20160119 디지털 헬스케어 의사모임 1월 전체 파일 v3

디지털 헬스케어
의사모임 1월 모임 발표자료
2016.1.19

디지털 헬스케어
업데이트
김치원
2016.1.19

IBM Watson + Medtronic: CGM을 통한 저혈당 예측 알고리즘
• CES 2016에서 발표
• Medtronic과 IBM 왓슨의 협
업으로 지속형 혈당 측정계를
사용해서 저혈당 발생을 최대 3
시간 전에 예측할 수 있는 알고
리즘을 개발했다고 발표
• 당뇨병 관리 서비스를 출시할
예정이라고 발표

활동량 측정계의 겨울 1: Fitbit, 애플워치와 유사한 신제품 출시 후
주가 급락

활동량 측정계의 겨울 2: 조본, Down-round

Fitbit의 심박수 측정이 정확도에 대한 집단 소송에 제기됨

Proteus의 스마트 알약을 Barton Health System병원에서 사용
• Proteus 회사가 만든 스마트 알약 Helius는
임상 시험 용으로 발표되었고 오츠카제약의
Abilify와 결합한 상태로 FDA 승인 잘차를
밟고 있음
• 캘리포니아의 지역 병원인 Barton Health
System에서 고혈압 약물에 대해서 사용하겠
다고 발표
• 이슈
• FDA 승인은 필요 없는지?
• 병원에서 지속적으로 사용 가능할 지?

Validic: 스마트폰 카메라를 사용한 의료기기 데이터 수집

빅데이터 분석:
유전체 정보와 개인라이프로그 정보 활용
서울대 최형진9

Contents
1. Introduction
2. Human Genome Project and Beyond
3. Genome Data
① Cancer Targeted Therapy
② Disease Risk (Common, Rare)
③ Pharmacogenomics
④ Others (Fetal DNA, Microbiome)
4. Sensor/Mobile Data
5. Electrical Health Records
6. National Healthcare Data
7. Medical Images
8. Biomedical Big Data + Artificial Intelligence

$215MPrecision Medicine Initiative
2015/1/30

며칠 전 유전자 검사를 받은 40대 남성입니다.
혈액세포의 DNA 상태를 분석해 앞으로 암에 걸릴 위험이 있는지 여부를 판단할 수
있다고 합니다.
2013.4.25. KBS 9시 뉴스

2013.4.25. KBS 9시 뉴스
60년전 DNA의 구조가 밝혀진 이래 2003년 인간 유전자 지도가 완성됐고, 현재는 어떤
유전자가 어떤 질병을 일으키는지 분석도 80% 정도 끝난 상태입니다.
예를들어 13번 염색체의 BRCA2 유전자에 이상이 생기면 유방암에 걸릴 확률이
높습니다. 또 17번 염색체 유전자는 난소암, 7번 염색체 유전자는 비만을 일으킵니다.

21
Heart Disorder 99% Probability
Life Expectancy 30.2 Years

Promise of Human Genome Project

Tissue Specific Expression
Comprehensive Catalogues of Genomic Data
Variation in the human genome
Mendelian (monogenic) diseases
(N=22,432)
Whole genome sequencing (N=1,000)
Four ethnic groups
(CEU, YRI, JPT, CHB, N=270)
GWAS catalog
Complex (multigenic) traits
(1926 publications and 13410 SNPs)
Disease-related variations
Functional elements
2014-06-29
25

All Major Tissues/Organs
All Proteins + All RNAs
2015 Science Tissue-based map of the human proteome
1. Immunohistochemistry (IHC)
24,028 antibodies
(16,975 proteins)
 >13 million IHC images
2. RNA-sequencing
(N=44)
26

111 Reference Human Epigenomes
2015.2.19. Nature. Integrative analysis of 111 reference human27

Data Dimensions
2015.2.19. Nature. Integrative analysis of 111 reference human29

Network-based Model of
Disease-disease Relationship
2015 Science Uncovering disease-disease relationships through the incomplete interactome30

Hypothesis Driven Science Data Driven Science
Hypothesis
Collect
Data
Data
Generate
Hypothesis
Analyze
Analyze

Candidate Gene
Approach
Genome-wide
Approach
Choose a Gene
from Prior Knowledge
Analyze the Gene
Analyze ALL Genes
Discover Novel Findings

GWAS
(Genome wide association study)
SNP chip
Whole Genome
SNP Profiling (500K~1M SNPs)
Common Variant
Choi HJ, Doctoral Thesis

Estrada et al., Nature Genetics, 2012
+ novel targets
for bone biology
Recent largest GWAS
GEFOS consortium

