Transformer-based intelligent detection model for early dental caries in panoramic radiographs

Abstract

Abstract Early detection of dental caries in panoramic radiographs remains challenging due to subtle radiographic features and complex anatomical structures. This study develops a Transformer-based intelligent detection model specifically optimized for identifying early-stage carious lesions in panoramic dental images. The proposed architecture integrates enhanced multi-scale feature fusion mechanisms, spatially-aware attention optimization, and improved two-dimensional positional encoding to capture global contextual relationships while maintaining fine-grained feature discrimination. A comprehensive dataset comprising 3,856 panoramic radiographs with 12,847 annotated carious lesions across severity grades (D1-D4) was constructed for model development and validation. The model achieved 87.3% mean average precision (mAP) across all caries stages, with notable sensitivity of 81.3% for D1 lesions and 84.7% for D2 lesions, surpassing conventional CNN-based approaches and average dentist performance. The system processes images in real-time (70 milliseconds per radiograph). This research demonstrates the efficacy of domain-adapted Transformer architectures for early dental caries detection and establishes its potential utility as a decision support tool for enhancing diagnostic accuracy and screening efficiency in dental practice.