To address the issues of insufficient detection accuracy and low model inference efficiency in wrist fracture detection for medical image analysis in emergency scenarios, this study proposes a lightweight wrist fracture detection algorithm based on the improved YOLO11, named MDM-YOLO. First, a multi-scale feature extraction module is designed to address the multi-scale representation challenges of complex fracture shapes by extracting information at different scales through multiple parallel branches. Second, a mixed spatial local attention mechanism is proposed, which combines local and global features to significantly enhance the attention to subtle fractures. Finally, a dynamic depthwise separable convolution (DDSConv) is designed to reduce computational complexity and accelerate inference speed while maintaining detection accuracy, thus making the model more lightweight. Experiments show that MDM-YOLO achieves an precision of 92.6%, a recall rate of 88.1%, and an mAP50 of 95.1% on the GRAZPEDWRI-DX dataset, representing improvements of 1.7%, 2.5%, and 1.5%, respectively, compared with the original model. Under the same hardware conditions, the detection speed increases by 37%, and the number of parameters is only 73.3% of the original model, verifying the effectiveness of the lightweight design. This provides an efficient solution for rapid wrist fracture diagnosis in emergency scenarios.