From Clinic to Community: An Interpretable Artificial Intelligence Framework for Enamel Caries Detection to Support Public Health Dentistry.

Objectives: Dental enamel caries is among the most prevalent oral diseases worldwide. Early detection is essential, as incipient lesions can be managed with noninvasive therapies. Conventional methods, such as visual-tactile inspection and radiography, remain limited by examiner variability and reduced sensitivity for early lesions. This study aimed to develop an efficient and interpretable deep learning framework for automated classification of enamel caries at multiple severity levels, while ensuring clinical applicability and transparency. Materials and Methods: A dataset of 2,000 clinical dental images categorized as advanced enamel caries, early-stage enamel caries, and no enamel caries was curated and expanded to 12,000 images using preprocessing and augmentation. Two transfer learning models, Modified EfficientNetB0 and Modified MobileNetV2, were trained individually, then combined using an attention-guided fusion mechanism. Gradient-weighted Class Activation Mapping (Grad-CAM) was applied to provide visual interpretability. Statistical Analysis: Performance was evaluated using accuracy, precision, sensitivity, specificity, F1 score, and ROC AUC. Comparative analysis was performed across models and classifiers, with inference time assessed for clinical feasibility. Results: The Modified EfficientNetB0 and MobileNetV2 models achieved accuracies of 96.33 and 96.25%, respectively. The fused model with Random Forest demonstrated superior performance, achieving 96.92% accuracy, F1 score of 96.92, and an ROC AUC of 99.34. Misclassifications were limited to adjacent disease stages, with no severe diagnostic errors. Conclusion: The proposed framework provides accurate, interpretable, and efficient enamel caries detection. Its low inference time supports real-time clinical use, enhancing diagnostic confidence and enabling early, minimally invasive interventions. Future research should focus on multicenter validation and multimodal datasets to improve generalizability.