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디지털 헬스케어를 어떻게 구현할 것인가: 국내 스타트업 업계를 중심으로

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네이버 D2 스타트업 팩토리에서 2016년 3월 15일 강의한 자료입니다.

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디지털 헬스케어를 어떻게 구현할 것인가: 국내 스타트업 업계를 중심으로

  1. 1. “It's in Apple's DNA that technology alone is not enough. 
 It's technology married with liberal arts.”
  2. 2. The Convergence of IT, BT and Medicine
  3. 3. Inevitable Tsunami of Change
  4. 4. http://rockhealth.com/2015/01/digital-health-funding-tops-4-1b-2014-year-review/
  5. 5. https://rockhealth.com/reports/digital-health-funding-2015-year-in-review/
  6. 6. PRESENTATION © 2015 ROCK HEALTH Source: Rock Health tracking and analysis based on news reports Note: M&A transactions totals and lists are not meant to be comprehensive; deals through December 8, 2015 We tracked 180 M&A deals throughout 2015, nearly doubling in transaction volume from 2014. 16 DIGITAL HEALTH ACQUIRERS 2015 (notable transactions listed at right) Digital health Tech Other PE Med device Provider Payer Biopharma 2 1 1 7 7 6 20 89 7 3 2 6 10 Other healthcare Aggregate transaction value Per disclosed deal $3.0B $157M $1.1B $183M $693B $99B $839M $120M $200M $200M $290M $290M $69M $35M N/A N/A N/A N/A $6.2B $140MTOTAL Target Acquirer Amount Merge Healthcare IBM $1.0B Allegra Health Emdeon $910M Virtual Radiologic MedNax $500M MyFitnessPal Under Armour $475M SoftWriters Managed Health Care Associates $450M CECity Premier $400M NaviHealth Cardinal Health $290M Assent Omnicell International $281M Misfit Fossil $260M Healthland CPSI $250M Sentry Data Systems ABRY Partners $200M HealthFusion Quality Systems $165M Learner’s Digest Wolters Kluwer $150M Acclaris Extend Health $150M HealthLine HealthStream $88M DR Systems Merge Healthcare $76M NextCode Health Wuxi PharmaTech $65M Healthcare Insights Premier $65M Target Acquirer Predilytics Welltok Explorys IBM Nextdocs Aurea Software Clinicast Elekta Cypher Genomics Human Longevity Health Heritage NantHealth Benefitter HealthMarkets Healthy Communities Institute Xerox 1DocWay Genoa Custom Data TelePharm MedSynergies UnitedHealth Group DoctorBase Kareo RazorInsights athenahealth GenoLogic Illumina Hot5 Weight Watchers Health Access Solutions MedVision CardioInsight Medtronic DISCLOSED UNDISCLOSED 22 25 16 # of M&A deals LEGEND With disclosed transaction value 2015 total https://rockhealth.com/reports/digital-health-funding-2015-year-in-review/
  7. 7. PRESENTATION © 2015 ROCK HEALTH Digital health continues to receive funding from a wide range of funds with a noticeable growth in the number of active corporate investors. 13 2 2 5 4 5 3 2 10 4 5 7 8 8 MOST ACTIVE FUNDS Deals in 2015 & cumulative deals 2011-2015 8 Source: Rock Health Funding Database Note: Only includes U.S. deals >$2M; data through December 8, 2015 10 1 3 3 4 3 3 24 2 3 4 10 10 13 4 4 2 4 6 7 2 3 11 2 3 1 2 2 3 4 2 8 20 6 9 5 20 5 12 11 5 5 4 4 4 6 12 4 4 # of deals LEGEND 2015 Seed / A by venture funds 2015 Total by venture funds 2015 Seed / A by corporate funds 2015 Total by corporate funds 2011-2015 Cumulative seed / A 2011-2015 Cumulative total 4 4 8 6 6 5 4 4 18 17 14 7 17 23 15 14 18 20 4 5 4 5 4 5 14 27 4 4 https://rockhealth.com/reports/digital-health-funding-2015-year-in-review/
  8. 8. 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
  9. 9. What is most important factor in digital medicine?
