A deep-learning system for the assessment of coronary heart disease risk via scleral photographs.

Cardiovascular disease is a major cause of death worldwide, especially the coronary heart disease (CHD). Scleral blood vessels provide information on the risk of CHD. Here, we report the development and validation of deep learning system that leverages scleral photographs for assessment of CHD risk, using diverse multi-age datasets that comprise more than 5000 images. Risk assessment of CHD measured by the system and by specialist doctors showed high agreement, with overall accuracy of 0.891 and AUC of 0.942. We further demonstrated that the trained deep learning system predominantly relied on vascular abnormalities as interpretable features for prediction, and in a subset of cases, it also captured pigmentation spots. These findings suggest that the model learns physiologically relevant cues linking scleral changes to CHD, thereby enhancing its clinical interpretability. Our findings motivate the development of clinically application explainable deep learning system for the assessment of CHD risk on the basis of the features of vessels and spots in scleral photographs.