2010 An Environment-Wide
Association Study (EWAS) on
Type 2 Diabetes Mellitus
Environment-Wide Association Study (EWAS)
 다양한 환경인자들 

GWAS  PheWAS
Phenotype-wide Association Study

1000 개의 질병들
Bioinformatics. 2010 PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene-disease associations.
Phenotype-wide Association Study

Genome-Envirome-Phenome-wide
Association Study
Phenome-wide
(Lab,Diagnosis)
Proposal
(Choi)
Genome-wide
Environment-wide
(Life style, diet, exercise, pollution)

Anatome-Phenome-wide
Association Study
2015.2.19. Nature. Genetic and epigenetic fine mapping of causal autoimmune disease
Phenome
Anatome

저의 유전자
분석 결과를
반영하여
진료해주세요!! 헠?
41

Disease genetic susceptibility
Cancer driver
somatic mutation
Pharmacogenomics
Targeted
Cancer Treatment
(EGFR)
Causal
Variant
Targeted Drug
(MODY-SU)
Drug Efficacy/Side Effect
Related Genotype
(CYP, HLA)
Genetic Diagnosis
(Mendelian,
Cystic fibrosis)
Molecular
Classification
- Prognosis
(Leukemia)
Hereditary
Cancer
(BRCA)
Microbiome
(Bacteria,
Virus)
Genomic Medicine
Risk prediction
(Complex disease,
Diabetes)
Germline Variants
Fetal DNA

Cancer Targeted Therapy
45
Targeted TherapyGenetic TestCancer

Cancer Rebiopsy
2013 JCO Genomics-Driven Oncology- Framework for an Emerging Paradig

(출처: 금창원 대표님 블로그)
$99
TV CF
50

GWAS
N=339,224 individuals
97 BMI-associated loci

Tissue Specific Gene Expression
2015 Nature Genetic studies of body mass index yield new insights for obesity biology
- Hypothalamus, Pituitary gland
(appetite regulation)
- Hippocampus, Limbic system
(learning, cognition, emotion,
memory)

Gene Set Enrichment Analysis
2015 Nature Genetic studies of body mass index yield new insights for obesity biology

Per-allele effect of BMI-associated
loci on body weight
2012 Genetic determinants of common obesity and their value in prediction

2011 Hum Genet. Type 2 diabetes and obesity- genomics and the clinic

2015 Nature Genetic studies
of body mass index yield new
insights for obesity biology
Blue: Previous
Red: Novel

2011 Hum Genet. Type 2 diabetes and obesity- genomics and the clinic
TCF7L2

◇◆ ‘parental obesity’
as a test to predict obesity
in adult life
•Dark blue 1–2 yrs
•Green 3–5 yrs
•Red 6–9 yrs
•Light blue 10–14 yrs
•Grey , 15–17 yrs
Genetic Prediction of Obesity Risk
The predictive ability of
the currently
established BMI-
associated loci is poor
2012 Genetic determinants of common obesity and their value in prediction

Influence of Genetics on Human Disease
For any condition the overall balance of
genetic and environmental determinants
can be represented by a point somewhere
within the triangle.
61
Single
Locus /
Mendelian
Multiple
Loci or multi-
chromosomal
Environmental
Cystic Fibrosis
Hemophilia A
Examples:
Alzheimer’s Disease
Type II Diabetes
Cardiovascular Disease
Diet
Carcinogens
Infections
Stress
Radiation
Lifestyle
Gene = F8
Gene= CFTR
F8 = Coagulation Factor VIII
CFTR = Cystic Fibrosis Conductance Transmembrane Regulator
Lung Cancer

2008 HMG Genome-based prediction of common diseases- advances and prospects62

2008 HMG Genome-based prediction of common diseases- advances and prospects63

Diabetes ≠ Genetic Disease?
• Familial aggregation
– Genetic influences?
– Epigenetic influences
• Intrauterine environment
– Shared family environment?
• Socioeconomic status
• Dietary preferences
• Food availability
• Gut microbiome content
• Overestimated heritability
– Phantom heritability
2012. Drong AW, Lindgren CM, McCarthy MI. Clin Pharmacol Ther. The genetic and epigenetic basis of type 2 diabetes and obesity.
2012. PNAS The mystery of missing heritability- Genetic interactions create phantom heritability65

Rare Variant in a Specific Population
• 3756 Latino: whole exome sequencing
 A rare functional variant in candidate gene
 14276: replication  Not found in other ethnic group
2014 JAMA Association of a Low-Frequency Variant in HNF1A With Type 2 Diabetes in a Latino Population

2014 NEJM Null Mutation in Hormone-Sensitive Lipase Gene and Risk of Type 2 Diabetes
Rare Functional Variant
= Monogenic Heritable Disease
All Amish

