Paolo Santiago Ocampo. *e-mail: narges.razavian@nyumc.org; aristotelis.tsirigos@nyumc.org A ccording to the American Cancer Society and the Cancer Statistics Center (see URLs), over 150,000 patients with lung cancer succumb to the disease each year (154,050 expected for 2018), while another 200,000 new cases are diagnosed on a yearly basis (234,030 expected for 2018). It is one of the most widely spread cancers in the world because of not only smoking, but also exposure to toxic chemicals like radon, asbestos and arsenic. LUAD and LUSC are the two most prevalent types of non–small cell lung cancer1 , and each is associated with discrete treatment guidelines. In the absence of definitive histologic features, this important distinc- tion can be challenging and time-consuming, and requires confir- matory immunohistochemical stains. Classification of lung cancer type is a key diagnostic process because the available treatment options, including conventional chemotherapy and, more recently, targeted therapies, differ for LUAD and LUSC2 . Also, a LUAD diagnosis will prompt the search for molecular biomarkers and sensitizing mutations and thus has a great impact on treatment options3,4 . For example, epidermal growth factor receptor (EGFR) mutations, present in about 20% of LUAD, and anaplastic lymphoma receptor tyrosine kinase (ALK) rearrangements, present in<5% of LUAD5 , currently have tar- geted therapies approved by the Food and Drug Administration (FDA)6,7 . Mutations in other genes, such as KRAS and tumor pro- tein P53 (TP53) are very common (about 25% and 50%, respec- tively) but have proven to be particularly challenging drug targets so far5,8 . Lung biopsies are typically used to diagnose lung cancer type and stage. Virtual microscopy of stained images of tissues is typically acquired at magnifications of 20×to 40×, generating very large two-dimensional images (10,000 to>100,000 pixels in each dimension) that are oftentimes challenging to visually inspect in an exhaustive manner. Furthermore, accurate interpretation can be difficult, and the distinction between LUAD and LUSC is not always clear, particularly in poorly differentiated tumors; in this case, ancil- lary studies are recommended for accurate classification9,10 . To assist experts, automatic analysis of lung cancer whole-slide images has been recently studied to predict survival outcomes11 and classifica- tion12 . For the latter, Yu et al.12 combined conventional thresholding and image processing techniques with machine-learning methods, such as random forest classifiers, support vector machines (SVM) or Naive Bayes classifiers, achieving an AUC of ~0.85 in distinguishing normal from tumor slides, and ~0.75 in distinguishing LUAD from LUSC slides. More recently, deep learning was used for the classi- fication of breast, bladder and lung tumors, achieving an AUC of 0.83 in classification of lung tumor types on tumor slides from The Cancer Genome Atlas (TCGA)13 . Analysis of plasma DNA values was also shown to be a good predictor of the presence of non–small cell cancer, with an AUC of ~0.94 (ref. 14 ) in distinguishing LUAD from LUSC, whereas the use of immunochemical markers yields an AUC of ~0.94115 . Here, we demonstrate how the field can further benefit from deep learning by presenting a strategy based on convolutional neural networks (CNNs) that not only outperforms methods in previously Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning Nicolas Coudray 1,2,9 , Paolo Santiago Ocampo3,9 , Theodore Sakellaropoulos4 , Navneet Narula3 , Matija Snuderl3 , David Fenyö5,6 , Andre L. Moreira3,7 , Narges Razavian 8 * and Aristotelis Tsirigos 1,3 * Visual inspection of histopathology slides is one of the main methods used by pathologists to assess the stage, type and sub- type of lung tumors. Adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) are the most prevalent subtypes of lung cancer, and their distinction requires visual inspection by an experienced pathologist. In this study, we trained a deep con- volutional neural network (inception v3) on whole-slide images obtained from The Cancer Genome Atlas to accurately and automatically classify them into LUAD, LUSC or normal lung tissue. The performance of our method is comparable to that of pathologists, with an average area under the curve (AUC) of 0.97. Our model was validated on independent datasets of frozen tissues, formalin-fixed paraffin-embedded tissues and biopsies. Furthermore, we trained the network to predict the ten most commonly mutated genes in LUAD. We found that six of them—STK11, EGFR, FAT1, SETBP1, KRAS and TP53—can be pre- dicted from pathology images, with AUCs from 0.733 to 0.856 as measured on a held-out population. These findings suggest that deep-learning models can assist pathologists in the detection of cancer subtype or gene mutations. Our approach can be applied to any cancer type, and the code is available at https://github.com/ncoudray/DeepPATH. NATURE MEDICINE | www.nature.com/naturemedicine 병리과병리과병리과병리과병리과병리과병리과 ARTICLES https://doi.org/10.1038/s41551-018-0301-3 1 Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, Chengdu, China. 