ArticleOral radiology2026
Two-stage deep learning framework for magnetic resonance imaging-based detection of temporomandibular joint osteoarthritis.
Article in Oral radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
objectivesCone-beam computed tomography (CBCT) is the reference standard for detecting osseous changes in temporomandibular joint osteoarthritis (TMJ-OA) but involves ionizing radiation. MRI avoids radiation exposure and enables simultaneous soft and hard tissue evaluation; however, its sensitivity for osseous abnormalities remains limited. This study aimed to develop and evaluate a proof-of-concept two-stage deep learning framework for automated MRI-based TMJ-OA detection.
methodsA retrospective dataset of 200 bilateral TMJ MRI examinations (400 condyles; 113 osteoarthritic, 287 non-osteoarthritic) was collected from two academic dental institutions. Stage 1 employed a U-Net architecture for automated mandibular condyle segmentation using manually annotated masks. Stage 2 applied ResNet-50 and ResNet-101 to classify segmented condylar regions as absent, normal, or osteoarthritic. Labels were established by two experienced oral and maxillofacial radiologists using CBCT-confirmed osseous findings and DC/TMD clinical criteria. Patient-level partitioning (70/15/15%) and class-weighted loss functions addressed data leakage and class imbalance. An experienced radiologist and a newly graduated clinician provided human benchmark comparisons on the held-out test subset.
resultsU-Net segmentation achieved a Dice similarity coefficient of 0.944, Intersection over Union of 0.894, and AUC of 0.97, with balanced precision and recall of 0.948. ResNet-50 yielded an F1 score of 0.813 (precision 0.867, recall 0.765); ResNet-101 yielded 0.800 (precision 0.778, recall 0.824). The experienced radiologist achieved an F1 score of 0.970; the newly graduated clinician achieved 0.414.
conclusionsThis study demonstrates the feasibility of a segmentation-first, classification-second deep learning pipeline for automated MRI-based TMJ-OA detection, providing a methodological foundation for future multi-institutional validation of a radiation-free diagnostic approach to TMJ-OA.
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