Pediatric wrist fractures are the most common pediatric traumatic injuries, while manual X‐ray diagnosis has high missed diagnosis rates and strong subjectivity in emergency settings. Existing deep learning models face core clinical bottlenecks, including low recall for subtle fractures, poor robustness to low‐quality images, and unbalanced accuracy and inference efficiency. We propose WFYOLO, an enhanced YOLO11‐based algorithm for pediatric wrist fracture detection, with a multi‐scale edge enhancement (MSE) module, lightweight slim‐neck, dynamic head (DYHead), and class‐weighted loss to address the above limitations. All experiments are conducted on the official standard split of the GRAZPEDWRI‐DX benchmark. Results show that WFYOLO achieves 68.99% mAP@50 and 43.6% mAP@50–95, outperforming the YOLO11s baseline by 3.33% and 2.33%, and surpassing existing state‐of‐the‐art models while maintaining 96.1 FPS real‐time inference speed. WFYOLO has excellent clinical robustness and deployment potential for pediatric fracture auxiliary diagnosis.
Yang et al. (Thu,) studied this question.