Variants and Disease Susceptibility
2008 NRG Genome-wide association studies for complex traits- consensus, uncertainty and challenges

Genotype Based Diabetes Therapy
Diabetes due to KATP channel mutations  sulphonylurea
2007 American Journal of Physiology - Endocrinology and Metabolism. ATP-sensitive K+ channels and disease- from molecule to m

Mendelian (single-gene)
genetic disorder
Known single-gene
candidates testing
Whole Genome or Whole
Exome Sequencing
72

Laboratory Director
강현석
73

2012 European Heart Journal. Personalized medicine: hope or hype?
Herceptin
Glivec
76

77
Pharmacogenomic Biomarkers
in Drug Labeling (N=166)
2015.9.14.
Atorvastatin, Azathioprine,
Carbamazepine, Carvediolol,
Clopidogrel, Codein,
Diazepam…..

Large Effect Size Variant?
Disease susceptibility variant Pharmacogenetic variant
Environmental
Exposure
Drug
Exposure

Subjects and Strategy
Groups Case 1 Case 2 Control 1 Control 2 Control 3
Role Discovery Discovery Discovery Discovery Validation
Status
Agranulocyto
sis Graves
Agranulocyto
sis Graves
General
Population
General
Population
Graves (no-
agranulocytosis)
Source Hospital Hospital KPGP Open DB Hospital
N 7 19 392 207, 4128 55
Genotyping
WES O O Available
HLA Typing
(6 loci)
O Available
Sanger
Sequencing
O O (n=10) O O
HLA Typing
(3 loci)
O (n=8) Available
HLA Typing
(1 locus)
O (n=10) O

CHR SNP BP A1 A2
Case
Frequency
Control
Frequency
P OR
4 4:36340834 36340834 T C 0.2143 0.002551 4.23E-05 106.6
6 rs112749594 32485397 A G 0.5 0 1.08E-06 NA
6 6:32485410 32485410 T G 0.5 0 1.08E-06 NA
6 6:32485448 32485448 G A 0.5 0 1.08E-06 NA
6 6:32485451 32485451 G A 0.5 0 1.08E-06 NA
6 rs75563047 32485492 T C 0.5 0 1.08E-06 NA
6 rs78287329 32485511 T G 0.5 0 1.08E-06 NA
6 rs115308342 32485705 G A 0.5 0 2.19E-06 NA
6 6:32485722 32485722 A G 0.5 0.006098 9.75E-06 163
6 rs114321399 32487474 A G 0.6 0.005682 2.72E-08 262.5
6 rs77015062 32610825 G A 0.8571 0.2376 2.72E-06 19.25
6 rs9273704 32629297 C T 0.7143 0.1104 4.08E-07 20.15
7 rs3800782 150027763 G A 0.6429 0.1582 7.36E-05 9.581
7 rs76490935 150028098 G A 0.6429 0.1582 7.36E-05 9.581
7 rs11978222 150028400 A G 0.6429 0.1582 7.36E-05 9.581
7 rs3800783 150028542 G A 0.6429 0.1569 6.91E-05 9.673
7 rs114973325 150028664 A G 0.6429 0.1582 7.36E-05 9.581
11 rs7396812 373404 A G 0.7143 0.1724 5.81E-05 12
11 rs7395781 373427 T C 0.7 0.03333 4.74E-05 67.67
11 rs4963161 769188 T C 0.2857 0.01403 7.29E-05 28.11
14 14:23861876 23861876 G T 0.2857 0.005249 4.48E-06 75.8
14 14:64693064 64693064 G T 0.2857 0.006667 8.49E-06 59.6
15 rs75893088 28518102 T C 0.5 0.002551 3.06E-12 391
15 15:28518136 28518136 A G 0.3571 0 7.52E-10 NA
17 17:27071310 27071310 G C 0.2857 0.01306 7.90E-05 30.23
19 rs649216 55324635 C T 0.7143 0.003788 1.68E-14 657.5
23 X:132435892 132435892 T G 0.2143 0.001276 1.71E-05 213.5
Results (1) Variant based approach

GWAS - Manhattan plot
HLA
P=2.7 x 10-8
GWS
HERC2
P=3.0 x 10-12
KIR2DL4
P=1.7 x 10-14
Results (1) Variant based approach
False Positive
(sequencing technical error)

Genome-Wide Discovery and Validation
Results (2) Gene based approach
Case (N=7)
Sanger
Validation
Control (N=392)
(General
population) Stage 1
8 locus
16 Sample
(case)
Stage 2
2 locus
16 Sample
(case 1 + control 15)
Stage 3
1 locus
40 Sample
(control)
OPN3 : 1
HLA-DQB1 : 1
SPA17 : 3
TOM1L1 : 2
NLRP9 : 1
HLA-DQB1 :1
NLRP9 : 1
NLRP9 : 1
Whole Exome Sequencing
Functional Variants

