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A deep-learning system for the assessment of cardiovascular disease risk via the measurement of retinal-vessel calibre


Retinal blood vessels provide information on the risk of cardiovascular disease (CVD). Here, we report the development and validation of deep-learning models for the automated measurement of retinal-vessel calibre in retinal photographs, using diverse multiethnic multicountry datasets that comprise more than 70,000 images. Retinal-vessel calibre measured by the models and by expert human graders showed high agreement, with overall intraclass correlation coefficients of between 0.82 and 0.95. The models performed comparably to or better than expert graders in associations between measurements of retinal-vessel calibre and CVD risk factors, including blood pressure, body-mass index, total cholesterol and glycated-haemoglobin levels. In retrospectively measured prospective datasets from a population-based study, baseline measurements performed by the deep-learning system were associated with incident CVD. Our findings motivate the development of clinically applicable explainable end-to-end deep-learning systems for the prediction of CVD on the basis of the features of retinal vessels in retinal photographs.

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Fig. 1: An example of CRAE and CRVE prediction using a DLS (SIVA-DLS).
Fig. 2: Comparison between SIVA-DLS and SIVA-human in cases with different CVD risk factors.

Data availability

The main data supporting the results in this study are available within the paper and its Supplementary Information. The deidentified individual-participant data and data on the evaluation of retinal photographs used in the SIVA-DLS are available on request from the corresponding author or C.Y. Cheung (e-mail:

Code availability

The custom code is currently available only on request because it is under a patent examination process.


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We thank all of the staff at the SNEC Ocular Reading Centre (SORC) for their contribution to this study. We acknowledge funding support from Singapore Ministry of Health’s National Medical Research Council (NMRC) grants OFLCG/001/2017, NMRC/STaR/003/2008, NMRC/STaR/0016/2013 and NMRC/CIRG/1371/2013. This research is also supported by the National Research Foundation, Singapore, under its AI Singapore Programme (AISG Award no. AISG-GC-2019-001). Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not reflect the views of the National Research Foundation, Singapore.

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C.Y. Cheung, D.X., W.H., M.L.L. and T.Y.W. contributed to study conception and design. D.X., W.H. and M.L.L. coded and optimized the DLS. C.Y. Cheung and M.Y. analysed the data. C.Y. Cheung and T.Y.W. contributed to the initial draft of the manuscript. C.Y. Cheung and T.Y.W. had full access to the data, vouch for the integrity of the data and the adherence to the study protocol, and were responsible for the decision to submit the manuscript. All of the authors contributed to data collection and interpretation, and revision of the manuscript for important content.

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Correspondence to Tien Y. Wong.

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C.Y. Cheung, D.X., W.H., M.L.L. and T.Y.W. are filing a patent (World Intellectual Property Organization International Bureau; International publication no. WO 2020/167251) for the DLS described in this study.

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Cheung, C.Y., Xu, D., Cheng, CY. et al. A deep-learning system for the assessment of cardiovascular disease risk via the measurement of retinal-vessel calibre. Nat Biomed Eng 5, 498–508 (2021).

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