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

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 KimSung Soo Kim, Sungha Park, Ching Yu Cheng, Tien Yin Wong

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74 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)e306-e316
JournalThe Lancet Digital Health
Issue number5
Publication statusPublished - 2021 May

Bibliographical note

Publisher Copyright:
© 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license

All Science Journal Classification (ASJC) codes

  • Medicine (miscellaneous)
  • Health Informatics
  • Decision Sciences (miscellaneous)
  • Health Information Management


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