2 Shanghai Wision AI Co., Ltd, Shanghai, China. 3 Beth Israel Deaconess Medical Center and Harvard Medical School, Center for Advanced Endoscopy, Boston , MA, USA. *e-mail: gary.samsph@gmail.com C olonoscopy is the gold-standard screening test for colorectal cancer1–3 , one of the leading causes of cancer death in both the United States4,5 and China6 . Colonoscopy can reduce the risk of death from colorectal cancer through the detection of tumours at an earlier, more treatable stage as well as through the removal of precancerous adenomas3,7 . Conversely, failure to detect adenomas may lead to the development of interval cancer. Evidence has shown that each 1.0% increase in adenoma detection rate (ADR) leads to a 3.0% decrease in the risk of interval colorectal cancer8 . Although more than 14million colonoscopies are performed in the United States annually2 , the adenoma miss rate (AMR) is estimated to be 6–27%9 . Certain polyps may be missed more fre- quently, including smaller polyps10,11 , flat polyps12 and polyps in the left colon13 . There are two independent reasons why a polyp may be missed during colonoscopy: (i) it was never in the visual field or (ii) it was in the visual field but not recognized. Several hardware innovations have sought to address the first problem by improv- ing visualization of the colonic lumen, for instance by providing a larger, panoramic camera view, or by flattening colonic folds using a distal-cap attachment. The problem of unrecognized polyps within the visual field has been more difficult to address14 . Several studies have shown that observation of the video monitor by either nurses or gastroenterology trainees may increase polyp detection by up to 30%15–17 . Ideally, a real-time automatic polyp-detection system could serve as a similarly effective second observer that could draw the endoscopist’s eye, in real time, to concerning lesions, effec- tively creating an ‘extra set of eyes’ on all aspects of the video data with fidelity. Although automatic polyp detection in colonoscopy videos has been an active research topic for the past 20 years, per- formance levels close to that of the expert endoscopist18–20 have not been achieved. Early work in automatic polyp detection has focused on applying deep-learning techniques to polyp detection, but most published works are small in scale, with small development and/or training validation sets19,20 . Here, we report the development and validation of a deep-learn- ing algorithm, integrated with a multi-threaded processing system, for the automatic detection of polyps during colonoscopy. We vali- dated the system in two image studies and two video studies. Each study contained two independent validation datasets. Results We developed a deep-learning algorithm using 5,545colonoscopy images from colonoscopy reports of 1,290patients that underwent a colonoscopy examination in the Endoscopy Center of Sichuan Provincial People’s Hospital between January 2007 and December 2015. Out of the 5,545images used, 3,634images contained polyps (65.54%) and 1,911 images did not contain polyps (34.46%). For algorithm training, experienced endoscopists annotated the pres- ence of each polyp in all of the images in the development data- set. We validated the algorithm on four independent datasets. DatasetsA and B were used for image analysis, and datasetsC and D were used for video analysis. DatasetA contained 27,113colonoscopy images from colo- noscopy reports of 1,138consecutive patients who underwent a colonoscopy examination in the Endoscopy Center of Sichuan Provincial People’s Hospital between January and December 2016 and who were found to have at least one polyp. Out of the 27,113 images, 5,541images contained polyps (20.44%) and 21,572images did not contain polyps (79.56%). All polyps were confirmed histo- logically after biopsy. DatasetB is a public database (CVC-ClinicDB; Development and validation of a deep-learning algorithm for the detection of polyps during colonoscopy Pu Wang1 , Xiao Xiao2 , Jeremy R. Glissen Brown3 , Tyler M. Berzin 3 , Mengtian Tu1 , Fei Xiong1 , Xiao Hu1 , Peixi Liu1 , Yan Song1 , Di Zhang1 , Xue Yang1 , Liangping Li1 , Jiong He2 , Xin Yi2 , Jingjia Liu2 and Xiaogang Liu 1 * The detection and removal of precancerous polyps via colonoscopy is the gold standard for the prevention of colon cancer. However, the detection rate of adenomatous polyps can vary significantly among endoscopists. Here, we show that a machine- learningalgorithmcandetectpolypsinclinicalcolonoscopies,inrealtimeandwithhighsensitivityandspecificity.Wedeveloped the deep-learning algorithm by using data from 1,290 patients, and validated it on newly collected 27,113 colonoscopy images from 1,138 patients with at least one detected polyp (per-image-sensitivity, 94.38%; per-image-specificity, 95.92%; area