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

Abstract

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.