EnamelNet-TRiX: A Lesion-Aware Dual-Transformer With Cross-Attention for Early and Advanced Enamel Caries Diagnosis.

purposeDental caries is one of the most prevalent oral diseases worldwide, and predicting Early Enamel Caries (EEC) and Advanced Enamel Caries (AEC) in intraoral imaging is a significant clinical research challenge. To overcome the challenges, this research presents an EnamelNet-TRiX, an automated diagnosis system for enamel caries based on a lesion-aware dual-transformer framework with across-attention guidance.

methodsThe proposed framework is developed using a shallow convolutional lesion-aware module (LAM) that incorporates on the lesions of the enamel combined with a Swin Transformer that zooms in on textures of the lesions, and a Vision Transformer (ViT) that captures the overall global structure to provide contextual global structural guidance followed by a multi-scale feature fusion stage that augments cross-stream attention with concatenation and unification for classification. The model is trained on the Caries-Spectra dataset, a set of internal images comprising 2000 intraoral images with three diagnostic classes (EEC, AEC, and No Enamel Caries [NEC]), which are cross-validated using an 80-20 split on a patient basis for training and testing.

resultsThe model is externally evaluated on the DentRT-2 dataset that consists of 300 real-world intraoral images with diverse diagnostic conditions. The model achieved accuracy: 99.25%, precision: 98.94%, recall: 99.12%, and F1-score: 99.03%, respectively, for the Caries-Spectra dataset, while confirming 96.33% accuracy on DentRT-2. The simulation results show the solid domain generalisation of caries diagnosis.

conclusionThe proposed lesion-aware dual-transformers with cross-attention, a multiscale fusion mechanism incorporating local, global, and lesion-prior features, and exhaustive internal and external clinical testing on a real-time dataset shed light on EnamelNet-TRiX as a trustworthy framework for the diagnosis of enamel caries.