under the receiver operating characteristic curve, 0.984), on a public database of 612 polyp-containing images (per-image-sensitiv- ity, 88.24%), on 138 colonoscopy videos with histologically confirmed polyps (per-image-sensitivity of 91.64%; per-polyp-sen- sitivity, 100%), and on 54 unaltered full-range colonoscopy videos without polyps (per-image-specificity, 95.40%). By using a multi-threaded processing system, the algorithm can process at least 25 frames per second with a latency of 76.80±5.60ms in real-time video analysis. The software may aid endoscopists while performing colonoscopies, and help assess differences in polyp and adenoma detection performance among endoscopists. NATURE BIOMEDICA L ENGINEERING | VOL 2 | OCTOBER 2018 | 741–748 | www.nature.com/natbiomedeng 741 소화기내과 1Wang P, et al. Gut 2019;0:1–7. doi:10.1136/gutjnl-2018-317500 Endoscopy ORIGINAL ARTICLE Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: a prospective randomised controlled study Pu Wang,  1 Tyler M Berzin,  2 Jeremy Romek Glissen Brown,  2 Shishira Bharadwaj,2 Aymeric Becq,2 Xun Xiao,1 Peixi Liu,1 Liangping Li,1 Yan Song,1 Di Zhang,1 Yi Li,1 Guangre Xu,1 Mengtian Tu,1 Xiaogang Liu  1 To cite: Wang P, Berzin TM, Glissen Brown JR, et al. Gut Epub ahead of print: [please include Day Month Year]. doi:10.1136/ gutjnl-2018-317500 ► Additional material is published online only.To view please visit the journal online (http://dx.doi.org/10.1136/ gutjnl-2018-317500). 1 Department of Gastroenterology, Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, Chengdu, China 2 Center for Advanced Endoscopy, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USA Correspondence to Xiaogang Liu, Department of Gastroenterology Sichuan Academy of Medical Sciences and Sichuan Provincial People’s Hospital, Chengdu, China; Gary.samsph@gmail.com Received 30 August 2018 Revised 4 February 2019 Accepted 13 February 2019 © Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. ABSTRACT Objective The effect of colonoscopy on colorectal cancer mortality is limited by several factors, among them a certain miss rate, leading to limited adenoma detection rates (ADRs).We investigated the effect of an automatic polyp detection system based on deep learning on polyp detection rate and ADR. Design In an open, non-blinded trial, consecutive patients were prospectively randomised to undergo diagnostic colonoscopy with or without assistance of a real-time automatic polyp detection system providing a simultaneous visual notice and sound alarm on polyp detection.The primary outcome was ADR. Results Of 1058 patients included, 536 were randomised to standard colonoscopy, and 522 were randomised to colonoscopy with computer-aided diagnosis.The artificial intelligence (AI) system significantly increased ADR (29.1%vs20.3%, p<0.001) and the mean number of adenomas per patient (0.53vs0.31, p<0.001).This was due to a higher number of diminutive adenomas found (185vs102; p<0.001), while there was no statistical difference in larger adenomas (77vs58, p=0.075). In addition, the number of hyperplastic polyps was also significantly increased (114vs52, p<0.001). Conclusions In a low prevalent ADR population, an automatic polyp detection system during colonoscopy resulted in a significant increase in the number of diminutive adenomas detected, as well as an increase in the rate of hyperplastic polyps.The cost–benefit ratio of such effects has to be determined further. Trial registration number ChiCTR-DDD-17012221; Results. INTRODUCTION Colorectal cancer (CRC) is the second and third- leading causes of cancer-related deaths in men and women respectively.1 Colonoscopy is the gold stan- dard for screening CRC.2 3 Screening colonoscopy has allowed for a reduction in the incidence and mortality of CRC via the detection and removal of adenomatous polyps.4–8 Additionally, there is evidence that with each 1.0% increase in adenoma detection rate (ADR), there is an associated 3.0% decrease in the risk of interval CRC.9 10 However, polyps can be missed, with reported miss rates of up to 27% due to both polyp and operator charac- teristics.11 12 Unrecognised polyps within the visual field is an important problem to address.11 Several studies have shown that assistance by a second observer increases the polyp detection rate (PDR), but such a strategy remains controversial in terms of increasing the ADR.13–15 Ideally, a real-time automatic polyp detec- tion system, with performance close to that of expert endoscopists, could assist the endosco- pist in detecting lesions that might correspond to adenomas in a more consistent and reliable way Significance of this study What is already known on this subject? ► Colorectal adenoma detection rate (ADR) is regarded as a main quality indicator of (screening) colonoscopy and has been shown to correlate with interval cancers. Reducing adenoma miss rates by increasing ADR has been a goal of many studies focused on imaging techniques and mechanical methods. ► Artificial