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 multimodal 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 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.

Automated summary

Through studying 844 radiographs, it was found that the CNN of ResNet18 demonstrated the best performance for diagnosing deep caries and pulpitis. Further integration with clinical parameters resulted in significantly enhanced diagnostic performance.