A study on volumetric change of mandibular condyles with osteoarthritis using cone-beam computed tomography.

This study aimed to quantitatively assess three-dimensional changes in the mandibular condyle with osteoarthritis using cone-beam computed tomography (CBCT). Pre- and post-treatment CBCT images of temporomandibular joints (TMJs) from 66 patients were used to assess longitudinal changes in condylar volume within individual patients using 3D slicer software. Total volume difference (dV), net increase (dV + , bone deposition), and net decrease (dV- , bone resorption) after treatment were analyzed based on clinical and radiological factors. Condyles with surface erosion at their first visit showed significantly decreased volume after treatment compared to condyles without erosion (p < 0.05). Amounts of bone resorption and deposition were higher in condyles with surface erosion (both p < 0.01). In patients with condylar erosion, the presence of joint pain was associated with a decrease in condylar volume and an increase in net resorption (both p < 0.01). When both joint pain and condylar erosion were present, patients with parafunctional habits showed reduced condylar volume after treatment (p < 0.05). Condylar volume change after treatment was negatively correlated with the duration of pain relief (R = - 0.501, p < 0.05). These results indicate that condylar erosion and TMJ pain could be significant variables affecting TMJ volume changes after treatment. Establishing appropriate treatment strategies is crucial for managing condylar erosion and TMJ pain.

Deep learning for osteoarthritis classification in temporomandibular joint.

OBJECTIVES: This study aimed to develop a diagnostic support tool using pretrained models for classifying panoramic images of the temporomandibular joint (TMJ) into normal and osteoarthritis (OA) cases. SUBJECTS AND METHODS: A total of 858 panoramic images of the TMJ (395 normal and 463 TMJ-OA) were obtained from 518 individuals from January 2015 to December 2018. The data were randomly divided into training, validation, and testing sets (6:2:2). We used pretrained Resnet152 and EfficientNet-B7 as transfer learning models. The accuracy, specificity, sensitivity, area under the curve, and gradient-weighted class activation mapping (grad-CAM) of both trained models were evaluated. The performances of the trained models were compared to that of dentists (both TMD specialists and general dentists). RESULTS: The classification accuracies of ResNet-152 and EfficientNet-B7 were 0.87 and 0.88, respectively. The trained models exhibited the highest accuracy in OA classification. In the grad-CAM analysis, the trained models focused on specific areas in osteoarthritis images where erosion or osteophyte were observed. CONCLUSIONS: The artificial intelligence model improved the diagnostic power of TMJ-OA when trained with two-dimensional panoramic condyle images and can be effectively applied by dentists as a screening diagnostic tool for TMJ-OA.

Influence of psychological factors on the prognosis of temporomandibular disorders pain.

BACKGROUND/PURPOSE: The leading symptom of temporomandibular disorders (TMD) is pain, and psychological factors are involved in the persistence of TMD-related pain. Therefore, this study was aimed to analyze the influence of psychological factors on the prognosis of TMD-related pain. MATERIALS AND METHODS: The medical records of 486 patients with TMD-related pain were analyzed. Each patient's psychological profile was assessed using the Symptom Checklist-90-Revised (SCL-90-R). Patients were classified into two groups according to a post-treatment numeric rating scale (NRS). Patients with an NRS score of 0 or 1 at the last visit were classified into group G, and those with an NRS score of 2 or greater were classified into group P. Following this, all patients were re-classified into groups N and R according to pain recurrence. Statistical analysis was performed to evaluate differences in the SCL-90-R T scores between the groups. In addition, multiple logistic regression analysis was used to identify psychological factors that affected treatment outcome. RESULTS: The patients in groups P and R had higher scores in all subscales of the SCL-90-R than groups G and N, respectively. In particular, somatization (SOM) and psychoticism (PSY) scores showed significant differences between the groups in the treatment outcome. CONCLUSION: A correlation is identified between psychological factors and treatment outcome in patients with TMD-related pain. In particular, patients with elevated SOM and PSY scores are more likely to develop refractory pain, and thus require additional interventions to control this risk.

Inhibitory effects of somatostatin on the substantia gelatinosa neurons of trigeminal subnucleus caudalis via somatostatin type 2 receptors in juvenile mice.

The substantia gelatinosa (SG) of the trigeminal subnucleus caudalis (Vc) receives many thin-myelinated Adelta-fiber and unmyelinated C primary afferent fibers and has been implicated in the processing of nociceptive information. Somatostatin (SST) is a neuromodulator in the brain and spinal cord. A number of studies have demonstrated that SST can play a key role in pain modulation at the spinal cord level. However, there is little information available on functional SST receptor expression in the SG neurons of the Vc in mice. This study examined the direct membrane effects of SST and SST receptor type 2 agonist, seglitide (SEG) on the SG neurons of Vc in gramicidin perforated current clamp mode. In addition, SSTR2 mRNA expression was detected on the SG neurons using single cell RT-PCR in juvenile mice. Most SG neurons (37/68, 54%) were hyperpolarized after a bath application of SST. When SST was applied in stages, the second responses (83% of the first response) were less intense than those after the first application suggesting that SSTRs are desensitized by repeated application. The SST-induced hyperpolarizing response was maintained in the presence of TTX (Na(+) channel blocker), AP-5 (NMDA receptor antagonist), CNQX (non-NMDA glutamate receptor antagonist), picrotoxin (GABA(A) receptor antagonist) and strychnine (glycine receptor antagonist), respectively, suggesting that SST has direct effects on the postsynaptic SG neurons. SSTR2 mRNA was detected in 11 out of 28 (39%) SG neurons tested. The SST-induced hyperpolarizing effects were mimicked by SEG, a SSTR2 agonist. These results suggest that functional SSTR2 receptors are expressed on the SG neurons of Vc in juvenile mice and can be a potential target for modulating orofacial pain.