Artificial intelligence-based method for detecting wrist fractures in children.

Pediatric wrist fractures are common skeletal injuries in clinical practice; however, due to the ongoing development of children's bones, fracture characteristics are complex and often prone to misdiagnosis or missed diagnosis. Moreover, traditional diagnostic methods rely heavily on the physician's experience, which may compromise efficiency and accuracy, especially in environments with limited medical resources. To address this issue, this study proposes an improved deep learning detection method based on YOLO11s, named Kid-YOLO, for the automatic detection of pediatric wrist fractures in X-ray images. By introducing the C3k2-WTConv module and Focaler-MPDIoU loss function, the model was improved in terms of multi-scale feature extraction, target box localization accuracy optimization, and addressing the class imbalance problem. The C3k2-WTConv module, which combines wavelet transform and convolution operations, effectively enhances the model's ability to detect subtle fractures and complex patterns. The Focaler-MPDIoU loss function improves performance in detecting rare targets by dynamically adjusting sample weight distribution and optimizing prediction box positioning. Experiments were conducted on the publicly available GRAZPEDWRI-DX dataset after data cleaning, The results show that, compared with the YOLO11 model, the improved model achieves a 3.2% increase in precision, a 1.6% increase in recall, a 1.8% improvement in mAP@50, and a 3.2% improvement in mAP@50-95. Furthermore, this study developed an AI-assisted diagnostic system with an integrated graphical user interface, capable of efficiently performing image loading, fracture detection, and result visualization, thereby providing physicians with a reliable diagnostic tool. In the future, this method is expected to be applied to a broader range of medical imaging analysis tasks, offering new technical support for precision medicine.