A novel segmentation-based deep learning model for enhanced scaphoid fracture detection

To develop a deep learning model to detect apparent and occult scaphoid fractures from plain wrist radiographs and to compare the model's diagnostic performance with that of a group of experts.

A dataset comprising 408 patients, 410 wrists, and 1011 radiographs was collected. 718 of these radiographs contained a scaphoid fracture, verified by magnetic resonance imaging or computed tomography scans. 58 of these fractures were occult. The images were divided into training, test, and occult fracture test sets. The images were annotated by marking the scaphoid bone and the possible fracture area. The performance of the developed DL model was compared with the ground truth and the assessments of three clinical experts.

The DL model achieved a sensitivity of 0.86 (95 % CI: 0.75-0.93) and a specificity of 0.83 (0.64-0.94). The model's accuracy was 0.85 (0.76-0.92), and the area under the receiver operating characteristics curve was 0.92 (0.86-0.97). The clinical experts' sensitivity ranged from 0.77 to 0.89, and specificity from 0.83 to 0.97. The DL model detected 24 of 58 (41 %) occult fractures, compared to 10.3 %, 13.7 %, and 6.8 % by the clinical experts.

Detecting scaphoid fractures using a segmentation-based DL model is feasible and comparable to previously developed DL models. The model performed similarly to a group of experts in identifying apparent scaphoid fractures and demonstrated higher diagnostic accuracy in detecting occult fractures. The improvement in occult fracture detection could enhance patient care.