Explainable Deep Learning Framework for Automated Classi-fication of Enamel Caries: Bridging Artificial Intelligence and Dental Public Health

This study proposes an explainable and interpretable deep learning framework for automated enamel caries classification, emphasizing diagnostic transparency and reliability for clinical use. The dual-model framework demonstrated high diagnostic precision and transparency, highlighting its potential for integration into clinical and community-level dental screening. Although dataset diversity remains a limitation, future research will focus on multi-modal and federated learning approaches to ensure broader generalization and population-level applicability.