Automated Identification of Accessory Mental Foramen Using Cone-Beam Computed Tomography and Convolutional Neural Networks.

INTRODUCTION AND AIMS: To develop and evaluate a deep learning-based system for automatic detection of the accessory mental foramen (AMF) using cone-beam computed tomography (CBCT) images, and to compare the detection accuracy and clinical reliability performance of two convolutional neural network (CNN) architectures for this model. METHODS: A total of 3000 CBCT scans were retrospectively screened. After expert evaluation, 700 CBCT scans exhibiting AMFs were identified. For comparative analysis, 700 CBCT scans with normal mental-foramen anatomy were selected as the matched control group. A custom lightweight CNN and a ResNet-50 model were trained for binary classification of AMF presence. Model performance was evaluated by determining accuracy, precision, recall, and the F1-score. Gradient-weighted class activation mapping (Grad-CAM) visualisation was employed to assess the anatomical relevance of the models' attention maps. Statistical analyses were performed to compare the diagnostic performance of the two networks. RESULTS: The ResNet-50 model achieved superior performance (overall accuracy: 85.8% for ResNet-50 vs 71.1% for the custom CNN). With the ResNet-50 model, anomaly recall improved from 0.68 to 0.88, reducing missed detections by 63%. Grad-CAM analysis demonstrated that the models focused primarily on anatomically valid regions around the MF, confirming the interpretability and clinical relevance of the models. CONCLUSIONS: Automatic detection of the AMF using CBCT and deep learning represents a reliable, objective, and efficient diagnostic approach that minimises observer bias and enhances clinical decision-making. CLINICAL RELEVANCE: Deep learning-based detection of AMFs on CBCT can enhance diagnostic accuracy and reduce the risk of surgical complications by providing consistent, observer-independent evaluations.