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

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Cardiovascular diseases (CVD) is the most common cause of death worldwide. While traditional risk factors, such as hypertension, hyperlipidemia and cigarette smoking, allow physicians to identify, monitor and treat patients at high-risk of CVD, a substantial proportion of CVD morbidity is not explained by these traditional risk factors. As a result, there is increasing interest in finding additional biomarkers for CVD risk stratification.

The retinal blood vessels, which are accessible to direct non-invasive visualization, share similar anatomical and physiological characteristics with microcirculation in the body. For over three decades, many researchers have sought to measure retinal blood vessel changes from photographs of the retina using imaging software, typically with human assessors. In our recent study in Nature Biomedical Engineering, we developed an artificial intelligence (AI) deep learning convolutional neural network (CNN) to measure the calibre of retinal blood vessels. We were able to test this AI system in diverse multi-ethnic, multi-country datasets in more than 60,000 retinal images. We found a high correlation of the AI system with validated human measurements, and demonstrated comparable (or stronger) relationships of AI-measured retinal vessel calibre with classic CVD risk factors (e.g., blood pressure). We were also able to demonstrate that AI-measured retinal vessel calibre could be predictive of incident CVD events.

So what is the next step? We are in contact with other collaborative groups to test the AI system, although there are challenges in data sharing in the current environment. We hope to create a “Retinal vessel CVD risk score” based on the AI system and compare this with traditional CVD risk equations. We are excited that advancement in AI in medical imaging has now allowed us to significantly improve our ability to study the human microcirculation via the retina vessels. Further evaluation of the utility of the AI system will allow translation to improve clinical management of CVD.


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Carol Cheung

Assistant Professor, The Chinese University of Hong Kong

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