intelligence has been recently introduced for polyp and adenoma detection as well as differentiation and has shown promising results in preliminary studies. What are the new findings? ► This represents the first prospective randomised controlled trial examining an automatic polyp detection during colonoscopy and shows an increase of ADR by 50%, from 20% to 30%. ► This effect was mainly due to a higher rate of small adenomas found. ► The detection rate of hyperplastic polyps was also significantly increased. How might it impact on clinical practice in the foreseeable future? ► Automatic polyp and adenoma detection could be the future of diagnostic colonoscopy in order to achieve stable high adenoma detection rates. ► However, the effect on ultimate outcome is still unclear, and further improvements such as polyp differentiation have to be implemented. on17March2019byguest.Protectedbycopyright.http://gut.bmj.com/Gut:firstpublishedas10.1136/gutjnl-2018-317500on27February2019.Downloadedfrom 소화기내과 Downloadedfromhttps://journals.lww.com/ajspbyBhDMf5ePHKav1zEoum1tQfN4a+kJLhEZgbsIHo4XMi0hCywCX1AWnYQp/IlQrHD3MyLIZIvnCFZVJ56DGsD590P5lh5KqE20T/dBX3x9CoM=on10/14/2018 Downloadedfromhttps://journals.lww.com/ajspbyBhDMf5ePHKav1zEoum1tQfN4a+kJLhEZgbsIHo4XMi0hCywCX1AWnYQp/IlQrHD3MyLIZIvnCFZVJ56DGsD590P5lh5KqE20T/dBX3x9CoM=on10/14/2018 Impact of Deep Learning Assistance on the Histopathologic Review of Lymph Nodes for Metastatic Breast Cancer David F. Steiner, MD, PhD,* Robert MacDonald, PhD,* Yun Liu, PhD,* Peter Truszkowski, MD,* Jason D. Hipp, MD, PhD, FCAP,* Christopher Gammage, MS,* Florence Thng, MS,† Lily Peng, MD, PhD,* and Martin C. Stumpe, PhD* Abstract: Advances in the quality of whole-slide images have set the stage for the clinical use of digital images in anatomic pathology. Along with advances in computer image analysis, this raises the possibility for computer-assisted diagnostics in pathology to improve histopathologic interpretation and clinical care. To evaluate the potential impact of digital assistance on interpretation of digitized slides, we conducted a multireader multicase study utilizing our deep learning algorithm for the detection of breast cancer metastasis in lymph nodes. Six pathologists reviewed 70 digitized slides from lymph node sections in 2 reader modes, unassisted and assisted, with a wash- out period between sessions. In the assisted mode, the deep learning algorithm was used to identify and outline regions with high like- lihood of containing tumor. Algorithm-assisted pathologists demon- strated higher accuracy than either the algorithm or the pathologist alone. In particular, algorithm assistance significantly increased the sensitivity of detection for micrometastases (91% vs. 83%, P=0.02). In addition, average review time per image was significantly shorter with assistance than without assistance for both micrometastases (61 vs. 116 s, P=0.002) and negative images (111 vs. 137 s, P=0.018). Lastly, pathologists were asked to provide a numeric score regarding the difficulty of each image classification. On the basis of this score, pathologists considered the image review of micrometastases to be significantly easier when interpreted with assistance (P=0.0005). Utilizing a proof of concept assistant tool, this study demonstrates the potential of a deep learning algorithm to improve pathologist accu- racy and efficiency in a digital pathology workflow. Key Words: artificial intelligence, machine learning, digital pathology, breast cancer, computer aided detection (Am J Surg Pathol 2018;00:000–000) The regulatory approval and gradual implementation of whole-slide scanners has enabled the digitization of glass slides for remote consults and archival purposes.1 Digitiza- tion alone, however, does not necessarily improve the con- sistency or efficiency of a pathologist’s primary workflow. In fact, image review on a digital medium can be slightly slower than on glass, especially for pathologists with limited digital pathology experience.2 However, digital pathology and image analysis tools have already demonstrated po- tential benefits, including the potential to reduce inter-reader variability in the evaluation of breast cancer HER2 status.3,4 Digitization also opens the door for assistive tools based on Artificial Intelligence (AI) to improve efficiency and con- sistency, decrease fatigue, and increase accuracy.5 Among AI technologies, deep learning has demon- strated strong performance in many automated image-rec- ognition applications.6–8 Recently, several deep learning– based algorithms have been developed for the detection of breast cancer metastases in lymph nodes as well as for other applications in pathology.9,10 Initial findings suggest that some algorithms can even exceed a pathologist’s sensitivity for detecting individual cancer foci in digital images. How- ever, this sensitivity gain comes at the cost of increased false positives, potentially limiting the utility of such algorithms for automated