Genetic Prediction
Two Markers Combined
Case Case Case Total Severe Case Control
WES Sanger WES+Sanger WES+Sanger Patient
Total 7 10 17 14 55
High risk genotype 7 8 15 14 19
Low risk genotype 0 2 2 0 36
Variant (%) 100% 80% 88% 100% 35%
OR=14.21, P=1x10
-4
Index Agranulocytosis Severe Agranulocytosis
Sensitivity 88% (15/17) 100% (14/14)
Specificity 65% (36/55) 66% (38/58)
Assuming
0.5%
Prevalence
Positive Predictive Value 1.2% 1.5%
Negative Predictive Value 99.9% 100%
Risk (Without testing) 0.5% 0.5%
Results (3) Clinical implication
200 tests needed to avoid 1 case

2013 NEJM A Randomized Trial of
Genotype-Guided Dosing of
Warfarin

2013 NEJM A
Randomized Trial of
Genotype-Guided
Dosing of Warfarin
Median
21 days
Median
29 days
Median
44 days
Median
59 days
P<0.001
P=0.003

Expected Metformin response
Other drug Metformin usual dose Metformin low dose (S/E)
0% -1% -2%-1.5% -2.5% -3%+0.5%
HbA1c change
Good Response
Genotype
Poor Response
Genotype

Genetics of eating behavior
2011 Genetics of eating behavior

Personalized Medicine
Pharmacogenomics
Nutrigenomics
IRS1 SNP GA/AA
High fat/
Low carb
IRS1 SNP GG
Standard
Higher
effect
Similar
effect
2013 Diabetes Care. IRS1 Genotype Modulates Metabolic Syndrome Reversion in Response to 2-Year Weight-Loss Diet Intervention - The POUNDS

Gene-Environment Interaction
Gene Environment
Disease
Genetic Predisposition Score Sugar-Sweetened Beverages

Soda School
No-Soda School
Obese Family Lean Family

Genotype Guided Personalized Treatment
Baseline
Genotyping
- Drug metabolism
- DM etiology
- DM complication
1 week 3 month Long term
Genotype based treatment strategy
- Drug choice
- Drug dose
- Lifestyle modification
- Complication evaluation
New
T2DM

Pharmacogenetic Tests: 최형진
No
Drug
(N= 10)
Gene
(6 genes=8 bioma
rkers)
Target SNPs
(N=12)
#5
(HJC)
Genotype Interpretation Clinical Interpretation
1 Clopidogrel CYP2C19
rs4244285 (G>A) GG
*1/*1
(EM)
Use standard dosers4986893 (G>A) GG
rs12248560 (C>T) CC
2 Warfarin
VKORC1 rs9923231 (C>T) TT
Low dose
(higher risk of bleeding)
Warfarin dose=0.5~2 mg/day
CYP2C9
rs1799853 (C>T) CC
rs1057910 (A>C) AC
3 Simvastatin SLCO1B1 rs4149056 (T>C) TT Normal
4
Azathioprine (AP),
MP, or TG
TPMT rs1142345 (A>G) AA Normal
5
Carbamazepine
or Phenytoin
HLA-B*1502
rs2844682 (C>T) CT
Normal
rs3909184 (C>G) CC
6 Abacavir HLA-B*5701 rs2395029 (T>G) TT Normal
7 Allopurinol HLA-B*5801 rs9263726 (G>A) GG Normal
Clopidogrel1)
: UM/EM=standard dose, IM/PM= consider alternative antiplatelet agent (eg. prasugrel/ticagrelor)
Warfarin2)
: high dose=5~7 mg/day, medium dose=3~4 mg/day, low dose=0.5~2 mg/day
=0
최형진
+1,000,000
?

2014 NEJM Genotype–Phenotype Correlation — Promiscuity in the Era of Next-Generation Sequencing

A Genotype-First Approach to Defining
the Subtypes of a Complex Disease
2014.2.27.

