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 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.

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.

Ji Hoo Kim, Hyun-Jeong Park, Yo-Seob Seo, Ji-Won Ryu, Jong-Mo Ahn. JOMP 2022;47:27-37. https://doi.org/10.14476/jomp.2022.47.1.27

Ji Hoo Kim, Jong-Mo Ahn. JOMP 2021;46:33-40. https://doi.org/10.14476/jomp.2021.46.2.33