clinical use.11 In addition, deep learning algo- rithms are inherently limited to the task for which they have been specifically trained. While we have begun to understand the strengths of these algorithms (such as exhaustive search) and their weaknesses (sensitivity to poor optical focus, tumor mimics; manuscript under review), the potential clinical util- ity of such algorithms has not been thoroughly examined. While an accurate algorithm alone will not necessarily aid pathologists or improve clinical interpretation, these benefits may be achieved through thoughtful and appropriate in- tegration of algorithm predictions into the clinical workflow.8 From the *Google AI Healthcare; and †Verily Life Sciences, Mountain View, CA. D.F.S., R.M., and Y.L. are co-first authors (equal contribution). Work done as part of the Google Brain Healthcare Technology Fellowship (D.F.S. and P.T.). Conflicts of Interest and Source of Funding: D.F.S., R.M., Y.L., P.T., J.D.H., C.G., F.T., L.P., M.C.S. are employees of Alphabet and have Alphabet stock. Correspondence: David F. Steiner, MD, PhD, Google AI Healthcare, 1600 Amphitheatre Way, Mountain View, CA 94043 (e-mail: davesteiner@google.com). Supplemental Digital Content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal’s website, www.ajsp.com. Copyright © 2018 The Author(s). Published by Wolters Kluwer Health, Inc. This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. ORIGINAL ARTICLE Am J Surg Pathol Volume 00, Number 00, ’’ 2018 www.ajsp.com | 1 병리과 S E P S I S A targeted real-time early warning score (TREWScore) for septic shock Katharine E. Henry,1 David N. Hager,2 Peter J. Pronovost,3,4,5 Suchi Saria1,3,5,6 * Sepsis is a leading cause of death in the United States, with mortality highest among patients who develop septic shock. Early aggressive treatment decreases morbidity and mortality. Although automated screening tools can detect patients currently experiencing severe sepsis and septic shock, none predict those at greatest risk of developing shock. We analyzed routinely available physiological and laboratory data from intensive care unit patients and devel- oped “TREWScore,” a targeted real-time early warning score that predicts which patients will develop septic shock. TREWScore identified patients before the onset of septic shock with an area under the ROC (receiver operating characteristic) curve (AUC) of 0.83 [95% confidence interval (CI), 0.81 to 0.85]. At a specificity of 0.67, TREWScore achieved a sensitivity of 0.85 and identified patients a median of 28.2 [interquartile range (IQR), 10.6 to 94.2] hours before onset. Of those identified, two-thirds were identified before any sepsis-related organ dysfunction. In compar- ison, the Modified Early Warning Score, which has been used clinically for septic shock prediction, achieved a lower AUC of 0.73 (95% CI, 0.71 to 0.76). A routine screening protocol based on the presence of two of the systemic inflam- matory response syndrome criteria, suspicion of infection, and either hypotension or hyperlactatemia achieved a low- er sensitivity of 0.74 at a comparable specificity of 0.64. Continuous sampling of data from the electronic health records and calculation of TREWScore may allow clinicians to identify patients at risk for septic shock and provide earlier interventions that would prevent or mitigate the associated morbidity and mortality. INTRODUCTION Seven hundred fifty thousand patients develop severe sepsis and septic shock in the United States each year. More than half of them are admitted to an intensive care unit (ICU), accounting for 10% of all ICU admissions, 20 to 30% of hospital deaths, and $15.4 billion in an- nual health care costs (1–3). Several studies have demonstrated that morbidity, mortality, and length of stay are decreased when severe sep- sis and septic shock are identified and treated early (4–8). In particular, one study showed that mortality from septic shock increased by 7.6% with every hour that treatment was delayed after the onset of hypo- tension (9). More recent studies comparing protocolized care, usual care, and early goal-directed therapy (EGDT) for patients with septic shock sug- gest that usual care is as effective as EGDT (10–12). Some have inter- preted this to mean that usual care has improved over time and reflects important aspects of EGDT, such as early antibiotics and early ag- gressive fluid resuscitation (13). It is likely that continued early identi- fication and treatment will further improve outcomes. However, the best approach to managing patients at high risk of developing septic shock before the onset of severe sepsis or shock has not been studied. Methods that can identify ahead of time which patients will later expe- rience septic shock are needed to further understand, study, and im- prove outcomes in this population. General-purpose illness severity scoring systems such as the Acute Physiology and Chronic Health Evaluation (APACHE II), Simplified Acute Physiology