AI-powered retinal imaging for early ASCVD risk stratification in a hybrid care model: preliminary findings from SAFER

Vollständiger Abstract

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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 < 0.001). After adjustment, age, female sex, and current smoking were independent predictors. The Reti-CVD score showed moderate discrimination for PREVENT-defined risk thresholds (AUC 0.689–0.757) and slight to fair agreement with PREVENT ( 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.