Pediatric forearm fractures, particularly involving the ulna and radius, are among the most common childhood injuries. However, the lack of standardized and openly available datasets has limited progress in artificial intelligence research and constrained clinical validation. To address this issue, we present the Pediatric Ulna and Radius Fractures (PediURF) dataset, a first-of-its-kind, publicly available collection of over 10,000 de-identified images. Each image is carefully annotated by expert radiologists and categorized into three clinically relevant types: proximal, midshaft, and distal fractures. By releasing PediURF, we aim to provide an accessible resource for deep learning-based models development, benchmarking, and clinical training. To validate its utility, we proposed URFNet, a dual-view classification model designed to integrate anteroposterior and lateral perspectives. The proposed model achieved the best performance when compared with other classification models. Collectively, the proposed PediURF dataset provides a valuable foundation for future deep learning-based studies in pediatric fracture classification.
Tang et al. (Wed,) studied this question.