Python-based Quantitative Morphometric Imaging Pipeline for Volumetric Temporomandibular Joint Space Analysis on Cone-beam Computed Tomography: A Retrospective Case-control Study to Detect Degenerative Joint Disease
Kunal Agarwal, Fakir M. Debta, Ajo B. George, Dirangzeule HauAbstract
Background:
The temporomandibular joint (TMJ) is frequently affected by degenerative joint disease (DJD), particularly osteoarthritis. Cone-beam computed tomography (CBCT) is widely used to assess osseous change, conventionally through linear joint space measurement; however, linear measurement is confined to two-dimensional sagittal sections and cannot capture the three-dimensional configuration of the joint cavity.
Objective:
To evaluate volumetric joint space measurement on CBCT as a diagnostic indicator of TMJ DJD and to compare it with the conventional linear anterior, superior, and posterior joint space parameters.
Methods:
Sixty anonymized CBCT scans (30 arthritic, 30 non-arthritic) were analyzed, retrospectively. Anterior (AJS), superior (SJS), and posterior (PJS) joint spaces were measured linearly, and joint space volumes were semi-automatically segmented in ITK-SNAP software with manual correction. DJD was diagnosed according to the Diagnostic Criteria for Temporomandibular Disorders (DC/TMD) imaging criteria. Statistical analysis comprised independent-samples t-tests for between-group comparison, Pearson correlation, principal component analysis (PCA), logistic regression, and Random Forest classification.
Results:
Arthritic joints showed significantly reduced AJS (2.09 ± 0.59 vs 2.64 ± 0.69 mm;
Conclusion:
Volumetric analysis of the TMJ joint space on CBCT shows potential as a diagnostic indicator of DJD and may offer advantages over linear parameters; prospective studies with observer-reliability assessment are required to confirm its clinical reproducibility.