Score (SAPS II), SequentialOrgan Failure Assessment (SOFA) scores, Modified Early Warning Score (MEWS), and Simple Clinical Score (SCS) have been validated to assess illness severity and risk of death among septic patients (14–17). Although these scores are useful for predicting general deterioration or mortality, they typical- ly cannot distinguish with high sensitivity and specificity which patients are at highest risk of developing a specific acute condition. The increased use of electronic health records (EHRs), which can be queried in real time, has generated interest in automating tools that identify patients at risk for septic shock (18–20). A number of “early warning systems,” “track and trigger” initiatives, “listening applica- tions,” and “sniffers” have been implemented to improve detection andtimelinessof therapy forpatients with severe sepsis andseptic shock (18, 20–23). Although these tools have been successful at detecting pa- tients currently experiencing severe sepsis or septic shock, none predict which patients are at highest risk of developing septic shock. The adoption of the Affordable Care Act has added to the growing excitement around predictive models derived from electronic health data in a variety of applications (24), including discharge planning (25), risk stratification (26, 27), and identification of acute adverse events (28, 29). For septic shock in particular, promising work includes that of predicting septic shock using high-fidelity physiological signals collected directly from bedside monitors (30, 31), inferring relationships between predictors of septic shock using Bayesian networks (32), and using routine measurements for septic shock prediction (33–35). No current prediction models that use only data routinely stored in the EHR predict septic shock with high sensitivity and specificity many hours before onset. Moreover, when learning predictive risk scores, cur- rent methods (34, 36, 37) often have not accounted for the censoring effects of clinical interventions on patient outcomes (38). For instance, a patient with severe sepsis who received fluids and never developed septic shock would be treated as a negative case, despite the possibility that he or she might have developed septic shock in the absence of such treatment and therefore could be considered a positive case up until the 1 Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA. 2 Division of Pulmonary and Critical Care Medicine, Department of Medicine, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA. 3 Armstrong Institute for Patient Safety and Quality, Johns Hopkins University, Baltimore, MD 21202, USA. 4 Department of Anesthesiology and Critical Care Medicine, School of Medicine, Johns Hopkins University, Baltimore, MD 21202, USA. 5 Department of Health Policy and Management, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21205, USA. 6 Department of Applied Math and Statistics, Johns Hopkins University, Baltimore, MD 21218, USA. *Corresponding author. E-mail: ssaria@cs.jhu.edu R E S E A R C H A R T I C L E www.ScienceTranslationalMedicine.org 5 August 2015 Vol 7 Issue 299 299ra122 1 onNovember3,2016http://stm.sciencemag.org/Downloadedfrom 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 감염내과 심장내과 BRIEF COMMUNICATION OPEN Digital biomarkers of cognitive function Paul Dagum1 To identify digital biomarkers associated with cognitive function, we analyzed human–computer interaction from 7 days of smartphone use in 27 subjects (ages 18–34) who received a gold standard neuropsychological assessment. For several neuropsychological constructs (working memory, memory, executive function, language, and intelligence), we found a family of digital biomarkers that predicted test scores with high correlations (p 10−4 ). These preliminary results suggest that passive measures from smartphone use could be a continuous ecological surrogate for laboratory-based neuropsychological assessment. npj Digital Medicine (2018)1:10 ; doi:10.1038/s41746-018-0018-4 INTRODUCTION By comparison to the functional metrics available in other disciplines, conventional measures of neuropsychiatric disorders have several challenges. First, they are obtrusive, requiring a subject to break from their normal routine, dedicating time and often travel. Second, they are not ecological and require subjects to perform a task outside of the context of everyday behavior. Third, they are episodic and provide sparse snapshots of a patient only at the time of the assessment. Lastly, they are poorly scalable, taxing limited resources including space and trained staff. In seeking objective and ecological measures of cognition, we attempted to develop a method to measure memory and executive function not in the laboratory but in the moment, day-to-day. We used human–computer interaction on smart- phones to identify digital biomarkers that were correlated with neuropsychological performance. RESULTS In 2014, 27 participants (ages 27.1 ± 4.4 years, education 