Precise Segmentation Of Mandibular Canal Bifurcation Images Using Deep Learning

This study developed a deep learning model to rapidly detect mandibular canal bifurcation and its anatomical features, aiding dentists in minimizing surgical risks and optimizing treatment planning. We collected a total of 160 reported case images from the PubMed and Web of Science databases. Of these, 140 images were allocated to a training set and 20 to a testing set, to develop the BMC-Net deep learning model. Performance was evaluated using the DSC, AUC, IoU, recall, precision, and a confusion matrix. To determine clinical utility, a comparative analysis with clinicians was performed, focusing on AUC, sensitivity, specificity, and time efficiency. The BMC-Net model achieved a DSC of 0.9704, AUC of 0.9458, IoU of 0.9412, recall of 0.9458, and precision of 0.9829 in the training set, with significant improvements in the testing set, where it reached a DSC of 0.9877, AUC of 0.9965, IoU of 0.9539, recall of 0.9569, and precision of 0.9976. Compared to clinicians, the BMC-Net model achieved an AUC of 0.9636, sensitivity of 0.9314, and specificity of 0.9461, and detected mandibular canal divergence in just 0.1004 seconds, significantly faster than the average clinician's recognition time of 95.5 to 164.75 seconds (P < 0.05). This groundbreaking model's utility markedly improves the accuracy of clinical diagnoses on mandibular nerve canal bifurcations, shortens patient visit times, and provides detailed navigational guidance for various clinical diagnostic and treatment applications.