AI-THEROSCOPE: AI Detection of Subclinical Atherosclerosis From Retinal Images

Cardiovascular risk scores are widely used for risk stratification but may fail to identify a substantial proportion of individuals with subclinical atherosclerosis who are at increased risk of future cardiovascular events. Vascular ultrasound can directly detect carotid and femoral atherosclerotic plaques but its implementation is limited by the need for trained operators and expert interpretation. The AI-THEROSCOPE study aims to develop and validate an artificial intelligence-based tool capable of detecting subclinical atherosclerosis through the analysis of non-mydriatic retinal fundus images. Participants undergo clinical assessment, laboratory testing, carotid and femoral ultrasound, and retinal fundus photography. The performance of the AI model will be evaluated against vascular ultrasound findings as the reference standard for the presence of subclinical atherosclerosis.

Inclusion Criteria: * Adults aged 18 years or older. * No previous established cardiovascular disease. * Undergoing cardiovascular risk assessment and carotid and femoral vascular ultrasound. * Ability to provide written informed consent. Exclusion Criteria: * Previous acute coronary syndrome, stroke, or peripheral arterial disease. * Previous carotid or femoral vascular surgery or stenting. * Previous ophthalmologic surgery. * Retinal or ocular diseases that significantly affect retinal vasculature or image quality, including moderate or severe diabetic retinopathy, retinal vascular occlusion, advanced hypertensive retinopathy, exudative age-related macular degeneration, or macular edema. * Inability or unwillingness to provide informed consent.