Point-of-care
Genotyping
HyBeacon Probes

Future of Genomic Medicine?
Test when neededWithout information Know your type
Blood
type
Geno
type
Here is my
sequence

111 2015 Sci Transl Med. The emerging field of mobile health

데이터베이스 기반 혈당관리
115

혈당 변화 실시간 모니터링
저녁 식사전 고혈당
117

구글 헬스 앱 분야 매출 1위 ‘눔(noom)’
118

Date Time name foodType calories unit amount
2014-08-09 0 미역국 0 23 1국그릇 (300ml) 105 g
2014-08-09 0 잡곡밥 0 80 1/4공기 (52.5g) 52 g
2014-08-09 0 열무김치 0 3 1/4소접시 (8.75g) 9 g
2014-08-09 0 파프리카 0 6 1/2개 (33.25g) 35 g
2014-08-09 0 토란대무침 0 28 1/2소접시(46.5g) 46 g
2014-08-09 1 복숭아 0 91 1개 (269g) 268 g
2014-08-09 2 마른오징어 2 88 1/4마리 (25g) 25 g
2014-08-09 2 파프리카 0 6 1/2개 (33.25g) 35 g
2014-08-09 2 저지방우유 1 72 1컵 (200ml) 180 g
2014-08-09 2 복숭아 0 183 2개 (538g) 538 g
2014-08-09 3 복숭아 0 91 1개 (269g) 268 g
2014-08-09 3 파프리카 0 6 1/2개 (33.25g) 35 g
2014-08-09 4 파프리카 0 6 1/2개 (33.25g) 35 g
2014-08-09 4 식빵 1 92 1장 (33g) 33 g
2014-08-09 4 삶은옥수수 1 197 1개 반 (150g) 150 g
2014-08-09 4 복숭아 0 91 1개 (269g) 268 g
2014-08-09 4 저지방우유 1 72 1컵 (200ml) 180 g
2014-08-10 0 복숭아 0 91 1개 (269g) 268 g
2014-08-10 0 저지방우유 1 36 1/2컵 (100ml) 90 g
2014-08-10 0 두부 0 20 1/4인분 (25g) 25 g
2014-08-10 0 견과류 2 190 1/4 컵 (50g) 31 g
2014-08-10 0 파프리카 0 11 1개 (66.5g) 65 g
121

식사량 실시간 모니터링
0
100
200
300
400
500
600
아침
아침간식
점심
점심간식
저녁
저녁간식
아침
아침간식
점심
점심간식
저녁
저녁간식
아침
아침간식
점심
점심간식
저녁
저녁간식
아침
아침간식
점심
점심간식
저녁
저녁간식
아침
아침간식
점심
점심간식
저녁
저녁간식
아침
아침간식
점심
점심간식
저녁
저녁간식
아침
아침간식
점심
점심간식
저녁
저녁간식
2014-08-09 2014-08-10 2014-08-11 2014-08-12 2014-08-13 2014-08-14 2014-08-15
점심 과식
저녁 금식
122

운동량 실시간 모니터링
0
5000
10000
15000
20000
25000
걸음 수
운동X
123

스마트폰 활용 당뇨병 통합관리
의사
상담 교육
조회/
분석
교육간호사
매주/필요시
진료 처방
2-3달 간격
평가 회의
매주/필요시
식사/
운동
스마트폰
데이터베이스 서버
자가관리
전송
분석
혈당
측정
혈당측정기
126

피부 사진 원격 진단/처방
129

심전도 원격 진단/처방
131

133http://startupbank.co.kr/board/board_view.html?ps_boid=75&ps_db=repo

134 2015 Cell Metabolism. A Smartphone App Reveals Erratic Diurnal Eating Patterns in Humans that Can Be Modulated for Health Bene
November 3, 2015

135 2015 Cell Metabolism. A Smartphone App Reveals Erratic Diurnal Eating Patterns in Humans that Can Be Modulated for Health Bene

Personalized Nutrition by
Prediction of Glycemic Responses
136 2015 Cell. Personalized Nutrition by Prediction of Glycemic Responses
Received: October 5,
2015;
Received in revised form:
October 29, 2015;
Accepted: October 30,
2015;

Electronic Health Records
2012 NRG Mining electronic health records- towards better research applications and clinical care141

