Artificial intelligence in digital cariology: a new tool for the diagnosis of deep caries and pulpitis using convolutional neural networks

BACKGROUND: An accurate diagnosis of deep caries and pulpitis on periapical radiographs is a clinical challenge. METHODS: A total of 844 radiographs were included in this study. Of the 844, 717 (85%) were used for training and 127 (15%) were used for testing the three convolutional neural networks (CNNs) (VGG19, Inception V3, and ResNet18). The performance [accuracy, precision, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC)] of the CNNs were evaluated and compared. The CNN model with the best performance was further integrated with clinical parameters to see whether multi-modal CNN could provide an enhanced performance. The Gradient-weighted Class Activation Mapping (Grad-CAM) technique illustrates what image feature was the most important for the CNNs. RESULTS: The CNN of ResNet18 demonstrated the best performance [accuracy =0.82, 95% confidence interval (CI): 0.80–0.84; precision =0.81, 95% CI: 0.73–0.89; sensitivity =0.85, 95% CI: 0.79–0.91; specificity =0.82, 95% CI: 0.76–0.88; and AUC =0.89, 95% CI: 0.86–0.92], compared with VGG19 and Inception V3 as well as the comparator dentists. Therefore, ResNet18 was chosen to integrate with clinical parameters to produce the multi-modal CNN of ResNet18 + C, which showed a significantly enhanced performance (accuracy =0.86, 95% CI: 0.84–0.88; precision =0.85, 95% CI: 0.76–0.94; sensitivity =0.89, 95% CI: 0.83–0.95; specificity =0.86, 95% CI: 0.79–0.93; and AUC =0.94, 95% CI: 0.91–0.97). CONCLUSIONS: The CNN of ResNet18 showed good performance (accuracy, precision, sensitivity, specificity, and AUC) for the diagnosis of deep caries and pulpitis. The multi-modal CNN of ResNet18 + C (ResNet18 integrated with clinical parameters) demonstrated a significantly enhanced performance, with promising potential for the diagnosis of deep caries and pulpitis.