  10. 10. “Data! Data! Data!” he cried.“I can’t make bricks without clay!” - Sherlock Holmes,“The Adventure of the Copper Beeches”
  11. 11. Three Steps to Implement Digital Medicine • Step 1. Measure the Data • Step 2. Collect the Data • Step 3. Insight from the Data
  12. 12. Digital Healthcare Industry Landscape Data Measurement Data Integration Data Interpretation Treatment Smartphone Gaget/Apps DNA Artificial Intelligence Telemedicine 2nd Opinion Device On Demand (O2O) Wearables / IoT 3D Printer Counseling (ver. 1) Digital Healthcare Institute Diretor, Yoon Sup Choi, Ph.D. yoonsup.choi@gmail.com EMR/EHR Data Platform Accelerator/early-VC
  13. 13. Digital Healthcare Industry Landscape Data Measurement Data Integration Data Interpretation Treatment Smartphone Gaget/Apps DNA Artificial Intelligence Telemedicine Device On Demand (O2O) Wearables / IoT 3D Printer Counseling (ver. 0.6) Digital Healthcare Institute Diretor, Yoon Sup Choi, Ph.D. yoonsup.choi@gmail.com EMR/EHR Data Platform Accelerator/early-VC
  14. 14. Step 1. Measure the Data
  15. 15. Smartphone: the origin of healthcare innovation
  16. 16. Sci Transl Med 2015
  17. 17. Jan 2015 WSJ
  18. 18. CellScope’s iPhone-enabled otoscope
  19. 19. http://www.firsthud.com/ Smartphone-connected dermatoscope
  20. 20. Smartphone video microscope automates detection of parasites in blood
  21. 21. SpiroSmart: spirometer using iPhone
  22. 22. AliveCor Heart Monitor
  23. 23. Sleep Cycle
  24. 24. Digital Phenotype: Your smartphone knows if you are depressed Ginger.io
  25. 25. • 초기 버전으로, 5가지 질환에 대한 앱 5개를 소개 ResearchKit
  26. 26. Wearable Devices
  27. 27. PwC Health Research Institute Health wearables: Early days2 insurers—offering incentives for use may gain traction. HRI’s survey Source: HRI/CIS Wearables consumer survey 2014 21% of US consumers currently own a wearable technology product 2% wear it a few times a month 2% no longer use it 7% wear it a few times a week 10% wear it everyday Figure 2: Wearables are not mainstream – yet Just one in five US consumers say they own a wearable device. Intelligence Series sought to better understand American consumers’ attitudes toward wearables through done with the data. PwC, Health wearables: early days, 2014
  28. 28. Hype or Hope?
  29. 29. Fitbit
  30. 30. Apple Watch
  31. 31. Xiaomi 17.4% Apple 18.6% Fitbit 22.2% http://www.idc.com/getdoc.jsp?containerId=prUS40674715
  32. 32. 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
  33. 33. • $20 • the first and only 24-hour thermometer • constantly monitor baby’s temperature • FDA cleared
  34. 34. iRythm ZIO patch
  35. 35. Google’s Smart Contact Lens
  36. 36. Withings Wireless Blood Pressure Monitor
  37. 37. Huinno: Cuff-less Blood Pressure Monitor
  38. 38. Smart Band detecting seizure
  39. 39. Ingestible Sensor, Proteus Digital Health
  40. 40. Personal Genome Analysis
  41. 41. Results within 6-8 weeksA little spit is all it takes! DTC Genetic TestingDirect-To-Consumer
  42. 42. 120 Disease Risk 21 Drug Response 49 Carrier Status 57Traits $99
  43. 43. 1,200,000 1,000,000 900,000 850,000 650,000 500,000 400,000 300,000 250,000 180,000 100,000 2007-11 2011-06 2011-10 2012-04 2012-10 2013-04 2013-06 2013-09 2013-12 2014-10 2015-02 2015-05 2015-06 2016-02 0 Customer growth of 23andMe
  44. 44. Human genomes are being sequenced at an ever-increasing rate. The 1000 Genomes Project has aggregated hundreds of genomes; The Cancer Genome Atlas (TGCA) has gathered several thousand; and the Exome Aggregation Consortium (ExAC) has sequenced more than 60,000 exomes. Dotted lines show three possible future growth curves. DNA SEQUENCING SOARS 2001 2005 2010 2015 2020 2025 100 103 106 109 Human Genome Project Cumulativenumberofhumangenomes 1000 Genomes TCGA ExAC Current amount 1st personal genome Recorded growth Projection Double every 7 months (historical growth rate) Double every 12 months (Illumina estimate) Double every 18 months (Moore's law) Michael Einsetein, Nature, 2015
  45. 45. Step1. Measure the Data • With your smartphone • With wearable devices (connected to smartphone) • Personal genome analysis ... without even going to the hospital!
  46. 46. Step 2. Collect the Data
  47. 47. Sci Transl Med 2015
  48. 48. Epic MyChart App Epic EHRDatabaseDexcom App Withings App Dexcom CGM Nike+ Patients Device/Apps HealthKit EHR Hospital Whitings + • Data stored in DB on the iPhone (, not mirroring to the cloud) • Consumer controls what data goes in/out, privacy level • HealthKit connects/direct devices, store data based on privacy rules Apple Watch iPhone
  49. 49. • 애플 HealthKit 가 미국의 23개 선도병원 중에, 14개의 병원과 협력 • 경쟁 플랫폼 Google Fit, S-Health 보다 현저히 빠른 움직임 • Beth Israel Deaconess 의 CIO • “25만명의 환자들 중 상당수가 웨어러블로 각종 데이터 생산 중.