PCA Analysis혈당
신장기능
142

Machine Learning
2014 Big data bioinformatics143

밤동안 저혈당수면 Lt.foot rolling Keep떨림,
식은땀, 현기증, 공복감, 두통, 피로감등의
저혈당 에 저혈당 이 있을 즉알려주도록
밤사이 특이호소 수면유지상처와 통증
상처부위 출혈 oozing, severe pain 알리도록
고혈당 처방된 당뇨식이의 중요성과 간식을
자제하도록 .고혈당 ,,관리 방법 .당뇨약 이해
잘 하고 수술부위 oozing Rt.foot rolling
keep드레싱 상태를 고혈당 고혈당 의식변화
BST 387 checked.고혈당으로 인한 구강 내
감염 위해 식후 양치, gargle 등 구강 위생
격려.당뇨환자의 발관리 방법에 .목표 혈당,
목표 당화혈색소에 .식사를 거르거나 지연하지
않도록 .식사요법, 운동요법, 약물요법을
정확히 지키는 것이 중요을 .처방된 당뇨식이의
중요성과 간식을 자제하도록 .고혈당 ,,관리
방법 .혈당 정상 범위임rt foot rolling중으로
pain호소 밤사이 수면양호걱정신경
예민감정변화 중임감정을 표현하도록
지지하고 경청기분상태 condition 조금 나은 듯
하다고 혈당 조절과 관련하여 신경쓰는 모습
보이며 혈당 self로 측정하는 모습 보임혈당
조절에 안내하고 불편감 지속알리도록고혈당
고혈당 의식변화 고혈당 허약감 지남력
혈당조절 안됨고혈당으로 인한 구강 내 감염
위해 식후 양치, gargle 등 구강 위생
격려.당뇨환자의 정기점검 내용과
빈도에 .BST 140 으로 저혈당 호소 밤동안
저혈당수면 Lt.foot rolling Keep떨림, 식은땀,
현기증, 공복감, 두통, 피로감등의 저혈당 에
저혈당 이 있을 즉알려주도록 pain 및 불편감
호소 WA 잘고혈당 고혈당 의식변화 고혈당
허약감 지남력 혈당조절 안됨식사요법,
운동요법, 약물요법을 정확히 지키는 것이
중요을 .저혈당/고혈당 과
대처법에 .혈당정상화, 표준체중의 유지, 정상
혈중지질의 유지에 .고혈당 ,,관리
방법 .혈당측정법,인슐린 자가 투여법,
경구투약,수분 섭취량,대체 탄수화물,의료진의
도움이 필요한 사항에 교혈당 정상
범위임수술부위 oozing Rt.foot rolling
간호기록지 Word Cloud
Natural Language Processing (NLP)
145

Korean Society
for
Bone and Mineral
Research
Anti-hypertensive
prescriptions
(2008-2011)
N = 8,315,709
New users
N = 2,357,908
Age ≥ 50 yrs
Monotherapy
Compliant user (MPR≥80%)
No previous fracture
N = 528,522
Prevalent users
N = 5,957,801
Excluded
Age <50
Combination therapy
Inadequate compliance
Previous fracture
N = 1,829,386
Final study population
심평원 빅데이터 연구
고혈압약과 골절
Choi et al., 2015 International Journal of Cardiolog151

Compare Fracture Risk
Comparator?Hypertension
CCB
High
Blood Pressure
Fracture
Risk
BB
Non-
user
Healthy
Non-
user
Cohort study (Health Insurance Review & Assessment Service)
New-user design (drug-related toxicity)
Non-user comparator (hypertension without medication)
2007 20112008
Choi et al., 2015 International Journal of Cardiolog152

2013 Science Structural and Functional Brain Networks- From Connections to Cognition

Green Lines: orexigenic pathways
Red lines: dopaminergic pathways
2013 The contribution of brain reward circuits to the obesity epidemic
2014 Obesity – A neuropsychological disease- Systematic review and neuropsychological mode
A, amygdala;
H, hypothalamus;
NA, nucleus accumbens,
PFC, prefrontal cortex;
VTA, ventral tegmental area
156
tDCS Neuromodulation Controls
Feeding Behavior via
Food Reward Activity and Connectivity
Neuromodulation Brain Activity Feeding Behavior
Brain Connectivity

Brain Functional Connectivity
R=0.648
p=0.043
tDCS effect on
right dlPFC - right thalamus
connectivity
tDCS effect on fullness score
Spearman’s corre
dlPFC (dorsolateral prefrontal cortex)
Thalamus

fMRI analysis
2013 Science Functional interactions as big data in the human brain

161
2012 Decoding subject-driven cognitive states with whole-brain connectivity patterns

2014
Radiomics
2014 Nature Communications. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach

Quantitative nuclear morphometry
2015 Laboratory Investigation. Novel genotype-phenotype associations in human cancers enabled by advanced molecular platforms and
computational analysis of whole slide images

Multidimensional Architecture
167
Gene
Hormone
Psychology
Behavior
Phenotype
Outcome
Metabolic
Disease
Vascular
Disease
Environment

Healthcare Big Data
+ Artificial Intelligence
168
Healthcare Big Data Machine Learning
Novel Insights and Applications

174
In a scan of 3,000 images,
IBM technology was able to
spot melanoma with an
accuracy of about 95
percent, much better than
the 75 percent to 84 percent
average of today's largely
manual methods.
IBM Research will continue
to work with Sloan Kettering
to develop additional
measurements and
approaches to further refine
diagnosis, as well as refine
their approach through larger
sets of data.
Dec 17, 2014

175
Aug. 11, 2015
IBM is betting that the same technology that
recognizes cats can identify tumors and other
signs of diseases.
In the long run, IBM and others in the field hope
such systems can become reliable advisers to
radiologists, dermatologists and other practitioners
who analyze images—especially in parts of the
world where health-care providers are scarce.
While IBM hopes Watson will learn to interpret
Merge’s images, it also expects the combination of
imagery, medical records and other data to reveal
patterns relevant to diagnosis and treatment that a
human physician may miss, ushering in an era of
computer-assisted care. Two other recent IBM
acquisitions, Phytel Inc. and Explorys Inc., yielded
50 million electronic medical records.

