M⁴4TMD: A multimodal, multi-task deep learning framework for comprehensive assessment of TMD-related abnormalities

Existing deep learning (DL) approaches for assessing temporomandibular disorders (TMD) are limited by underutilization of magnetic resonance imaging (MRI) in some tasks, a narrow focus on single-task detection, and predominant reliance on unimodal data. This study proposes a multimodal DL framework to address these issues.

We collected 12,690 MRI slices and clinical data from 765 participants (1410 temporomandibular joints), with each joint annotated for degenerative joint disease (DJD), anterior disc displacement (ADD), and effusion. We developed M⁴4TMD, utilizing multimodal data including multi-sequence and multi-slice MRI with clinical data, for concurrent assessment of DJD, ADD, and effusion. Performance was benchmarked against three recent DL methods and four clinicians with varying expertise across internal, temporal, and external test sets; assessments included generalization and visual interpretability experiments.

Built upon ResNet50, M⁴4TMD exhibited superior internal test performance (ROC-AUC: 0.831, 0.913, and 0.961), surpassing prior methods. The accuracy of M⁴4TMD for three abnormalities was superior to that of junior dentists and comparable to that of two senior dentists (10 and 20 years experience): DJD (74.9% vs. 74.9%/72.5%; P > 0.05, > 0.05), ADD (78.2% vs. 71.1%/75.8%; P > 0.05, > 0.05), and effusion (90.5% vs. 88.6%/79.6%; P >0.05, < 0.01). Strong robustness and interpretability were validated through generalization and visual interpretability experiments.

The M⁴4TMD framework enables concurrent assessment of TMD-related abnormalities by integrating multimodal MRI and clinical data, exhibiting assessment performance comparable to senior dentists and demonstrating excellent robustness.

The M⁴4TMD framework represents a critical step toward advancing DL-based TMD diagnosis in clinical practice.