 이 모든 디바이스에 인터페이스를 우리 병원은 제공할 수 없다. 
 하지만 애플이라면 가능하다.” 2015.2.5
  50. 50. Step 3. Insight from the Data
  51. 51. Data Overload
  52. 52. How to Analyze and Interpret the Big Data?
  53. 53. and/or Two ways to get insights from the big data
  54. 54. Hospitals in the future: Data Analysis Center
  55. 55. Doctors in the future: Data Scientists
  56. 56. No choice but to bring AI into the medicine
  57. 57. • 약한 인공 지능 (Artificial Narrow Intelligence) • 특정 방면에서 잘하는 인공지능 • 체스, 퀴즈, 메일 필터링, 상품 추천, 자율 운전 • 강한 인공 지능 (Artificial General Intelligence) • 모든 방면에서 인간 급의 인공 지능 • 사고, 계획, 문제해결, 추상화, 복잡한 개념 학습 • 초 인공 지능 (Artificial Super Intelligence) • 과학기술, 사회적 능력 등 모든 영역에서 인간보다 뛰어난 인공 지능 • “충분히 발달한 과학은 마법과 구분할 수 없다” - 아서 C. 클라크
  58. 58. Jeopardy! 2011년 인간 챔피언 두 명 과 퀴즈 대결을 벌여서 압도적인 우승을 차지
  59. 59. 600,000 pieces of medical evidence 2 million pages of text from 42 medical journals and clinical trials 69 guidelines, 61,540 clinical trials IBM Watson on Medicine Watson learned... + 1,500 lung cancer cases physician notes, lab results and clinical research + 14,700 hours of hands-on training
  60. 60. • Trained by 400 cases of historical patients cases • Assessed accuracy OEA treatment suggestions 
 using MD Anderson’s physicians’ decision as benchmark • When 200 leukemia cases were tested, • False positive rate=2.9% (OEA 추천 치료법이 부정확한 경우) • False negative rate=0.4% (정확한 치료법이 낮은 점수를 받은 경우) • Overall accuracy of treatment recommendation=82.6% • Conclusion: Suggested personalized treatment option showed reasonably high accuracy MDAnderson’s Oncology ExpertAdvisor Powered by IBM Watson :AWeb-Based Cognitive Clinical Decision Support Tool
  61. 61. 2015.10.4.Transforming Medicine, San Diego
  62. 62. 한국에서도 Watson을 볼 수 있을까? 2015.7.9. 서울대학병원
  63. 63. Deep Learning http://theanalyticsstore.ie/deep-learning/
  64. 64. DeepFace: Closing the Gap to Human-Level Performance in FaceVerification Taigman,Y. et al. (2014). DeepFace: Closing the Gap to Human-Level Performance in FaceVerification, CVPR’14. Figure 2. Outline of the DeepFace architecture. A front-end of a single convolution-pooling-convolution filtering on the rectified input, followed by three locally-connected layers and two fully-connected layers. Colors illustrate feature maps produced at each layer. The net includes more than 120 million parameters, where more than 95% come from the local and fully connected layers. very few parameters. These layers merely expand the input into a set of simple local features. The subsequent layers (L4, L5 and L6) are instead lo- cally connected [13, 16], like a convolutional layer they ap- ply a filter bank, but every location in the feature map learns a different set of filters. Since different regions of an aligned image have different local statistics, the spatial stationarity The goal of training is to maximize the probability of the correct class (face id). We achieve this by minimiz- ing the cross-entropy loss for each training sample. If k is the index of the true label for a given input, the loss is: L = log pk. The loss is minimized over the parameters by computing the gradient of L w.r.t. the parameters and Human: 95% vs. DeepFace in Facebook: 97.35% Recognition Accuracy for Labeled Faces in the Wild (LFW) dataset (13,233 images, 5,749 people)
  65. 65. FaceNet:A Unified Embedding for Face Recognition and Clustering Schroff, F. et al. (2015). FaceNet:A Unified Embedding for Face Recognition and Clustering Human: 95% vs. FaceNet of Google: 99.63% Recognition Accuracy for Labeled Faces in the Wild (LFW) dataset (13,233 images, 5,749 people) False accept False reject s. This shows all pairs of images that were on LFW. Only eight of the 13 errors shown he other four are mislabeled in LFW. on Youtube Faces DB ge similarity of all pairs of the first one our face detector detects in each video. False accept False reject Figure 6. LFW errors. This shows all pairs of images that were incorrectly classified on LFW. Only eight of the 13 errors shown here are actual errors the other four are mislabeled in LFW. 5.7. Performance on Youtube Faces DB We use the average similarity of all pairs of the first one hundred frames that our face detector detects in each video. This gives us a classification accuracy of 95.12%±0.39. Using the first one thousand frames results in 95.18%. Compared to [17] 91.4% who also evaluate one hundred frames per video we reduce the error rate by almost half. DeepId2+ [15] achieved 93.2% and our method reduces this error by 30%, comparable to our improvement on LFW. 5.8. Face Clustering Our compact embedding lends itself to be used in order to cluster a users personal photos into groups of people with the same identity. The constraints in assignment imposed by clustering faces, compared to the pure verification task, lead to truly amazing results. Figure 7 shows one cluster in a users personal photo collection, generated using agglom- erative clustering. It is a clear showcase of the incredible invariance to occlusion, lighting, pose and even age. Figure 7. Face Clustering. Shown is an exemplar cluster for one user. All these images in the users personal photo collection were clustered together. 