MRI and DC/TMD Methods Analyze the Diagnostic Accuracy of the Change in Articular Disc of Temporomandibular Joint

Do Temporomandibular Disorder Patients with Joint Pain Exhibit Forward Head Posture? A Cephalometric Study

Purpose

To evaluate head and cervical posture in individuals with or without temporomandibular disorders (TMDs) and to assess the correlations between pain, severity of symptoms, and posture.

Methods

A total of 384 patients (129 males and 255 females) was included. The Fonseca Anamnestic Index (FAI) was used to assess the severity and prevalence of TMD and the presence of temporomandibular joint (TMJ) pain. Patients were divided into three groups: the TMD-free group, TMD without TMJ pain group, and TMD with TMJ pain group. Subsequently, the patients with TMJ pain were further divided into mild TMD and moderate/severe TMD groups. Nine parameters were traced on cephalograms to characterize the head and cervical posture.

Results

TMD patients with TMJ pain showed increased forward head posture (FHP) than patients without TMJ pain and TMD-free subjects. No significant difference was observed between the TMD patients without TMJ pain and TMD-free subjects. In the TMD patients with the TMJ pain group, the moderate/severe TMD patients demonstrated increased FHP compared to mild TMD patients. TMD patients with joint pain had greater CVT/RL (B = 3.099), OPT/RL (B = 2.117), and NSL/C2' (B = 4.646) than the patients without joint pain after adjusting for confounding variables (P < 0.05).

Conclusion

TMD patients with TMJ pain showed increased FHP compared to other groups, and FHP became more significant as TMD severity increased in male patients, indicating the FHP might play an important role in the development of TMJ pain. In the clinical assessment of TMD, the patients' abnormal head and cervical posture might be considered.

Comparison of magnetic resonance imaging findings in 880 temporomandibular disorder patients of different age groups: a retrospective study

BACKGROUND

Magnetic resonance imaging (MRI) findings of temporomandibular joint (TMJ) in temporomandibular disorder (TMD) patients of different ages are still unclear. The aim of this study was to analyze and compare the characteristics of MRI features of TMJs in different age groups.

METHODS

A total of 1760 TMJs from 880 patients were included in the study and divided into three groups: ≤ 18Y (n = 195, 14.89 ± 2.35Y); 19-30Y (n = 475, 24.09 ± 3.23Y); and > 30Y (n = 210, 41.73 ± 10.45Y). T2-weighted image (T2WI) of MRI was obtained to evaluate the relationship between age and disc morphology, the degree of disc displacement, joint effusion, joint movement and changes of condylar bone morphology. Data were analyzed by Pearson Chi square test and Spearman correlation coefficient.

RESULTS

There was no statistical difference between left and right sides in all age groups. Except condylar morphology (χ2 = 0.943, P = 0.624), there were significant differences in the distribution of disc morphology, disc position, joint effusion and joint motion among different age groups (χ2 = 24.450, χ2 = 24.829, χ2 = 19.855, χ2 = 39.259, respectively). There were significant differences in the distribution of the degree of anterior disc displacement, condyle morphology and joint effusion in different types of disc morphology among the different age groups (except for joint effusion in > 30Y), among which the first two were significantly correlated with the disc morphology.

CONCLUSIONS

The morphology and position of the articular disc changed significantly with age, but the proportion of abnormal condylar bone remained about 50%. The greater the degree of disc folding, the more prone to bone abnormalities. Trial registration This study was retrospectively registered on 28/03/2022 and endorsed by the Ethics committee (LCYJ2022014).

Myogenous Temporomandibular Disorders: Diagnostic Concepts and Prospective Pilot Study on Extracorporeal Shockwave Therapy

The aims of this article are to discuss the current, and potential future directions, in the diagnosis of myogenous temporomandibular disorders (M-TMD), as well as to report a pilot study to investigate the feasibility and clinical outcomes of extracorporeal shockwave therapy (ESWT) in the treatment of M-TMD. Forty-one adult patients presented with M-TMD were recruited into the study and randomized into two groups: Group 1 received ESWT treatment, whereas Group 2 received placebo treatment. The variables investigated were pain, measured by a numerical rating scale (NRS) and mouth opening. Twenty-six patients (Group 1: n = 14, mean age = 45.3 (16.7) years; Group 2: n = 12, mean age = 46.8 (19.7) years) completed 1-year follow up and were included into the final analysis. In both groups, reduction in pain and increase in MO (unassisted maximum, assisted maximum, and pain-free) were seen at post-treatment 1 year. There were more reduction in pain and increase in all MO in Group 1 than Group 2, but statistical significance was not detected. No major complications were encountered in this study. Although significant differences were not seen between groups, this prospective pilot study provided preliminary evidence that ESWT is safe and potentially beneficial in the treatment of M-TMD.

Advantages of deep learning with convolutional neural network in detecting disc displacement of the temporomandibular joint in magnetic resonance imaging

This study investigated the usefulness of deep learning-based automatic detection of anterior disc displacement (ADD) from magnetic resonance imaging (MRI) of patients with temporomandibular joint disorder (TMD). Sagittal MRI images of 2520 TMJs were collected from 861 men and 399 women (average age 37.33 ± 18.83 years). A deep learning algorithm with a convolutional neural network was developed. Data augmentation and the Adam optimizer were applied to reduce the risk of overfitting the deep-learning model. The prediction performances were compared between the models and human experts based on areas under the curve (AUCs). The fine-tuning model showed excellent prediction performance (AUC = 0.8775) and acceptable accuracy (approximately 77%). Comparing the AUC values of the from-scratch (0.8269) and freeze models (0.5858) showed lower performances of the other models compared to the fine-tuning model. In Grad-CAM visualizations, the fine-tuning scheme focused more on the TMJ disc when judging ADD, and the sparsity was higher than that of the from-scratch scheme (84.69% vs. 55.61%, p < 0.05). The three fine-tuned ensemble models using different data augmentation techniques showed a prediction accuracy of 83%. Moreover, the AUC values of ADD were higher when patients with TMD were divided by age (0.8549–0.9275) and sex (male: 0.8483, female: 0.9276). While the accuracy of the ensemble model was higher than that of human experts, the difference was not significant (p = 0.1987–0.0671). Learning from pre-trained weights allowed the fine-tuning model to outperform the from-scratch model. Another benefit of the fine-tuning model for diagnosing ADD of TMJ in Grad-CAM analysis was the deactivation of unwanted gradient values to provide clearer visualizations compared to the from-scratch model. The Grad-CAM visualizations also agreed with the model learned through important features in the joint disc area. The accuracy was further improved by an ensemble of three fine-tuning models using diversified data. The main benefits of this model were the higher specificity compared to human experts, which may be useful for preventing true negative cases, and the maintenance of its prediction accuracy across sexes and ages, suggesting a generalized prediction.