Intraoral radiograph anatomical region classification using neural networks.

Abstract

Dental radiography represents 13% of all radiological diagnostic imaging. Eliminating the need for manual classification of digital intraoral radiographs could be especially impactful in terms of time savings and metadata quality. However, automating the task can be challenging due to the limited variation and possible overlap of the depicted anatomy. This study attempted to use neural networks to automate the classification of anatomical regions in intraoral radiographs among 22 unique anatomical classes. Thirty-six literature-based neural network models were systematically developed and trained with full supervision and three different data augmentation strategies. Only libre software and limited computational resources were utilized. The training and validation datasets consisted of 15,254 intraoral periapical and bite-wing radiographs, previously obtained for diagnostic purposes. All models were then comparatively evaluated on a separate dataset as regards their classification performance. Top-1 accuracy, area-under-the-curve and F1-score were used as performance metrics. Pairwise comparisons were performed among all models with Mc Nemar's test. Cochran's Q test indicated a statistically significant difference in classification performance across all models (p < 0.001). Post hoc analysis showed that while most models performed adequately on the task, advanced architectures used in deep learning such as VGG16, MobilenetV2 and InceptionResnetV2 were more robust to image distortions than those in the baseline group (MLPs, 3-block convolutional models). Advanced models exhibited classification accuracy ranging from 81 to 89%, F1-score between 0.71 and 0.86 and AUC of 0.86 to 0.94. According to our findings, automated classification of anatomical classes in digital intraoral radiographs is feasible with an expected top-1 classification accuracy of almost 90%, even for images with significant distortions or overlapping anatomy. Model architecture, data augmentation strategies, the use of pooling and normalization layers as well as model capacity were identified as the factors most contributing to classification performance.