6. Summary We provide a method to directly learn an embedding into an Euclidean space for face verification. This sets it apart from other methods [15, 17] who use the CNN bottleneck layer, or require additional post-processing such as concate- nation of multiple models and PCA, as well as SVM clas- sification. Our end-to-end training both simplifies the setup and shows that directly optimizing a loss relevant to the task at hand improves performance. Another strength of our model is that it only requires False accept False reject Figure 6. LFW errors. This shows all pairs of images that were incorrectly classified on LFW. Only eight of the 13 errors shown here are actual errors the other four are mislabeled in LFW. 5.7. Performance on Youtube Faces DB We use the average similarity of all pairs of the first one hundred frames that our face detector detects in each video. This gives us a classification accuracy of 95.12%±0.39. Using the first one thousand frames results in 95.18%. Compared to [17] 91.4% who also evaluate one hundred frames per video we reduce the error rate by almost half. DeepId2+ [15] achieved 93.2% and our method reduces this error by 30%, comparable to our improvement on LFW. 5.8. Face Clustering Our compact embedding lends itself to be used in order to cluster a users personal photos into groups of people with the same identity. The constraints in assignment imposed by clustering faces, compared to the pure verification task, Figure 7. Face Clustering. Shown is an exemplar cluster for one user. All these images in the users personal photo collection were clustered together. 6. Summary We provide a method to directly learn an embedding into an Euclidean space for face verification. This sets it apart from other methods [15, 17] who use the CNN bottleneck layer, or require additional post-processing such as concate- nation of multiple models and PCA, as well as SVM clas-
  66. 66. Show and Tell: A Neural Image Caption Generator Vinyals, O. et al. (2015). Show and Tell:A Neural Image Caption Generator, arXiv:1411.4555 v om Samy Bengio Google bengio@google.com Dumitru Erhan Google dumitru@google.com s a cts his re- m- ed he de- nts A group of people shopping at an outdoor market. ! There are many vegetables at the fruit stand. Vision! Deep CNN Language ! Generating! RNN Figure 1. NIC, our model, is based end-to-end on a neural net- work consisting of a vision CNN followed by a language gener-
  67. 67. Show and Tell: A Neural Image Caption Generator Vinyals, O. et al. (2015). Show and Tell:A Neural Image Caption Generator, arXiv:1411.4555 Figure 5. A selection of evaluation results, grouped by human rating.
  68. 68. Business Area Medical Image Analysis VUNOnet and our machine learning technology will help doctors and hospitals manage medical scans and images intelligently to make diagnosis faster and more accurately. Original Image Automatic Segmentation EmphysemaNormal ReticularOpacity Our system finds DILDs at the highest accuracy * DILDs: Diffuse Interstitial Lung Disease Digital Radiologist Collaboration with Prof. Joon Beom Seo (Asan Medical Center) Analysed 1200 patients for 3 months
  69. 69. Digital Radiologist Med Phys. 2013 May;40(5):051912. doi: 10.1118/1.4802214. Collaboration with Prof. Joon Beom Seo (Asan Medical Center) Analysed 1200 patients for 3 months
  70. 70. Figure 4. Participating Pathologists’ 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 Diagnostic Concordance Among Pathologists Interpreting Breast Biopsy Specimens The overall agreement between the individual pathologists’ interpretations and the expert consensus–derived reference diagnoses was 75.3% (total 240 cases)
  71. 71. Constructing higher-level contextual/relational features: Relationships between epithelial nuclear neighbors Relationships between morphologically regular and irregular nuclei Relationships between epithelial and stromal objects Relationships between epithelial nuclei and cytoplasm Characteristics of stromal nuclei and stromal matrix Characteristics of epithelial nuclei and epithelial cytoplasm Building an epithelial/stromal classifier: Epithelial vs.stroma classifier Epithelial vs.stroma classifier B Basic image processing and feature construction: H&E image Image broken into superpixels Nuclei identified within each superpixel A Relationships of contiguous epithelial regions with underlying nuclear objects Learning an image-based model to predict survival Processed images from patients Processed images from patients C D onNovember17,2011stm.sciencemag.orgwnloadedfrom TMAs contain 0.6-mm-diameter cores (median of two cores per case) that represent only a small sample of the full tumor. We acquired data from two separate and independent cohorts: Nether- lands Cancer Institute (NKI; 248 patients) and Vancouver General Hospital (VGH; 328 patients). Unlike previous work in cancer morphom- etry (18–21), our image analysis pipeline was not limited to a predefined set of morphometric features selected by pathologists. Rather, C-Path measures an extensive, quantitative feature set from the breast cancer epithelium and the stro- ma (Fig. 