Journal article
A deep-learning system for the assessment of cardiovascular disease risk via the measurement of retinal-vessel calibre
CY Cheung, D Xu, CY Cheng, C Sabanayagam, YC Tham, M Yu, TH Rim, CY Chai, B Gopinath, P Mitchell, R Poulton, TE Moffitt, A Caspi, JC Yam, CC Tham, JB Jonas, YX Wang, SJ Song, LM Burrell, O Farouque Show all
Nature Biomedical Engineering | NATURE PORTFOLIO | Published : 2021
Abstract
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 chol..
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Awarded by National Medical Research Council
Funding Acknowledgements
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.