A fundus image dataset for intelligent diabetic retinopathy system

Abstract Diabetic retinopathy (DR), the most prevalent microvascular complication of diabetes mellitus, is the leading cause of irreversible vision loss in the global working-age population. At present, deep learning-integrated ultra-wide-field (UWF) image analysis systems have improved DR grading consistency and reduced peripheral lesion misdiagnosis rates, thereby overcoming the limitations of traditional 45° viewing field AI models in detecting peripheral retinal lesions. However, the lack of standardized, high-quality, and publicly available UWF-DR datasets has severely restricted the generalization ability and reliability of AI models in clinical practice. To address this, this study constructed a dataset comprising 1,630 UWF fundus images from 809 patients, which were annotated and classified by three senior ophthalmologists, for development and validation of AI system in UWF-based DR diagnosis. This dataset aims to empower researchers to train more efficient and accurate AI-assisted DR diagnosis systems based on UWF images, advancing its widespread real-world clinical applications.