Automated detection and classification of mandibular fractures on multislice spiral computed tomography using modified convolutional neural networks.

Objective: To evaluate the performance of convolutional neural networks (CNNs) for the automated detection and classification of mandibular fractures on multislice spiral computed tomography (MSCT).

Study Design: MSCT data from 361 patients with mandibular fractures were retrospectively collected. Two experienced maxillofacial surgeons annotated the images as ground truth. Fractures were detected utilizing the following models: YOLOv3, YOLOv4, Faster R-CNN, CenterNet, and YOLOv5-TRS. Fracture sites were classified by the following models: AlexNet, GoogLeNet, ResNet50, original DenseNet-121, and modified DenseNet-121. The performance was evaluated for accuracy, sensitivity, specificity, and area under the curve (AUC). AUC values were compared using the Z-test and P values <.05 were considered to be statistically significant.

Results: Of all of the detection models, YOLOv5-TRS obtained the greatest mean accuracy (96.68%). Among all of the fracture subregions, body fractures were the most reliably detected (with accuracies of 88.59%-99.01%). For classification models, the AUCs for body fractures were higher than those of condyle and angle fractures, and they were all above 0.75, with the highest AUC at 0.903. Modified DenseNet-121 had the best overall classification performance with a mean AUC of 0.814.

Conclusions: The modified CNN-based models demonstrated high reliability for the diagnosis of mandibular fractures on MSCT.