1). Our image processing system first performed an automated, hierarchical scene seg- mentation that generated thousands of measure- ments, including both standard morphometric descriptors of image objects and higher-level contextual, relational, and global image features. The pipeline consisted of three stages (Fig. 1, A to C, and tables S8 and S9). First, we used a set of processing steps to separate the tissue from the background, partition the image into small regions of coherent appearance known as superpixels, find nuclei within the superpixels, and construct Constructing higher-level contextual/relational features: Relationships between epithelial nuclear neighbors Relationships between morphologically regular and irregular nuclei Relationships between epithelial and stromal objects Relationships between epithelial nuclei and cytoplasm Characteristics of stromal nuclei and stromal matrix Characteristics of epithelial nuclei and epithelial cytoplasm Epithelial vs.stroma classifier Epithelial vs.stroma classifier Relationships of contiguous epithelial regions with underlying nuclear objects Learning an image-based model to predict survival Processed images from patients alive at 5 years Processed images from patients deceased at 5 years L1-regularized logisticregression modelbuilding 5YS predictive model Unlabeled images Time P(survival) C D Identification of novel prognostically important morphologic features basic cellular morphologic properties (epithelial reg- ular nuclei = red; epithelial atypical nuclei = pale blue; epithelial cytoplasm = purple; stromal matrix = green; stromal round nuclei = dark green; stromal spindled nuclei = teal blue; unclassified regions = dark gray; spindled nuclei in unclassified regions = yellow; round nuclei in unclassified regions = gray; background = white). (Left panel) After the classification of each image object, a rich feature set is constructed. (D) Learning an image-based model to predict survival. Processed images from patients alive at 5 years after surgery and from patients deceased at 5 years after surgery were used to construct an image-based prog- nostic model. After construction of the model, it was applied to a test set of breast cancer images (not used in model building) to classify patients as high or low risk of death by 5 years. www.ScienceTranslationalMedicine.org 9 November 2011 Vol 3 Issue 108 108ra113 2 onNovember17,2011stm.sciencemag.orgDownloadedfrom Digital Pathologist Sci Transl Med. 2011 Nov 9;3(108):108ra113
  72. 72. Digital Pathologist Sci Transl Med. 2011 Nov 9;3(108):108ra113 Top stromal features associated with survival. primarily characterizing epithelial nuclear characteristics, such as size, color, and texture (21, 36). In contrast, after initial filtering of im- ages to ensure high-quality TMA images and training of the C-Path models using expert-derived image annotations (epithelium and stroma labels to build the epithelial-stromal classifier and survival time and survival status to build the prognostic model), our image analysis system is automated with no manual steps, which greatly in- creases its scalability. Additionally, in contrast to previous approaches, our system measures thousands of morphologic descriptors of diverse identification of prognostic features whose significance was not pre- viously recognized. Using our system, we built an image-based prognostic model on the NKI data set and showed that in this patient cohort the model was a strong predictor of survival and provided significant additional prognostic information to clinical, molecular, and pathological prog- nostic factors in a multivariate model. We also demonstrated that the image-based prognostic model, built using the NKI data set, is a strong prognostic factor on another, independent data set with very different SD of the ratio of the pixel intensity SD to the mean intensity for pixels within a ring of the center of epithelial nuclei A The sum of the number of unclassified objects SD of the maximum blue pixel value for atypical epithelial nuclei Maximum distance between atypical epithelial nuclei B C D Maximum value of the minimum green pixel intensity value in epithelial contiguous regions Minimum elliptic fit of epithelial contiguous regions SD of distance between epithelial cytoplasmic and nuclear objects Average border between epithelial cytoplasmic objects E F G H Fig. 5. Top epithelial features. The eight panels in the figure (A to H) each shows one of the top-ranking epithelial features from the bootstrap anal- ysis. Left panels, improved prognosis; right panels, worse prognosis. (A) SD of the (SD of intensity/mean intensity) for pixels within a ring of the center of epithelial nuclei. Left, relatively consistent nuclear intensity pattern (low score); right, great nuclear intensity diversity (high score). (B) Sum of the number of unclassified objects. Red, epithelial regions; green, stromal re- gions; no overlaid color, unclassified region. Left, few unclassified objects (low score); right, higher number of unclassified objects (high score). (C) SD of the maximum blue pixel value for atypical epithelial