14.1 ± 2.3 years, M:F 8:19) volunteered for neuropsychological assessment and a test of the smartphone app. Smartphone human–computer interaction data from the 7 days following the neuropsychological assessment showed a range of correla- tions with the cognitive scores. Table 1 shows the correlation between each neurocognitive test and the cross-validated predictions of the supervised kernel PCA constructed from the biomarkers for that test. Figure 1 shows each participant test score and the digital biomarker prediction for (a) digits backward, (b) symbol digit modality, (c) animal fluency, (d) Wechsler Memory Scale-3rd Edition (WMS-III) logical memory (delayed free recall), (e) brief visuospatial memory test (delayed free recall), and (f) Wechsler Adult Intelligence Scale- 4th Edition (WAIS-IV) block design. Construct validity of the predictions was determined using pattern matching that computed a correlation of 0.87 with p 10−59 between the covariance matrix of the predictions and the covariance matrix of the tests. Table 1. Fourteen neurocognitive assessments covering five cognitive domains and dexterity were performed by a neuropsychologist. Shown are the group mean and standard deviation, range of score, and the correlation between each test and the cross-validated prediction constructed from the digital biomarkers for that test Cognitive predictions Mean (SD) Range R (predicted), p-value Working memory Digits forward 10.9 (2.7) 7–15 0.71 ± 0.10, 10−4 Digits backward 8.3 (2.7) 4–14 0.75 ± 0.08, 10−5 Executive function Trail A 23.0 (7.6) 12–39 0.70 ± 0.10, 10−4 Trail B 53.3 (13.1) 37–88 0.82 ± 0.06, 10−6 Symbol digit modality 55.8 (7.7) 43–67 0.70 ± 0.10, 10−4 Language Animal fluency 22.5 (3.8) 15–30 0.67 ± 0.11, 10−4 FAS phonemic fluency 42 (7.1) 27–52 0.63 ± 0.12, 10−3 Dexterity Grooved pegboard test (dominant hand) 62.7 (6.7) 51–75 0.73 ± 0.09, 10−4 Memory California verbal learning test (delayed free recall) 14.1 (1.9) 9–16 0.62 ± 0.12, 10−3 WMS-III logical memory (delayed free recall) 29.4 (6.2) 18–42 0.81 ± 0.07, 10−6 Brief visuospatial memory test (delayed free recall) 10.2 (1.8) 5–12 0.77 ± 0.08, 10−5 Intelligence scale WAIS-IV block design 46.1(12.8) 12–61 0.83 ± 0.06, 10−6 WAIS-IV matrix reasoning 22.1(3.3) 12–26 0.80 ± 0.07, 10−6 WAIS-IV vocabulary 40.6(4.0) 31–50 0.67 ± 0.11, 10−4 Received: 5 October 2017 Revised: 3 February 2018 Accepted: 7 February 2018 1 Mindstrong Health, 248 Homer Street, Palo Alto, CA 94301, USA Correspondence: Paul Dagum (paul@mindstronghealth.com) www.nature.com/npjdigitalmed 정신의학과 P R E C I S I O N M E D I C I N E Identification of type 2 diabetes subgroups through topological analysis of patient similarity Li Li,1 Wei-Yi Cheng,1 Benjamin S. Glicksberg,1 Omri Gottesman,2 Ronald Tamler,3 Rong Chen,1 Erwin P. Bottinger,2 Joel T. Dudley1,4 * Type 2 diabetes (T2D) is a heterogeneous complex disease affecting more than 29 million Americans alone with a rising prevalence trending toward steady increases in the coming decades. Thus, there is a pressing clinical need to improve early prevention and clinical management of T2D and its complications. Clinicians have understood that patients who carry the T2D diagnosis have a variety of phenotypes and susceptibilities to diabetes-related compli- cations. We used a precision medicine approach to characterize the complexity of T2D patient populations based on high-dimensional electronic medical records (EMRs) and genotype data from 11,210 individuals. We successfully identified three distinct subgroups of T2D from topology-based patient-patient networks. Subtype 1 was character- ized by T2D complications diabetic nephropathy and diabetic retinopathy; subtype 2 was enriched for cancer ma- lignancy and cardiovascular diseases; and subtype 3 was associated most strongly with cardiovascular diseases, neurological diseases, allergies, and HIV infections. We performed a genetic association analysis of the emergent T2D subtypes to identify subtype-specific genetic markers and identified 1279, 1227, and 1338 single-nucleotide polymorphisms (SNPs) that mapped to 425, 322, and 437 unique genes specific to subtypes 1, 2, and 3, respec- tively. By assessing the human disease–SNP association for each subtype, the enriched phenotypes and biological functions at the gene level for each subtype matched with the disease comorbidities and clinical dif- ferences that we identified through EMRs. Our approach demonstrates the utility of applying the precision medicine paradigm in T2D and the promise of extending the approach to the study of other complex, multi- factorial diseases. INTRODUCTION Type 2 diabetes (T2D) is a complex, multifactorial disease that has emerged as an increasing prevalent worldwide health concern asso- ciated with high economic and physiological burdens. An estimated 29.1 million Americans (9.3% of the population) were estimated to have some form of diabetes in 2012—up 13% from 2010—with T2D representing up to 95% of all diagnosed cases (1, 