RetFit: A Novel Deep Learning Biomarker Based on Cardiorespiratory Fitness Derived From the Retina

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Abstract

Cardiorespiratory fitness (CRF) is a powerful predictor of cardiovascular events and overall mortality, often surpassing traditional risk factors in prognostic value. However, its clinical use remains limited because current assessments rely on specialized equipment, trained personnel, and lengthy procedures that are often impractical for broad or routine application, especially in at-risk populations. Because CRF is closely tied to vascular health, surrogate measures that capture vascular features may offer a practical alternative for its estimation. Retinal Color Fundus Images (CFIs) provide a non-invasive window into systemic vascular health and have already demonstrated their utility in predicting cardiovascular risk factors and diseases, yet CFIs have yet to be explored for their potential to predict CRF. In this study, we develop RetFit, a novel CRF estimator derived from CFIs by leveraging state-of-the-art vision transformers. RetFit enables a non-invasive, easy-to-acquire CRF proxy, addressing some of the limitations inherent to standard CRF measures and linking retinal imaging to the cardiovascular system. We evaluated RetFits clinical relevance by analyzing its associations with cardiovascular risk factors, disease outcomes, and exploring its genetic architecture, benchmarking it against a submaximal-exercise-test CRF (SETCRF) estimate. We find that RetFit is prognostic of both cardiovascular events (hazard ratios as low as 0.878, 95%CI 0.856 to 0.901, p<0.001) and overall mortality (hazard ratios as low as 0.780, 95%CI 0.754 to 0.801, p<0.001) and significantly associates with the majority of disease states and risk factors explored within our analysis. Although RetFit and SETCRF shared a moderate phenotypic correlation with each other (r=0.45), their significant genetic associations were disjoint. Interpretability analyses revealed that RetFits predictions are driven by the retinal vasculature, with the number of arterial bifurcations showing the strongest association with RetFit ({beta}=0.287, 95%CI 0.263 to 0.311, p<0.001). These findings highlight the potential of retinal imaging as a scalable, cost-effective, and accessible alternative for CRF estimation, supporting its use in large-scale screening and risk stratification in both clinical and public health contexts.

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