nuclei. Left, high score; right, low score. (D) Maximum distance between atypical epithe- lial nuclei. Left, high score; right, low score. (Insets) Red, atypical epithelial nuclei; black, typical epithelial nuclei. (E) Minimum elliptic fit of epithelial contiguous regions. Left, high score; right, low score. (F) SD of distance between epithelial cytoplasmic and nuclear objects. Left, high score; right, low score. (G) Average border between epithelial cytoplasmic objects. Left, high score; right, low score. (H) Maximum value of the minimum green pixel intensity value in epithelial contiguous regions. Left, low score indi- cating black pixels within epithelial region; right, higher score indicating presence of epithelial regions lacking black pixels. onNovember17,2011stm.sciencemag.orgDownloadedfrom and stromal matrix throughout the image, with thin cords of epithe- lial cells infiltrating through stroma across the image, so that each stromal matrix region borders a relatively constant proportion of ep- ithelial and stromal regions. The stromal feature with the second largest coefficient (Fig. 4B) was the sum of the minimum green in- tensity value of stromal-contiguous regions. This feature received a value of zero when stromal regions contained dark pixels (such as inflammatory nuclei). The feature received a positive value when stromal objects were devoid of dark pixels. This feature provided in- formation about the relationship between stromal cellular composi- tion and prognosis and suggested that the presence of inflammatory cells in the stroma is associated with poor prognosis, a finding con- sistent with previous observations (32). The third most significant stromal feature (Fig. 4C) was a measure of the relative border between spindled stromal nuclei to round stromal nuclei, with an increased rel- ative border of spindled stromal nuclei to round stromal nuclei asso- ciated with worse overall survival. Although the biological underpinning of this morphologic feature is currently not known, this analysis sug- gested that spatial relationships between different populations of stro- mal cell types are associated with breast cancer progression. Reproducibility of C-Path 5YS model predictions on samples with multiple TMA cores For the C-Path 5YS model (which was trained on the full NKI data set), we assessed the intrapatient agreement of model predictions when predictions were made separately on each image contributed by pa- tients in the VGH data set. For the 190 VGH patients who contributed two images with complete image data, the binary predictions (high or low risk) on the individual images agreed with each other for 69% (131 of 190) of the cases and agreed with the prediction on the aver- aged data for 84% (319 of 380) of the images. Using the continuous prediction score (which ranged from 0 to 100), the median of the ab- solute difference in prediction score among the patients with replicate images was 5%, and the Spearman correlation among replicates was 0.27 (P = 0.0002) (fig. S3). This degree of intrapatient agreement is only moderate, and these findings suggest significant intrapatient tumor heterogeneity, which is a cardinal feature of breast carcinomas (33–35). Qualitative visual inspection of images receiving discordant scores suggested that intrapatient variability in both the epithelial and the stromal components is likely to contribute to discordant scores for the individual images. These differences appeared to relate both to the proportions of the epithelium and stroma and to the appearance of the epithelium and stroma. Last, we sought to analyze whether sur- vival predictions were more accurate on the VGH cases that contributed multiple cores compared to the cases that contributed only a single core. This analysis showed that the C-Path 5YS model showed signif- icantly improved prognostic prediction accuracy on the VGH cases for which we had multiple images compared to the cases that con- tributed only a single image (Fig. 7). Together, these findings show a significant degree of intrapatient variability and indicate that increased tumor sampling is associated with improved model performance. DISCUSSION Heat map of stromal matrix objects mean abs.diff to neighbors H&E image separated into epithelial and stromal objects A B C Worse prognosis Improved prognosis Improved prognosis Improved prognosis Worse prognosis Worse prognosis Fig. 4. Top stromal features associated with survival. (A) Variability in ab- solute difference in intensity between stromal matrix regions and neigh- bors. Top panel, high score (24.1); bottom panel, low score (10.5). (Insets) Top panel, high score; bottom panel; low score. Right panels, stromal matrix objects colored blue (low), green (medium), or white (high) according to each object’s absolute difference in intensity to neighbors. (B) Presence R E S E A R C H A R T I C L E onNovember17,2011stm.sciencemag.orgDownloadedfrom Top epithelial features.The eight panels in the figure (A to H) each shows one of the top-ranking epithelial features from the bootstrap anal- ysis. Left panels, improved prognosis; right panels, worse prognosis.
