ArticleFrontiers in artificial intelligence2026
AI-powered retinal imaging for early ASCVD risk stratification in a hybrid care model: preliminary findings from SAFER.
Article in Frontiers in artificial intelligence, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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 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.
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