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CBCT-MRI-based prediction models for stratifying anterior disc displacement in orthodontic patients: development and
Ji-Teng Liu1,2,3, Wei-Wen Fang1,2,3, Xin-Yu Cai1,2,3
1Department of Orthodontics, The Affiliated Stomatological Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.
Objectives:
Undiagnosed anterior disc displacement (ADD) and anterior disc displacement without reduction (ADDwoR) during orthodontic treatment can compromise treatment outcomes and long-term stability. This study aimed to establish quantitative decision-support models for stratifying ADD and its subtypes based on the temporomandibular joint (TMJ) radiological morphology in order to address the diagnostic challenges in orthodontic patients with dentofacial deformities.
Methods:
In this retrospective diagnostic study, 72 patients (144 TMJs) awaiting orthodontic treatment were allocated to a modeling group (n = 61) and an independent internal validation group (n = 11), with TMJ imaging indicators (joint space, disc thickness, condylar dimensions, and condylar volume) quantified using CBCT and MRI. TMJs were stratified into normal, anterior disc displacement with reduction (ADDwR), or ADDwoR groups according to MRI disc-condylar angle. Diagnostic models were developed using Spearman's correlation analysis, logistic regression, and were visualized as nomograms, with internal validation via the Bootstrap method and independent internal validation using the validation group. Model reliability was evaluated using the intraclass correlation coefficient (ICC), goodness-of-fit tests, and McNemar tests, while discriminative ability was assessed via receiver operating characteristic (ROC) curve analysis.
Results:
Two logistic regression models were developed. The ADD diagnosis model (AUC = 0.925) included anterior joint space, posterior band thickness, and condylar diameters (APCD and MLCD); the ADDwoR subclassification model (AUC = 0.898) incorporated anterior band thickness, middle band thickness, and condylar volume. Optimized thresholds (0.629, 0.748) had sensitivities (75.8%, 90.6%), specificities (87.1%, 78.2%), and good consistent calibration curves (P > 0.05), with no validation group-reference differences (P = 0.063, 0.125).
Conclusions:
The developed logistic regression models could be explored as a potential imaging-based tool for ADD subtyping, offering supplementary information in orthodontic clinical decision-making for ambiguous TMD cases and potentially aiding treatment planning in orthodontic and craniofacial practice.