Deep Learning-Based Detection of Scaphoid Fractures on Anteroposterior Wrist Radiographs

Because injuries are often vague and easily unnoticed, missed diagnosis of scaphoid fractures on emergency radiographs reveals a critical limitation of acute care imaging. In addition, owing to unremarkable radiographic features, scaphoid fractures are particularly challenging. Therefore, a deep learning-based scaphoid fracture detection (DLSFD) framework is developed in this study for predicting scaphoid fractures on anteroposterior wrist radiographs. A ten-year retrospective cohort of wrist radiographs including both fractures and non-fractures were collected and analyzed in the study. Furthermore, data augmentation and labeling were used to improve model performance. The proposed deep learning-based scaphoid fracture detection framework first applies the YOLOv8 algorithm to localize and segment the scaphoid region in anteroposterior wrist radiographs. Then, a U-Net-based classifier is employed to predict the fracture or non-fracture with 5-fold cross-validation to prevent overfitting. Instead of using heat maps to represent the regions of scaphoid fractures, this study carries out pixel-level segmentation and generates pixel-wise masks to clearly locate scaphoid fracture area. Numerical results indicate that the proposed DLSFD framework is a feasible and promising alternative in predicting scaphoid fractures in terms of classification performance. Moreover, overlay segmentation masks generated by the developed DLSFD framework provide visual assistance for clinical interpretation. Thus, the designed DLSFD framework is able to successfully identify scaphoid fractures and may be useful in clinical practice for assisting clinical assessment.