177
Environment
Gene
Eat
Exercise
Metabolism
Brain
Glucose
DM
Blood Pressure
HTN
Cardiovascular
Disease
Cognitive
Hormone
Behavior
Psychotherapy
Behavior Therapy
Policy Making
Genetic Testing
Neuroimaging
Neuromodulation
Drug
Drug
Lab
Survey
Survey
Sensor
DrugDiagnosis
EMR
Government
Data
Mining

Environment Survey
NeuroimagingGenetics Lab/Hormone Hospital
Cognitive
Personalize
Psychotherapy
Dietary
Intervention
Exercise
Intervention
Food
Exercise
Glucose
Mobile
Drug
Neuromodulation
Monitoring

CES 2016 후기
삼성전자 C-lab WELT
강 성 지

• 1월 6일~9일
• 17만명 이상 방문
• 프레스 6,000명
• 1조원 이상 매출

그나마 라스베가스라 가능한…

기존 CES 기간
숙소 4만 8000원/박 48만원/박
비행기 왕복 160만원 왕복 290만원
렌트 40만원/주 50만원/주
주차 공간 부족
UBER 가격이 Taxi 보다 비쌈
고든 램지 버거. 1시간 30분 waiting…

유레카 파크
• 575개 업체 출품

소박한 부스
• 등록비
– 1000달러
• 전원+랜선
– 1300달러
• 그외,
돈만 있으면
뭐든지 가능

• http://news.samsung.com/global/video-c-lab-
projects-highlight-startup-spirit-inside-samsung

Smart Interactive Stroke Rehabilitation System
RAPAEL Smart Glove and system is designed to help people recover from central
nervous system disorders. The games contents of the RAPAEL training system are
designed to delicately induce brain plasticity and guide muscle re-education in stokes
patients.

What is Stroke?
Any disease process that disrupts blood flow to a focal region of the brain
- Bleeding into brain and surrounding tissue
- Busted pipe
Ischemic stroke
- Blood clot stops the flow of blood
- Clogged pipe
20%80%Cerebral Hemorrhage
WE INSPIRE HOPE

New view
What is Stroke Rehabilitation?
- Compensation
- The adult brain cannot be changed
Old view
Nudo et al., 1992,
1996
WE INSPIRE HOPE
- Remediation
- We can induce changes in the brain and the
neuromuscular system via exercise and therapy.
→ Neuro-plasticity

What is Stroke Rehabilitation?
Ontario Stroke Rehabilitation Consensus Panel 2007
“Stroke rehabilitation is a progressive,
dynamic, goal-oriented process aimed at
enabling a person with an impairment to
reach his or her optimal physical, cognitive,
emotional, communicative and/or social
functional level.”
WE INSPIRE HOPE

Four Principles for Effective Stroke Rehabilitation
Skill Acquisition of Function Task
- Animal model (Nudo et al., 1996)
- Definite evidences from human studies
(Butefisch, 1995; Wolf et al., 2006; Wolf et al., 2008)
- Limited time of actual therapeutic activity
(Lincoln et al, 1996)
- Reduced quality of life (Duncan et al., 1999)
- Functional task-specific training for stroke patients
(Wolf et al., 2006)
- Goal-oriented functional tasks
(van Vliet et al., 1995; Wu et al., 2000)
Intensive Task Practice
Individualized Adaptive
Training- Practice is “a particular type of repetition”
problem-solving (Bernstein, 1967)
- Voluntary movement > passive movement
(Lotze et al, 2003)
- Active guidance is best for learning
(van Asseldonk et al, 2009)
- Challenging tasks for neurorehabilitation
(Nudo et al., 1996)
- Computational study: Failure in learning too
difficult tasks (Sanger et al, 2004)
- A performance based algorithm
(Choi et al, 2008)
Active Participation
WE INSPIRE HOPE

A more specific method for the principles
Adaptive Practice Scheduler
WE INSPIRE HOPE

