This study provides an updated overview of the clinical characteristics of post-traumatic trigeminal neuropathic pain (PTNP) resulting from dental procedures or facial trauma, addressing its etiology, prevalence, evaluation, management, and prognosis. PTNP arises from injury to the trigeminal nerve, which governs sensory and motor functions in the maxillofacial region. The prevalence and characteristics of PTNP vary considerably across studies, with a reported prevalence ranging from 1.55% to 13%. The predominant causative factors are dental procedures, particularly third molar removal and implant placement. While gender distribution varies, a trend towards higher incidence in females is observed, particularly within the 40–60-year age group. Anatomically, the mandibular nerve is frequently involved. PTNP presents with a spectrum of symptoms ranging from tingling sensations to severe pain. Diagnostic challenges arise due to the lack of standardized criteria and potential overlap with focal neuralgia, necessitating comprehensive evaluation. Misdiagnosis can lead to prolonged patient suffering and unnecessary interventions. Successful management hinges on prompt diagnosis and interdisciplinary collaboration, with early intervention crucial in mitigating progression to chronic pain. Although nerve recovery post-trauma is challenging, preventive measures through accurate evaluation and treatment are paramount. Management strategies for PTNP include non-invasive and surgical interventions, with non-invasive approaches encompassing systemic and local pharmacological management. This narrative review aims to enhance uniformity in PTNP evaluation and treatment approaches, ultimately improving patient care and outcomes.

The aim of this study was to evaluate cortical bone formation on the mandibular condyle using cone-beam computed tomography (CBCT) in asymptomatic adolescents and young adults and to evaluate the relationship between age and sex. CBCT images that can evaluate the shape of the mandibular condyle were selected from asymptomatic patients aged 13–25. The degree of cortication on the mandibular condyle (CMC) was evaluated using CBCT images reconstructed in the axial, sagittal, and coronal planes. CBCT data of 829 patients (413 males, 416 females) were selected and then the left and right images of all patients were acquired; consequently, a total of 1658 temporomandibular joint-related images were evaluated in this study. The degree of CMC was correlated with age in men and women (p < 0.05). The frequency of CMC 0 disappeared in woman aged 20 years and in men aged 21 years. Cortical bone formation of the mandibular condyle was completed at age 22 years in women and 24 years in men. The degrees of cortical bone formation of the mandibular condyle between men and women showed significant differences between the ages of 15–19 and 22 years. This difference can be interpreted as a different mandible growth period between the sexes.

Background: Tongue squamous cell carcinoma (TSCC) is an aggressive oral malignancy characterized by early submucosal invasion and a high risk of cervical lymph node metastasis. Accurate and timely diagnosis is essential, but it remains challenging when relying solely on conventional imaging and histopathology. This systematic review aimed to evaluate studies applying artificial intelligence (AI) in the diagnostic imaging of TSCC. Methods: This review was conducted under PRISMA 2020 guidelines and included studies from January 2020 to December 2024 that utilized AI in TSCC imaging. A total of 13 studies were included, employing AI models such as Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and Random Forest (RF). Imaging modalities analyzed included MRI, CT, PET, ultrasound, histopathological whole-slide images (WSI), and endoscopic photographs. Results: Diagnostic performance was generally high, with area under the curve (AUC) values ranging from 0.717 to 0.991, sensitivity from 63.3% to 100%, and specificity from 70.0% to 96.7%. Several models demonstrated superior performance compared to expert clinicians, particularly in delineating tumor margins and estimating the depth of invasion (DOI). However, only one study conducted external validation, and most exhibited moderate risk of bias in patient selection or index test interpretation. Conclusions: AI-based diagnostic tools hold strong potential for enhancing TSCC detection, but future research must address external validation, standardization, and clinical integration to ensure their reliable and widespread adoption.

In children and adolescents, dental age estimation is performed with the development of the teeth. Various statistical analysis methods have been used to determine the relationship between age and dental maturity and develop an accurate method of age calculation. This study attempted to apply a neural network model for the statistical analysis of dental age estimation in children and evaluated its applicability. This study used 1196 panoramic radiographs of patients aged 3–16 years, and 996 and 200 were randomly classified into training and test sets, respectively. The dental maturity of the mandibular left teeth was evaluated using Demirjian's method, the neural network model using the backpropagation algorithm was derived using training sets, and the errors were evaluated using 100 radiographs of each male and female as test sets. In addition, multiple linear regression analysis was conducted on the same training set, and the error was calculated by applying it to the test set and comparing it with the error of the neural network model. In the neural network model, the mean absolute error (MAE) and root mean squared error (RMSE) were 0.589 and 0.783 in male subjects and 0.529 and 0.760 in female subjects, respectively. In the multiple linear regression model, the MAE and RMSE were 0.600 and 0.748 in male subjects and 0.566 and 0.789 in female subjects, respectively. When applying the neural network model to the statistical analysis of the dental developmental stage, the results were as accurate as those of conventional statistical analysis methods. This study’s approach is expected to be useful for estimating the ages of children.

The relationship between fixed orthodontic treatment and the development of temporomandibular disorders (TMDs) in adolescents has been a topic of considerable debate. This systematic review and meta-analysis aimed to evaluate the impact of fixed orthodontic treatment on the prevalence of TMDs in adolescents. A comprehensive literature search was conducted using PubMed, Web of Science, EMBASE, Google Scholar, and the Cochrane Library, yielding 886 records. After duplicate removal, 665 records were screened, and 8 studies were assessed for eligibility. Following quality assessment using the Joanna Briggs Institute checklist, 4 studies were included in the final analysis. Data were analyzed using a random-effects model in RevMan software. The meta-analysis revealed an overall odds ratio of 0.75 (95% CI: 0.37–1.51, p = 0.42), indicating no statistically significant association between fixed orthodontic treatment and the risk of developing TMDs. Substantial heterogeneity was observed (I² = 73%), attributed to variations in study designs, populations, and outcome measures. The risk of bias analysis highlighted concerns in several domains, particularly selection bias and measurement of outcomes. While confounding bias and missing data bias were generally well-controlled, deviations in intervention and inconsistent outcome measurements were noted across the studies. These findings suggest that fixed orthodontic treatment does not significantly alter the risk of developing TMDs in adolescents. However, the substantial heterogeneity and potential biases across the included studies emphasize the need for further high-quality, standardized research to confirm these results and provide clearer clinical guidance.