  73. 73. Project Artemis at UIOT
  74. 74. Jan 7, 2016
  75. 75. In an early research project involving 600 patient cases, the team was able to 
 predict near-term hypoglycemic events up to 3 hours in advance of the symptoms. IBM Watson-Medtronic Jan 7, 2016
  76. 76. Prediction ofVentricular Arrhythmia
  77. 77. Prediction ofVentricular Arrhythmia Collaboration with Prof. Segyeong Joo (Asan Medical Center) Analysed “Physionet Spontaneous Ventricular Tachyarrhythmia Database” for 2.5 months (on going project) Joo S, Choi KJ, Huh SJ, 2012, Expert Systems with Applications (Vol 39, Issue 3) ▪ Recurrent Neural Network with Only Frequency Domain Transform • Input : Spectrogram with 129 features obtained after ectopic beats removal • Stack of LSTM Networks • Binary cross-entropy loss • Trained with RMSprop • Prediction Accuracy : 76.6% ➞ 89.6% Dropout Dropout
  78. 78. Three Steps to Implement Digital Medicine • Step 1. Measure the Data • Step 2. Collect the Data • Step 3. Insight from the Data
  79. 79. 향후 피해갈 수 없는 이슈들 그러나 아직은 회색 지대인 것들
  80. 80. Regulations
  81. 81. 디지털 헬스케어 (예비) 스타트업을 위한 여덟 가지 잔소리
  82. 82. 아무도 원하지 않는 제품을 만들고 있는 것은 아닌가? 예쁜 쓰레기를 만들지 마라
  83. 83. 아무도 원하지 않는 제품을 만들고 있는 것은 아닌가? • 진짜 니즈가 무엇인지 파악하라 • 고객들이 원한다고 말하는 것 (X) • 고객들이 원한다고 당신이 생각하는 것 (X) • 실제로 진짜 고객들이 원하는 것 (O) • 무엇이 가능한지 모르기 때문에, 고객은 스스로 무엇을 원하는지 모를 것이다.
  84. 84. 고객이 누구인가? 그 고객 중 누구를 고를 것인가
  85. 85. 고객이 누구인가? • 헬스케어 시장의 니즈는 고객마다 매우 세분화되어 있다. • 건강인 / 환자 • 20대 / 30대 / 40대 / 50대 / 60대 / 70대 / 80대 • 남성 / 여성 • 저체중 / 정상/ 과체중 • 가족력
 • B2C / B2B
  86. 86. 고객이 누구인가? • 모든 고객의 니즈를 모두 충족시키는 것은 불가능하다. • 그렇다면 어떤 고객을 골라야하나? • 가장 절박한 니즈를 가진 고객 세그먼트는? • 우리가 실제로 해결책을 제시할 수 있는 고객은? • 돈을 낼 수 있는 고객은?
  87. 87. Fitbit
  88. 88. https://www.empatica.com/science
  89. 89. 3.6 percent Proportion of U.S. couples that use its tests before trying to conceive
  90. 90. 의료적 관점에서도 동의할 수 있는 해결책인가 의료 전문가의 도움이 필요하다
  91. 91. 의료적 관점에서도 동의할 수 있는 해결책인가 • 의료 전문가 (의사)의 조언이 필요하다. • 과학적/의학적 설득력이 없는 (a.k.a. 사이비) 서비스/제품은 곤란하다. • 의료 현실에 맞지 않는 서비스는 외면 당하거나, 극심한 반대에 부딪힌다.
 
 • 직원 중에 의사가 꼭 있을 필요는 없지만, 언제든 조언을 얻을 수 있는 분은 필요하다. • 의사들 사이에서도 성향 차이 / 의견 차이가 존재한다.
  92. 92. 한국 의료 시스템의 특수성을 이해하라 • 한국 의료 체계는 미국과는 크게 다르다. • 국내 의료 시스템의 특성을 명확히 파악할 필요가 있다. • 의료 접근성, 의료 보험 체계, 의료 수가 등등 • 미국에서 통했던 것이, 한국에서는 통하지 않거나 / 아예 불법일 수 있다. • 그렇다고 꼭 국내 시장에 국한될 필요는 없다.
  93. 93. CellScope’s iPhone-enabled otoscope
  94. 94. • NewYork • First-time home visit $50; regular visits $200; physical $100
  95. 95. 근거를 만들어야 한다. 데이터, 데이터, 데이터!
