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This paper introduces a Transformer-based model for early dental caries detection in panoramic radiographs, addressing the challenges of subtle radiographic features and complex anatomical structures. The model incorporates enhanced multi-scale feature fusion, spatially-aware attention, and improved 2D positional encoding to capture global context and fine-grained features. Evaluated on a dataset of 3,856 radiographs with 12,847 annotated lesions, the model achieved 87.3% mAP and real-time processing, outperforming CNNs and dentists in early-stage caries detection.
A Transformer model spots early dental decay better than dentists, promising faster and more accurate diagnoses from panoramic X-rays.
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.