2). Risk factors for T2D include obesity, family history of diabetes, physical inactivity, eth- nicity, and advanced age (1, 2). Diabetes and its complications now rank among the leading causes of death in the United States (2). In fact, diabetes is the leading cause of nontraumatic foot amputation, adult blindness, and need for kidney dialysis, and multiplies risk for myo- cardial infarction, peripheral artery disease, and cerebrovascular disease (3–6). The total estimated direct medical cost attributable to diabetes in the United States in 2012 was $176 billion, with an estimated $76 billion attributable to hospital inpatient care alone. There is a great need to im- prove understanding of T2D and its complex factors to facilitate pre- vention, early detection, and improvements in clinical management. A more precise characterization of T2D patient populations can en- hance our understanding of T2D pathophysiology (7, 8). Current clinical definitions classify diabetes into three major subtypes: type 1 dia- betes (T1D), T2D, and maturity-onset diabetes of the young. Other sub- types based on phenotype bridge the gap between T1D and T2D, for example, latent autoimmune diabetes in adults (LADA) (7) and ketosis- prone T2D. The current categories indicate that the traditional definition of diabetes, especially T2D, might comprise additional subtypes with dis- tinct clinical characteristics. A recent analysis of the longitudinal Whitehall II cohort study demonstrated improved assessment of cardiovascular risks when subgrouping T2D patients according to glucose concentration criteria (9). Genetic association studies reveal that the genetic architec- ture of T2D is profoundly complex (10–12). Identified T2D-associated risk variants exhibit allelic heterogeneity and directional differentiation among populations (13, 14). The apparent clinical and genetic com- plexity and heterogeneity of T2D patient populations suggest that there are opportunities to refine the current, predominantly symptom-based, definition of T2D into additional subtypes (7). Because etiological and pathophysiological differences exist among T2D patients, we hypothesize that a data-driven analysis of a clinical population could identify new T2D subtypes and factors. Here, we de- velop a data-driven, topology-based approach to (i) map the complexity of patient populations using clinical data from electronic medical re- cords (EMRs) and (ii) identify new, emergent T2D patient subgroups with subtype-specific clinical and genetic characteristics. We apply this approachtoadatasetcomprisingmatchedEMRsandgenotypedatafrom more than 11,000 individuals. Topological analysis of these data revealed three distinct T2D subtypes that exhibited distinct patterns of clinical characteristics and disease comorbidities. Further, we identified genetic markers associated with each T2D subtype and performed gene- and pathway-level analysis of subtype genetic associations. Biological and phenotypic features enriched in the genetic analysis corroborated clinical disparities observed among subgroups. Our findings suggest that data- driven,topologicalanalysisofpatientco 내분비내과 LETTER Derma o og - eve c a ca on o k n cancer w h deep neura ne work 피부과 FOCUS LETTERS W W W W W Ca d o og s eve a hy hm a de ec on and c ass ca on n ambu a o y e ec oca d og ams us ng a deep neu a ne wo k M m M FOCUS LETTERS 심장내과 D p a n ng nab obu a m n and on o human b a o y a n v o a on 산부인과 O G NA A W on o On o og nd b e n e e men e ommend on g eemen w h n e pe mu d p n umo bo d 종양내과 D m m B D m OHCA m Kw MD K H MD M H M K m MD M M K m MD M M L m MD M K H K m MD D MD D MD D R K C MD D B H O MD D D m Em M M H K D C C C M H K T w A D C D m M C C M H G m w G R K Tw w C A K H MD D C D m M C C M H K G m w G R K T E m m @ m m A A m O OHCA m m m w w T m m DCA M T w m K OHCA w A CCEPTED M A N U SCRIPT 응급의학과
  104. 104. •복잡한 의료 데이터의 분석 및 insight 도출 •영상 의료/병리 데이터의 분석/판독 •연속 데이터의 모니터링 및 예방/예측 의료 인공지능의 세 유형
  105. 105. •복잡한 의료 데이터의 분석 및 insight 도출 •영상 의료/병리 데이터의 분석/판독 •연속 데이터의 모니터링 및 예방/예측 의료 인공지능의 세 유형
  106. 106. Jeopardy! 2011년 인간 챔피언 두 명 과 퀴즈 대결을 벌여서 압도적인 우승을 차지
  107. 107. 메이요 클리닉 협력 (임상 시험 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 JJ, 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 
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 센터 도입 식약처 인공지능 가이드라인 메이요 클리닉 임상시험매칭 결과발표 2018 건양대병원 도입 IBM Watson Health Chronicle WFO 최초 논문 대구가톨릭병원 대구동산병원 
 도입
  109. 109. 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%)
  110. 110. WFO in ASCO 2017 •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
  111. 111. 원칙이 필요하다 •어떤 환자의 경우, 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
  112. 112. •복잡한 의료 데이터의 분석 및 insight 도출 •영상 의료/병리 데이터의 분석/판독 •연속 데이터의 모니터링 및 예방/예측 의료 인공지능의 세 유형
  113. 113. Deep Learning http://theanalyticsstore.ie/deep-learning/
  114. 114. Radiologist
  115. 115. •손 엑스레이 영상을 판독하여 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

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