Year: 2026
Type: data-paper
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.
Source: Scientific Data
Language: English
Topic: Retinal Imaging and Analysis
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,490
OpenAlex ID: W7147420137