Deep learning-based caries lesion classification of primary teeth using bitewing radiographs and its comparison with dental professionals.

purposeTo evaluate the performance of a deep learning-based convolutional neural network (CNN) algorithm and compare it with that of general dentists and dental students in classifying caries lesions in primary teeth using bitewing radiographs.

methodsA total of 1400 bitewing radiographs (4715 tooth images) were divided into training, validation, and testing datasets, with carious lesions classified into four and seven classes. After training, the best-performing ResNet model was selected and compared with three general dentists and three dental students via a reference test. The accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), macro F1 score, area under the curve (AUC), and confusion matrices were evaluated.

resultsResNet-152 outperformed ResNet-50 and ResNet-101 in the validation process. In the 4 class classification, ResNet-152 and Dental student 1 achieved accuracies exceeding 0.7, while most examiners ranged between 0.62 and 0.67. Only Dental student 1 and ResNet-152 achieved specificity and NPV values of 0.9 or higher. ResNet-152 and most examiners exhibited lower sensitivity for initial and moderate lesions than for extensive lesions. In the 7 class classification, the accuracy ranged from 0.37 to 0.58, with the best-performing comparators-Dental student 1, ResNet-152, and General dentist 3 exceeding 0.5. The sensitivity, PPV, and macro F1 score followed similar trends. ResNet-152 achieved a favourable AUC of 0.85.

conclusionResNet-152 performed comparably to its leading human comparators, general dentists and dental students, demonstrating favourable performance in caries lesion classification. CNNs could serve as a second option in caries lesion classification, potentially leading to improved treatment decisions.