  96. 96. • 헬스케어/의료 서비스는 근거가 필수적이다. • 하지만 그렇지 못한 것이 현실 applications, from photometric diagnostics to medical-grade imaging (16).Taking advantage of these properties, newly developed devices permit the automated determination of refractive error merely by having an individual look through a lens attached to a smartphone (17). Another transportable imaging capability involves the enabling of remote diagnosis through the use of a smartphone case with an attached otoscope (for detecting an ear infection) (18), multimodal colposcope for cervical cancer identification (19), or optical screening tool for potentially cancerous oral lesions (20). Dermatologic diagnostics may be especially well suited for exploiting the myriad smartphone capabilities for teledermatology (21). The technologies highlighted above can improve care simply through their ability to markedly in- crease the accessibility and convenience of care by bringing clinic- and hospital-quality moni- toring and diagnostics to the point of need. How- ever, their greatest potential might be in allowing for the complete redefining of “normal” physio- logical responses and in enhancing our under- standing of the natural histories of poorly defined chronic conditions. Continuous beat-to-beat moni- toring of blood pressure throughout daily activities will help to refine the catchall diagnosis of “essential hypertension” as multiple distinct phenotypes. Similarly, understanding individual varia- views conclude that high-quality evidence is lacking for the use of mHealth to effect behavioral changes or to manage chronic diseases, 1000 Funding ($) in millions Publications Funding($inmillions) 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 800 600 400 200 0 10,000 8000 6000 4000 2000 0 WoSpublications(number) Fig. 2. mHealth taking center stage. Measures are funding and number of related publications. Shown are the annual total funding for patient-facing mHealth companies and the annual num- ber of related publications [identified with Web of Science (WoS) using search terms “telemedi- cine” and “mhealth*” and “digital health” and “digital medicine”]. Funding data provided by B. Dolan and A. Pai of MobiHealthNews. R E V I E W onApril27,2015emag.org 근거를 만들어야 한다.
  97. 97. 근거를 만들어야 한다. • 의료 기관과의 협업이 필요할 가능성이 높다. • 하지만 의료 기관과 일하기 쉽지 않다. • Right person, Right hospital, Right department, Right time… • 의사의 관심과 스타트업의 관심사가 다르다. • 의사와 스타트업의 공통점: 리소스가 턱없이 부족하다. • 가장 좋은 근거는 역시 임상 연구 결과 • 연구 조건은 case by case. • Randomised, Double-blinded, controlled trial. • 충분한 N 수, 충분한 기간
  98. 98. 헬스케어는 규제 산업이다 … 우린 아마 안 될꺼야.
  99. 99. 헬스케어는 규제 산업이다 • 규제는 본질적으로 기술의 발전을 뒤따를 수 밖에 없다. • 국내 규제 상황은 별로 좋지 않다. • 합리성, 일관성, 불확실성 • 싫든 좋든, 규제를 개척하는 것도 역할의 하나이다. • 초기에 식약처 등 관련 기관을 컨택하는 것도 필요하다.
  100. 100. 디지털 헬스케어를 이해하는 VC는 아마 없을 것이다. 투자 이상은 기대하기 어렵다
  101. 101. 디지털 헬스케어를 이해하는 VC는 아마 없을 것이다. • IT를 이해하는VC 는 많다. • 바이오를 이해하는VC 도 적지 않다.
 • … 하지만 둘 다 이해하고 • 네트워크와 통찰력을 가진VC는 거의 없다.
  102. 102. PRESENTATION © 2015 ROCK HEALTH Digital health continues to receive funding from a wide range of funds with a noticeable growth in the number of active corporate investors. 13 2 2 5 4 5 3 2 10 4 5 7 8 8 MOST ACTIVE FUNDS Deals in 2015 & cumulative deals 2011-2015 8 Source: Rock Health Funding Database Note: Only includes U.S. deals >$2M; data through December 8, 2015 10 1 3 3 4 3 3 24 2 3 4 10 10 13 4 4 2 4 6 7 2 3 11 2 3 1 2 2 3 4 2 8 20 6 9 5 20 5 12 11 5 5 4 4 4 6 12 4 4 # of deals LEGEND 2015 Seed / A by venture funds 2015 Total by venture funds 2015 Seed / A by corporate funds 2015 Total by corporate funds 2011-2015 Cumulative seed / A 2011-2015 Cumulative total 4 4 8 6 6 5 4 4 18 17 14 7 17 23 15 14 18 20 4 5 4 5 4 5 14 27 4 4 https://rockhealth.com/reports/digital-health-funding-2015-year-in-review/
  103. 103. 디지털 헬스케어를 이해하는 엑셀러레이터도 없을 것이다. 마찬가지다.
  104. 104. 헬스케어 도메인 전문가 의사/병원과의 협력 헬스케어 창업 및 exit 경험 있는 기업가 초기 투자 등 자금 조달 전문가 제조 기술 전문가 및 지원 서비스 해외 시장 개척 및 해외 투자 유치 지원 0 2 4 6 8 10 12 14 16 18 디지털 헬스케어 엑셀러레이터에게 가장 필요한 것은? Source: Mobile Healthcare | 웨어러블 디바이스, 모바일 헬스케어 https://www.facebook.com/groups/koreamobilehealthcare/
  105. 105. 권장 도서
  106. 106. Feedback/Questions • Email: yoonsup.choi@gmail.com • Blog: http://www.yoonsupchoi.com • Facebook: 최윤섭 디지털 헬스케어 연구소

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