CBCT assisted diagnosis system for temporomandibular joint disc displacement based on deep learning

The diagnosis of temporomandibular joint (TMJ) disc displacement relies on clinical symptoms and magnetic resonance imaging (MRI), which is complex, costly and time-consuming. Although cone-beam computed tomography (CBCT) reveals indirect signs suggestive of TMJ disc displacement, manual interpretation remains expertise-dependent, thereby limiting its use in clinical practice. This study aims to predict the presence of TMJ disc displacement risk in CBCT images using deep learning techniques.

By leveraging the CBCT images of 330 patients, a two-stage TMJ disc displacement screening model was developed. In the first stage, an object-detection model was trained on YOLOv11, using 30 manually annotated CBCT images as reference. A total of 5,238 TMJ Regions of Interest (ROIs) were identified, among which 2,260 showing signs of TMJ disc displacement. Subsequently, these detected images were used to train a FastViT-t8-based binary-classification model, with diagnostic results of two experienced oral and maxillofacial radiologists based on MRI set as the ground truth.

The object-detection model achieved a Precision of 0.986, a Recall of 0.982, an mAP50 of 0.988, and an mAP50-95 of 0.534. The binary-classification model achieved an AUC of 0.733 (95% CI: [0.713-0.756]), an AUPR of 0.716 (95% CI: [0.685-0.745]), and an accuracy of 0.669.

The proposed model demonstrates preliminary screening capability for TMJ disc displacement using CBCT images. While its current performance precludes standalone diagnostic use, the model may serve as a practical triage tool in orthodontic settings, assisting in the early identification of patients who should be referred for confirmatory MRI and offering references for related research.