Multimodal deep learning with anatomically constrained attention for screening MRI-detectable TMJ abnormalities from panoramic images

Abstract

Early diagnosis of temporomandibular disorders is challenging. Particularly, intra-articular temporomandibular joint (TMJ) abnormalities can only be confirmed using magnetic resonance imaging (MRI). This study aimed to develop a comprehensive screening method for MRI-detectable TMJ pathologies. We developed an interpretable deep learning framework that leveraged paired open- and closed-mouth TMJ panoramic radiographs and structured clinical metadata. The architecture integrated anatomically guided attention, multimodal clinical features, and ensemble learning for enhanced diagnostic accuracy and interpretability. Across 1355 patients (2710 joints), the best-performing ensemble framework achieved an area under the curve of 0.86, with a balanced classification of MRI-negative and -positive cases. Gradient-weighted Class Activation Mapping visualizations confirmed a consistent focus on the condylar regions, and ablation studies demonstrated the added value of clinical metadata and spatial attention. In conclusion, our prototype workflow can be useful to triage TMJ patients for MRI referral, thus supporting early detection of TMJ abnormalities and timely interventions.