To evaluate the clinical value of a YOLO 11-based ultrasound detection model for identifying fracture ends in limb long bones.

A total of 206 patients with long bone fractures in Shanghai Eighth People's Hospital from July 2023 to January 2025 were prospectively included. Ultrasonic long-axis sectional images of the fracture ends were collected. The images were labeled using the Labelme software to establish a training dataset (461 images), which was randomly divided into a training set, a validation set, and a test set at a ratio of 7:1:2. The YOLO 11 model was trained. The performance of the model was evaluated by precision, recall, F1 score, and the mean average precision when the intersection over union was greater than 50% (mAP@50), and it was compared with the YOLOv8 model. In addition, 40 subjects (20 with fractures and 20 normal controls) were newly included to construct an independent clinical validation set. Ultrasonic long-axis sectional images of the fracture ends and normal bones were collected. The clinical diagnostic efficacy was comprehensively evaluated by sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV), and the average inference time per frame was analyzed.

According to the data of the test set, the indicators of the YOLO 11 model were superior to those of the YOLOv8 model. Its precision, recall, F1-score, and mAP@50 were 87.9%, 85.3%, 86.6%, and 89.2%, respectively. In the clinical validation set, the sensitivity (92.7% vs 83.9%) and NPV (91.2% vs 82.8%) of YOLO 11 were better than those of YOLOv8, while the specificity (95.4% vs 98.0%) and PPV (96.2% vs 98.2%) slightly decreased, and the average inference time per frame shows a marginal increase over YOLOv8 (42.5 ms vs 36.1 ms).

The YOLO 11 model can efficiently identify the fracture ends of long bones in the limbs, providing an effective auxiliary tool for rapid pre-hospital screening and clinical diagnosis.