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New research · Ophthalmology
Journal of clinical medicine · 5d
AI / informaticsJournal of clinical medicine · 2026

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

Junseung Rho, Sung-Goo Kang, Se-Hong Kim, Sang-Wook Song
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OphthalmologyAI / informatics

Reti-CVD's retinal AI risk score shows moderate accuracy for cardiovascular risk

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

Junseung Rho … Sang-Wook Song
Journal of clinical medicine · 2026
Background

Cardiovascular disease (cardiovascular disease) prevention depends on accurate risk stratification before symptoms develop.

Purpose

This narrative review evaluates its clinical positioning and implementation as an exemplar rather than a product endorsement, organizing evidence by cohort, comparing the approach with established scores and subclinical atherosclerosis markers, and considering implementation, regulation, and equity.

Methods

Standard tools such as the Pooled Cohort Equations, QRISK3, and SCORE2 require laboratory data and are less informative in borderline-risk individuals, creating a role for accessible adjuncts.

Results
0.75
score correctly ranked higher vs lower cardiovascular risk about 3 times in 4
More results

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.

More results

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.

“
Conclusion

Reti-CVD is therefore best regarded as a non-invasive risk enhancer for borderline/intermediate-risk reclassification, not as a tool of established clinical utility; independent validation, intervention trials, and cost-effectiveness and reimbursement evidence are needed before broad integration.

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