Introduction The aims of this study were to evaluate how head orientation interferes with the amounts of directional change in 3-dimensional (3D) space and to propose a method to obtain a common coordinate system using 3D surface models. Methods Three-dimensional volumetric label maps were built for pretreatment (T1) and posttreatment (T2) from cone-beam computed tomography images of 30 growing subjects. Seven landmarks were labeled in all T1 and T2 volumetric label maps. Registrations of T1 and T2 images relative to the cranial base were performed, and 3D surface models were generated. All T1 surface models were moved by orienting the Frankfort horizontal, midsagittal, and transporionic planes to match the axial, sagittal, and coronal planes, respectively, at a common coordinate system in the Slicer software (open-source, version 4.3.1; http://www.slicer.org). The matrix generated for each T1 model was applied to each corresponding registered T2 surface model, obtaining a common head orientation. The 3D differences between the T1 and registered T2 models, and the amounts of directional change in each plane of the 3D space, were quantified for before and after head orientation. Two assessments were performed: (1) at 1 time point (mandibular width and length), and (2) for longitudinal changes (maxillary and mandibular differences). The differences between measurements before and after head orientation were quantified. Statistical analysis was performed by evaluating the means and standard deviations with paired t tests (mandibular width and length) and Wilcoxon tests (longitudinal changes). For 16 subjects, 2 observers working independently performed the head orientations twice with a 1-week interval between them. Intraclass correlation coefficients and the Bland-Altman method tested intraobserver and interobserver agreements of the x, y, and z coordinates for 7 landmarks. Results The 3D differences were not affected by the head orientation. The amounts of directional change in each plane of 3D space at 1 time point were strongly influenced by head orientation. The longitudinal changes in each plane of 3D space showed differences smaller than 0.5 mm. Excellent intraobserver and interobserver repeatability and reproducibility (>99%) were observed. Conclusions The amount of directional change in each plane of 3D space is strongly influenced by head orientation. The proposed method of head orientation to obtain a common 3D coordinate system is reproducible.

After chronic low back pain, Temporomandibular Joint (TMJ) disorders are the second most common musculoskeletal condition affecting 5 to 12% of the population, with an annual health cost estimated at $4 billion. Chronic disability in TMJ osteoarthritis (OA) increases with aging, and the main goal is to diagnosis before morphological degeneration occurs. Here, we address this challenge using advanced data science to capture, process and analyze 52 clinical, biological and high-resolution CBCT (radiomics) markers from TMJ OA patients and controls. We tested the diagnostic performance of four machine learning models: Logistic Regression, Random Forest, LightGBM, XGBoost. Headaches, Range of mouth opening without pain, Energy, Haralick Correlation, Entropy and interactions of TGF-β1 in Saliva and Headaches, VE-cadherin in Serum and Angiogenin in Saliva, VE-cadherin in Saliva and Headaches, PA1 in Saliva and Headaches, PA1 in Saliva and Range of mouth opening without pain; Gender and Muscle Soreness; Short Run Low Grey Level Emphasis and Headaches, Inverse Difference Moment and Trabecular Separation accurately diagnose early stages of this clinical condition. Our results show the XGBoost + LightGBM model with these features and interactions achieves the accuracy of 0.823, AUC 0.870, and F1-score 0.823 to diagnose the TMJ OA status. Thus, we expect to boost future studies into osteoarthritis patient-specific therapeutic interventions, and thereby improve the health of articular joints.

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Objectives: To investigate the reliability of regional three-dimensional registration and superimposition methods for assessment of temporomandibular joint condylar morphology across subjects and longitudinally. Methods: The sample consisted of cone beam CT scans of 36 patients. The across-subject comparisons included 12 controls, mean age 41.3 ± 12.0 years, and 12 patients with temporomandibular joint osteoarthritis, mean age 41.3 ± 14.7 years. The individual longitudinal assessments included 12 patients with temporomandibular joint osteoarthritis, mean age 37.8 ± 16.7 years, followed up at pre-operative jaw surgery, immediately after and one-year post-operative. Surface models of all condyles were constructed from the cone beam CT scans. Two previously calibrated observers independently performed all registration methods. A landmark-based approach was used for the registration of across-subject condylar models, and temporomandibular joint osteoarthritis vs control group differences were computed with shape analysis. A voxel-based approach was used for registration of longitudinal scans calculated x, y, z degrees of freedom for translation and rotation. Two-way random intraclass correlation coefficients tested the interobserver reliability. Results: Statistically significant differences between the control group and the osteoarthritis group were consistently located on the lateral and medial poles for both observers. The interobserver differences were #0.2 mm. For individual longitudinal comparisons, the mean interobserver differences were #0.6 mm in translation errors and 1.2°in rotation errors, with excellent reliability (intraclass correlation coefficient .0.75). Conclusions: Condylar registration for across-subjects and longitudinal assessments is reliable and can be used to quantify subtle bony differences in the three-dimensional condylar morphology.

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