Prediction of incident atrial fibrillation from retinal fundus images using a multimodal foundation model.

Abstract

Atrial fibrillation (AF), a common cardiac arrhythmia, presents significant challenges for early detection and management due to its asymptomatic and paroxysmal characteristics. In this study, we introduce the RetiAF score, a multimodal foundation-model-based biomarker derived from retinal fundus images for early detection of AF. We identified that the RetiAF score demonstrated a robust performance across multi-ethnic, multi-center datasets, achieving AUROCs of 0.8610 and 0.8019 on the UK Biobank (UKBB) development and internal testing datasets, and an AUROC of 0.7803 on an external dataset acquired in Shanghai, China. In addition, we identified that the RetiAF score consistently outperformed the traditional risk scores such as CHARGE-AF (AUROC: 0.7553) and C2HEST (AUROC: 0.7246) for the UKBB internal testing dataset. Multivariable logistic regression and propensity score analyses further demonstrated that the RetiAF score was independently associated with AF risk (p < 0.001). When stratified by higher C2HEST scores (≥3), the RetiAF score achieved an AUROC of 0.9619, highlighting its potential for identifying high-risk patients before the clinical onset of AF. The multimodal hybrid version of RetiAF (Hybrid_RetiAF) score, which incorporated clinical features (e.g., Age, BMI, etc) into the deep learning model, further enhanced predictive performance and achieved AUROCs of 0.8924 and 0.8381 on the UKBB Cohorts. As a secondary exploratory analysis, we evaluated whether RetiAF-derived scores were associated with chronic ischemic heart disease in UKBB, suggesting shared cardio-retinal risk information. These findings underscore the potentials of non-invasive retinal imaging as a scalable tool for AF and cardiovascular risk assessment, offering a promising alternative for large-scale screenings and personalized interventions.