A multimodal retinal aging clock for biological age prediction and systemic health assessment via OCT and fundus imaging.

Herein we developed age clocks that predict biological age from fundus photography and optical coherence tomography. We evaluated our multimodal models' clinical relevance by examining their associations between predicted biological age and the Charlson Comorbidity Index (CCI). Study 1 assessed how models trained on normal eyes generalize to diseased eyes, and Study 2 tested whether incorporating disease labels improves performance and systemic associations. Models were fine-tuned to the imaging dataset to predict biological age. Linear regressors were trained on chronological and biological features to infer CCI. Gradient-weighted regression activation mapping also generated heatmaps to identify the model's region of focus. Prediction performance improved when trained on both normal and diseased eyes. Predicted biological age showed significantly stronger correlations with CCI than chronological age across both studies, supporting our algorithm's association with this validated measure of mortality. Thus, our algorithm may provide insight into systemic health burdens beyond that of traditional risk assessments.