Persistent pain after dental procedures is often interpreted as odontogenic pain, treatment failure, temporomandibular disorder, or unexplained facial pain. This focused review aimed to propose a practical dental practice-based triage pathway for recognizing dental procedure-related post-traumatic trigeminal neuropathic pain (PTNP). Established diagnostic criteria, clinical phenotyping studies, trigeminal nerve injury assessment methods, and expert consensus on trigeminal nerve injuries were narratively synthesized. The pathway emphasizes early recognition of neuropathic warning symptoms, chairside sensory screening, exclusion of active odontogenic pathology, assessment of ongoing or surgically correctable mechanical causes, avoidance of unnecessary irreversible retreatment, and selective referral. Dental procedure-related PTNP should be considered when persistent pain is accompanied by sensory disturbance in a trigeminal nerve distribution.

Purpose: This study aimed to develop a clinically interpretable risk stratification model for identifying patients with the Graded Chronic Pain Scale (GCPS)-defined high-impact pain among patients with painful temporomandibular disorders (TMD) using L1-regularized logistic regression. Methods: This secondary cross-sectional analysis included 1,488 adult patients with painful TMD diagnosed according to Research Diagnostic Criteria for Temporomandibular Disorders Axis I. High-disability pain was defined as GCPS grades III-IV, and low-disability pain as grades I-II. Candidate predictors included demographic factors, TMD diagnostic category, pain severity (PS) and interference, pain catastrophizing, subjective sleep quality, and psychological distress measures. Variables with multicollinearity or conceptual overlap with GCPS grading were excluded. An L1-regularized logistic regression model with class weighting was fitted, and performance was assessed using the area under the receiver operating characteristic curve (ROC-AUC). Results: Among 1,488 patients, 518 (34.8%) were classified as having high-impact pain. The final model retained PS, pain catastrophizing, poor subjective sleep quality, and somatization as key predictors. PS showed the strongest association with high-impact pain (odds ratio [OR]=2.99, 95% confidence interval [CI] 2.54-3.52), followed by pain catastrophizing (OR=1.39, 95% CI 1.20-1.61), poor sleep quality (OR=1.16, 95% CI 1.01-1.32), and somatization (OR=1.17, 95% CI 1.02-1.34). Sex and the TMD diagnostic category were not significant predictors. The model demonstrated acceptable discrimination, with an AUC of 0.777 on the held-out test set. Conclusions: GCPS-defined high-disability pain in painful TMD was characterized mainly by PS, catastrophizing, poor sleep quality, and somatic symptom burden. L1-regularized logistic regression provided a reduced-complexity and clinically interpretable model for biopsychosocial stratification of high-disability pain.

Purpose: By evaluating the coverage status of the mandibular condylar cortical bone in cone-beam computed tomography (CBCT), mandibular growth can be directly assessed. This study aimed to develop an automated method for assessing condylar development using deep learning techniques on CBCT images and to evaluate its clinical applicability. Methods: This study was conducted using CBCT images from 825 patients aged 13-25 years who visited Chosun University Dental Hospital. For each subject, one frontal, sagittal, and transverse view was obtained and analyzed, using the mandibular condyle as the reference point. The extracted left and right temporomandibular joint images (three views each) were input into convolutional neural network algorithms to develop a regression model for condylar age assessment by evaluating the relationship between chronological age and temporomandibular joint features. Using the regression models, the correlation coefficient between the actual and estimated ages, as well as the estimation error, was calculated. Additionally, we manually classified condylar cortication into three stages and compared the distributions of chronological age and predicted age with two-way ANOVA. Results: The estimated age from the regression model showed a strong correlation with the actual age derived from the CBCT images, with a Pearson correlation coefficient of 0.851, a mean absolute error of 1.540 years, a root mean squared error of 1.956 years, and an R-squared value of 0.722. The two-way ANOVA revealed no significant effect of age estimation relative to actual age (Right: F=1.099, p=0.296; Left: F=1.139, p=0.287), indicating comparable distributions across condyle cortication. Conclusions: Although this study is limited by the relatively small sample size, the mandibular condylar age assessment model developed in this study provides a novel methodology for evaluating mandibular growth.