Clinical Positioning and Implementation of a Deep-Learning Retinal Biomarker (Reti-CVD) for Cardiovascular Risk Stratification: A Narrative Review.

Retinal imaging directly visualizes the systemic microvasculature, and deep-learning oculomics may provide complementary risk information.

Reti-CVD generates a three-tier classification from a retinal photograph and is among the more extensively validated retinal-AI tools.

RetiCAC was trained using coronary artery calcium as a surrogate label; subsequent Reti-CVD studies included UK Biobank, Singapore SEED, and CMERC-HI.

Most evidence originates from one research group and one commercial algorithm, and no randomized or outcome-based study has shown that Reti-CVD-guided care improves clinical outcomes.