One notable anomaly, presence of distomolars, arises beyond the typical sequence of the human dental system. In this study, convolutional neural networks (CNNs) based machine learning methods were employed to classify distomolar tooth existence using panoramic radiography (PR).

PRs dataset, composed of 117 subjects with distomolar teeth and 146 subjects without distomolar teeth, was constructed. These images were assessed using AlexNet, DarkNet, DenseNet, EfficientNet, GoogLeNet, ResNet, MobileNet, NasNet-Mobile, VGG, and XceptionNet frameworks for distomolar teeth existence. Considering the moderate number dataset samples, transfer learning was also utilized to improve the performance of these CNN based networks along with 5-fold cross-validation. The final classification was obtained through the fusion of the classifiers results.

Performance of the experimental studies was assessed utilizing accuracy (Acc), sensitivity (sen), specificity (spe) and precision (pre) metrics. Best accuracy value of 96.2 % was obtained for the fusion of DarkNet, DenseNet, and ResNet, three best individual performing architectures, in distomolar teeth classification problem.

In summary, this study has demonstrated the significant potential of CNNs in accurately detecting distomolar teeth in dental radiographs, a critical task for clinical diagnosis and treatment planning. The fusion of CNN architectures, particularly ResNet, Darknet, and DenseNet, has shown exceptional performance, pointing towards the future of artificial intelligence (AI) driven dental diagnostics. Our findings showed that these systems can help clinicians during radiologic examinations.