Abstract
<title>Abstract</title> <p>Ultra-widefield (UWF) retinal imaging enables comprehensive visualization of retinal pathology but introduces substantial heterogeneity across diseases, clinical tasks, and imaging devices, limiting the applicability of existing retinal foundation models. Here we present UWF-FM, a foundation model pretrained using self-supervised learning on 632,627 unlabeled UWF retinal images. We evaluated UWF-FM on 27,641 labeled image–task pairs and 10,686 external images spanning multiple levels of retinal assessment, including characterization of 28 retinal findings involving both posterior-pole and peripheral pathology, diabetic retinopathy grading, and diagnosis of 12 retinal diseases and tumors. Across diverse tasks, patient cohorts, and imaging systems, UWF-FM consistently outperformed foundation models pretrained on non-UWF data. Performance gains were maintained across external datasets, clinically relevant operating thresholds, and cross-vendor evaluation (Optos to Zeiss) without additional adaptation. In a prospectively registered randomized crossover multi-reader study (ClinicalTrials.gov: NCT07651943; 600 UWF images; four ophthalmologists), AI assistance increased clinician sensitivity with minimal loss of specificity. During retrospective and prospective silent deployment involving 37,665 consecutive examinations, UWF-FM achieved a 100% processing success rate, a median inference time of approximately 1.1 seconds per examination, and no workflow disruption. These findings establish UWF-FM as the first foundation model for UWF retinal imaging and demonstrate that UWF-specific foundation modeling provides a scalable framework for robust, generalizable, and clinically deployable retinal decision-support systems.</p>