More specific rehab tools or robots (for upper extremities)
ADAPT (Choi et al,
2009)
ADLER (Johnson et al,
2006)
AutoCITE (Lum et al, 2005)
WE INSPIRE HOPE

30 minute-session for in and out patients
Big devices occupies space
Only 2 years for medical insurance
Outpatients mostly needs family’s help to visit hospital
Therapists want to be recognized as experts by using
high-tech devices
No robot rehabilitation category for medical insurance
There is not so much ways to show the rehabilitation
progress of patients
Many patients give up rehabilitation
Not so many places to do rehabilitation other than
Hospital
Rehab training is mostly boring to patients
.
.
.
Insights
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
Reality of Stroke Rehabilitation, Medical Service
Affinity Analysis (Interview and Observe 5 major rehab hospitals in Korea)
WE INSPIRE HOPE

Persona type
Reality of Stroke Rehabilitation, Patients
Based on analysis of postings in stroke patients’ community
Irrational Rational
Passive
Active
WE INSPIRE HOPE

Emerging Techs in Stroke Rehabilitation
Software for
changing
behaviour
(Games and digital
coaching)
Analytics
&
big data
Devices that
Integrate
multiple sensors
and interfaces
Communication
Networks
& tools
WE INSPIRE HOPE
- Emerging Technologies in Physical Therapy and Rehabilitation in Europe, 2013
-

WE INSPIRE YOU TO
HOPE
Rapael smart rehab solution
RAPAEL
Smart Glove
Game-like Exercises Data Visualization
SMARTREHABILITATION
by affordable
device and service

Values – Physician/Patient/Hospital
WE INSPIRE HOPE
Data-based
Planning &
tracking
Motivation
Feeling of
achievement
Reduced resource
requirement
(space, labor, others)

Three principles for product design & development
WE INSPIRE HOPE
Affordabl
e
portabl
e
ligh
t

Hardware – rapael smart Glove Components
WE INSPIRE HOPE
- Tablet PC : Android OS /
Touchscreen
(inc. power adapter &
cord)
- Smart Glove : Left & Right
- Silicone pad : 2 units
- Body band : 2 units
- Battery : 3 units
- Battery charger : 1 unit
- Instruction for user

Smart glove - Key Features & Technologies
WE INSPIRE HOPE
Bending Sensor is a variable resistor that changes
as it is bent. The sensor is connected to computer
system which can accurately compute the amount
of individual finger movements.
Bending sensor
technology
Key
featuresLightweight
Portable
Sensor Technology
Wireless
Ergonomic
Easy wearing
Easy cleansing
✓
✓
✓
✓
✓
✓
✓
- 3 acceleration channels
- 3 angular rate channels
- 3 magnetic field channels
9-axis movement
& position sensor
Engineered to be suitable for rehabilitation
process. Luxurious and sophisticated design.
Ultra-low-power
energylite
32-bit microcontrollers

Software - Rapael rehab platform
WE INSPIRE HOPE
Range of motion (ROM)
- Active
- Passive
Evaluation
- Intensive
- Repetitive
- Task-oriented
(eg. ADL-related)
GAMe-like
exercises Performance data
- Play time
- Number of motions
- ROM data
GAMe result
Performance history
- Progress monitoring
- Data sharing via
printing or emailing
Performance
report

Rapael rehab platform - evaluation
WE INSPIRE HOPE
PROMNeutral
position
arom
With therapist’s help By patient

Rapael rehab platform – Game exercises
WE INSPIRE HOPE

Rapael rehab platform – Performance report
WE INSPIRE HOPE
Performance history by each motion
- Current state, improvement, and progress in the rehabilitation process

Target patients based on field clinical experiences
- Stroke
- SCI (Spinal Cord Injury)
- Parkinson’s disease
- MS (Multiple Sclerosis)
- ALS (Amyotrophic Lateral Sclerosis)
- Rheumatoid arthritis
- Hand burn
- Prosthetic robot hands
WE INSPIRE HOPE
< Stroke
Prosthetic
robot hand >
< hand burn
SCI >

Clinical trial results
- Korea National Rehabilitation Center
- Jan – Oct 2014
- Open label design
Control (conventional OT) n=14
RAPAEL Smart Glove n=15
- Non-inferiority design
- Statistical superiority proven
- Publication plan (Oct-Nov)

Our roadmap for Smart interactive rehab
system

Regulation clearance and plan
WE INSPIRE HOPE
KF
DA
FD
A
ce C-
FDA(complete) (complete)

We won’t let stroke survivors
give up their life
until their full recovery.
Summary

Thank
youFor Your Time & Attention!
GET IN TOUCH WITH US
sy@neofect.com

20160119 디지털 헬스케어 의사모임 1월 전체 파일 v3

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