Abstract
Background
AI-enabled retinal imaging provides a non-invasive method for assessing cardiovascular risk by identifying microvascular changes linked to atherosclerotic cardiovascular disease. This study assessed the feasibility and clinical associations of the AI-derived retinal risk score (Reti-CVD) in a hybrid care model and compared its categorical alignment with the American Heart Association PREVENT equation as a comparator risk model.
Methods
In this retrospective cross-sectional study, 1,461 adults aged ≥40 at Metabolic underwent non-mydriatic retinal imaging. Fundus photographs were analyzed using the Dr. Noon AI platform to classify Reti-CVD risk. Associations with cardiometabolic characteristics were examined, and categorical agreement with PREVENT was assessed using Cohen’s kappa. ROC analyses evaluated the alignment of the continuous Reti-CVD score with PREVENT thresholds.
Results
Higher Reti-CVD risk was significantly linked to older age, male sex, diabetes, hypertension, elevated HbA1c, higher blood pressure, lower HDL cholesterol, higher triglycerides, and older AI-predicted vascular age (all
p
κ
= 0.144; weighted κ = 0.241). Among patients classified as low risk by PREVENT, 49.1% were categorized as moderate or high by Reti-CVD.
Conclusion
AI-enabled retinal imaging was feasible in a hybrid setting and was associated with clinically plausible cardiometabolic risk gradients. Reti-CVD showed limited categorical agreement with PREVENT. However, the findings are hypothesis-generating and require prospective validation against ASCVD events before claims, improved prediction, or beneficial reclassification.