AI-Assisted Interpretation of Ultra-Widefield Retinal Images

The goal of this prospective observational study is to evaluate the impact of artificial intelligence (AI) assistance on clinician interpretation of ultra-widefield (UWF) retinal images. The main questions it aims to answer are: whether AI assistance improves the diagnostic performance of ophthalmologists in detecting retinal findings on UWF retinal images; whether AI assistance improves sensitivity, specificity, and inter-reader agreement across clinicians with different levels of experience. Approximately 600 UWF retinal images prospectively collected from multiple ophthalmic centers in China will be included. Images will be independently annotated by expert retinal specialists to establish reference labels for retinal finding categories. Four ophthalmologists with different levels of clinical experience, including one senior retinal specialist and three junior ophthalmologists, will participate in a crossover multi-reader study. For each clinician, the dataset will be randomly divided into two equal subsets. During the first reading session, clinicians will evaluate one subset without AI assistance and the other subset with AI assistance. After a washout interval of at least two weeks, the reading conditions will be reversed in a second reading session with independently randomized image order. Under the AI-assisted condition, clinicians will be provided with category-level AI prediction probabilities for retinal findings. No localization maps, heatmaps, segmentation overlays, or automated diagnostic recommendations will be displayed. Clinicians will retain full autonomy over final decisions. Reader performance under AI-assisted and unaided conditions will be compared using expert reference annotations as the ground truth.

Inclusion Criteria: * Participants undergoing ultra-widefield retinal imaging at participating ophthalmic centers; Exclusion Criteria: * Poor-quality or ungradable retinal images;