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Deep-learning-based cardiovascular risk stratification using coronary artery calcium scores predicted from retinal photographs

Authors
 Tyler Hyungtaek Rim  ;  Chan Joo Lee  ;  Yih-Chung Tham  ;  Ning Cheung  ;  Marco Yu  ;  Geunyoung Lee  ;  Youngnam Kim  ;  Daniel S W Ting  ;  Crystal Chun Yuen Chong  ;  Yoon Seong Choi  ;  Tae Keun Yoo  ;  Ik Hee Ryu  ;  Su Jung Baik  ;  Young Ah Kim  ;  Sung Kyu Kim  ;  Sang-Hak Lee  ;  Byoung Kwon Lee  ;  Seok-Min Kang  ;  Edmund Yick Mun Wong  ;  Hyeon Chang Kim  ;  Sung Soo Kim  ;  Sungha Park  ;  Ching-Yu Cheng  ;  Tien Yin Wong 
Citation
 LANCET DIGITAL HEALTH, Vol.3(5) : e306-e316, 2021-05 
Journal Title
LANCET DIGITAL HEALTH
Issue Date
2021-05
MeSH
Adult ; Aged ; Algorithms* ; Area Under Curve ; Cardiovascular Diseases / diagnosis* ; Coronary Artery Disease / complications* ; Deep Learning* ; Female ; Humans ; Kaplan-Meier Estimate ; Male ; Middle Aged ; Predictive Value of Tests ; Proportional Hazards Models ; ROC Curve ; Republic of Korea ; Retina / diagnostic imaging* ; Risk Assessment / methods* ; Singapore ; United Kingdom ; Vascular Calcification / complications*
Abstract
Background: Coronary artery calcium (CAC) score is a clinically validated marker of cardiovascular disease risk. We developed and validated a novel cardiovascular risk stratification system based on deep-learning-predicted CAC from retinal photographs.

Methods: We used 216 152 retinal photographs from five datasets from South Korea, Singapore, and the UK to train and validate the algorithms. First, using one dataset from a South Korean health-screening centre, we trained a deep-learning algorithm to predict the probability of the presence of CAC (ie, deep-learning retinal CAC score, RetiCAC). We stratified RetiCAC scores into tertiles and used Cox proportional hazards models to evaluate the ability of RetiCAC to predict cardiovascular events based on external test sets from South Korea, Singapore, and the UK Biobank. We evaluated the incremental values of RetiCAC when added to the Pooled Cohort Equation (PCE) for participants in the UK Biobank.

Findings: RetiCAC outperformed all single clinical parameter models in predicting the presence of CAC (area under the receiver operating characteristic curve of 0·742, 95% CI 0·732-0·753). Among the 527 participants in the South Korean clinical cohort, 33 (6·3%) had cardiovascular events during the 5-year follow-up. When compared with the current CAC risk stratification (0, >0-100, and >100), the three-strata RetiCAC showed comparable prognostic performance with a concordance index of 0·71. In the Singapore population-based cohort (n=8551), 310 (3·6%) participants had fatal cardiovascular events over 10 years, and the three-strata RetiCAC was significantly associated with increased risk of fatal cardiovascular events (hazard ratio [HR] trend 1·33, 95% CI 1·04-1·71). In the UK Biobank (n=47 679), 337 (0·7%) participants had fatal cardiovascular events over 10 years. When added to the PCE, the three-strata RetiCAC improved cardiovascular risk stratification in the intermediate-risk group (HR trend 1·28, 95% CI 1·07-1·54) and borderline-risk group (1·62, 1·04-2·54), and the continuous net reclassification index was 0·261 (95% CI 0·124-0·364).

Interpretation: A deep learning and retinal photograph-derived CAC score is comparable to CT scan-measured CAC in predicting cardiovascular events, and improves on current risk stratification approaches for cardiovascular disease events. These data suggest retinal photograph-based deep learning has the potential to be used as an alternative measure of CAC, especially in low-resource settings.

Funding: Yonsei University College of Medicine; Ministry of Health and Welfare, Korea Institute for Advancement of Technology, South Korea; Agency for Science, Technology, and Research; and National Medical Research Council, Singapore.
Files in This Item:
T202102699.pdf Download
DOI
10.1016/S2589-7500(21)00043-1
Appears in Collections:
6. Others (기타) > Gangnam Severance Hospital Health Promotion Center(강남세브란스병원 체크업) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Ophthalmology (안과학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Preventive Medicine (예방의학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
Yonsei Authors
Kang, Seok Min(강석민) ORCID logo https://orcid.org/0000-0001-9856-9227
Kim, Sung Soo(김성수) ORCID logo https://orcid.org/0000-0002-0574-7993
Kim, Hyeon Chang(김현창) ORCID logo https://orcid.org/0000-0001-7867-1240
Park, Sung Ha(박성하) ORCID logo https://orcid.org/0000-0001-5362-478X
Baik, Su Jung(백수정) ORCID logo https://orcid.org/0000-0002-3790-7701
Lee, Byoung Kwon(이병권) ORCID logo https://orcid.org/0000-0001-9259-2776
Lee, Sang Hak(이상학) ORCID logo https://orcid.org/0000-0002-4535-3745
Lee, Chan Joo(이찬주) ORCID logo https://orcid.org/0000-0002-8756-409X
Choi, Yoon Seong(최윤성)
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/184261
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