Automated detection of zygomatic fractures on spiral computed tomography using a deep learning model

The aim of this study was to evaluate the performance of the YOLOv8 deep learning model for detecting zygomatic fractures. Computed tomography scans with zygomatic fractures were collected, with all slices annotated to identify fracture lines across seven categories: zygomaticomaxillary suture, zygomatic arch, zygomaticofrontal suture, sphenozygomatic suture, orbital floor, zygomatic body, and maxillary sinus wall. The images were divided into training, validation, and test datasets in a 6:2:2 ratio. Performance metrics were calculated for each category. A total of 13,988 axial and 14,107 coronal slices were retrieved. The trained algorithm achieved accuracy of 94.2-97.9%. Recall exceeded 90% across all categories, with sphenozygomatic suture fractures having the highest value (96.6%). Average precision was highest for zygomatic arch fractures (0.827) and lowest for zygomatic body fractures (0.692). The highest F1 score was 96.7% for zygomaticomaxillary suture fractures, and the lowest was 82.1% for zygomatic body fractures. Area under the curve (AUC) values were also highest for zygomaticomaxillary suture (0.943) and lowest for zygomatic body fractures (0.876). The YOLOv8 model demonstrated promising results in the automated detection of zygomatic fractures, achieving the highest performance in identifying fractures of the zygomaticomaxillary suture and zygomatic arch.