Faster R-CNN model for target recognition and diagnosis of scapular fractures.

Objective: This study aims to establish a diagnostic model for scapular fractures using a convolutional neural network (CNN) and to discuss the clinical advantages of this model in diagnosing such complex conditions. Methods: Computed tomography (CT) images of 90 patients with scapular fractures were collected. A faster R-CNN-based recognition model was developed and compared with manual diagnosis. External validation was conducted to evaluate the model's accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. Results: The CNN model, when combined with medical expert interpretation, demonstrated significantly higher specificity and positive predictive value compared to orthopedist-independent interpretation and algorithm-independent prediction (P < 0.05). The area under the curve (AUC) value of the combined approach was significantly higher than that of orthopedist-independent interpretation and algorithm-independent prediction groups, with statistically significant differences (P < 0.05). The accuracy of the CNN algorithm model combined with orthopedist interpretation was 97.78 %, significantly higher than orthopedist-independent interpretation (82.95 %) and CNN algorithm-independent prediction (92.05 %) (P < 0.05). Conclusions: The CNN-based recognition model for scapular fractures can assist clinicians in improving their diagnostic accuracy and precision in identifying such fractures on CT images.