Deep Learning for Wrist X-ray Analysis: Automatic Classification and Radius Segmentation Using ResNet50 and U-Net
Introduction Fractures of the distal radius are among the most common orthopedic injuries, particularly in two high-risk populations: children under 16 years and postmenopausal women between 60 and 70 years of age. According to national health data, over 175,000 cases of distal radius fractures were treated in Korea in 2018 alone , reflecting the growing burden of wrist injuries in aging societies. While Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) can provide detailed information on fracture patterns and associated soft tissue injuries, plain radiographs (anteroposterior and lateral views) remain the first-line diagnostic tool due to their accessibility, cost-effectiveness, and speed. However, interpretation accuracy varies widely depending on the physician’s experience. Studies have shown that junior emergency physicians achieve only around 72% diagnostic accuracy when interpreting wrist radiographs. To address this gap, recent advances in Artificial In...
