Oral diseases最新文献

Objectives: This study introduces a novel classification approach that combines convolutional neural network (CNN) and Raman mapping to differentiate between tongue squamous cell carcinoma (TSCC) and non-tumorous tissue, as well as to identify different histological grades of TSCC.

Materials and methods: In this study, 240 Raman mappings data from 30 tissue samples were collected from 15 patients who had undergone surgical resection for TSCC. A total of 18,000 sub-mappings extracted from Raman mappings were then used to train and test a CNN model, which extracted feature representations that were subsequently processed through a fully connected network to perform classification tasks based on the Raman mapping data.

Results: The experimental results indicated that the proposed method achieved competitive classification accuracy above 83%. To further validate the effectiveness of the Raman mapping, its performance was compared with Raman spectroscopy, demonstrating a competitive accuracy rate.

Conclusions: The promising outcomes from this application of CNN in Raman mapping suggest that this technique could be a reliable method for intraoperative assessment of surgical margins, potentially leading to shorter detection times.

Objective: This study aimed to simplify the Jaw Functional Limitation Scale (JFLS) and evaluate its reliability and validity in assessing jaw function in patients with temporomandibular disorders (TMD) in primary care.

Methods: TMD patients and non-TMD participants were assessed using Diagnostic Criteria for Temporomandibular Disorders (DC/TMD). Demographic data and the 8-item JFLS questionnaire were collected to quantify jaw functional restriction. Confirmatory factor analysis tested the validity of JFLS-8 using chi-square, comparative fit index (CFI), root mean square error of approximation (RMSEA), and root mean square residual (RMR). The study selected key items from each domain to create a 4-item JFLS for further testing. Exploratory factor analysis, reliability, and validity tests were conducted.

Results: Analysis included 671 valid questionnaires (502 TMD patients, 169 non-TMD participants). JFLS-8 exhibited satisfactory fit among TMD patients (RMSEA = 0.143, CFI = 0.881, RMR = 0.567). JFLS-4 retained items with high factor loadings: JFLS1 (0.935), JFLS2 (0.923), JFLS7 (0.766), and JFLS5 (0.713). The simplified JFLS-4 showed good reliability (Cronbach's alpha 0.714) and validity (Kaiser-Meyer-Olkin 0.558, Bartlett's test < 0.001) among TMD patients.

Conclusion: A 4-item JFLS was developed and validated, demonstrating good reliability and validity in assessing mandibular function in TMD patients, exhibiting the potential to streamline evaluation processes in clinical practice.

Objectives: To externally validate a clinical prediction model for surgical site infection (SSI) after lower third molar (L3M) surgery and evaluate its clinical usefulness.

Methods: We conducted a retrospective cohort study of patients who underwent L3M surgery at Hokkaido University Hospital. The study was designed to evaluate the historical and methodological transportability. Clinical usefulness was evaluated using decision curve analysis on the data of the non-antibiotic-treated patients.

Results: We obtained 2543 validation cohorts from April 2020 to March 2023, and 640 non-antibiotic cohorts from July 2010 to September 2023. The incidences of SSI after L3M surgery were 5.3% (135/2543) and 7.7% (49/640) in the validation and non-antibiotic cohorts, respectively. The discrimination ability of the prediction model was acceptable for the external validation cohort (c-statistic: 0.67; 95% CI: 0.62-0.71) and adequate for the non-antibiotic cohort (c-statistic: 0.72; 95% CI: 0.63-0.79). In both cohorts, the model showed excellent calibration between the observed and predicted probabilities. Decision curve analysis showed increased net benefit across a range of meaningful risk thresholds.

Conclusion: A simple risk prediction model for SSI after L3M surgery demonstrated clinical transportability and usefulness. This model may help surgeons/clinicians determine the appropriateness of prophylactic antibiotics administration for patients in L3M surgery.

Introduction: Junctional epidermolysis bullosa (JEB) is a rare genetic disease manifesting with skin and mucosal blistering. As part of the JEB, patients present with syndromic amelogenesis imperfecta (AI). Reports have described external crown resorption (ECR) in the teeth of patients with JEB, but its prevalence is unknown.

Objective: To determine the prevalence of ECR in patients with JEB.

Methods: A longitudinal retrospective cohort study was performed at the Special Care Dentistry Clinic, University of Chile. Clinical records of patients with JEB between 2005 and 2024 were analysed. Prevalence of ECR per patient, per type of tooth and per tooth was calculated.

Results: Of the 20 patients examined, 90% presented ECR in at least one tooth, with an average of 4.8 primary and 6.8 permanent teeth affected. The most affected type of teeth were the incisors. 57.5% of primary incisors and 68% of permanent incisors had resorption. The most affected tooth was #82 in primary dentition (75%) and #32 in the permanent dentition (88.9%).

Conclusions: The prevalence of ECR in patients with AI due to JEB caused by variants in LAMB3 was 90%. Establishing clinical and radiographic dental protocols for the early detection of ECR is essential to prevent extensive tooth destruction.

Objective: This study aimed to explore the risk factors for death in extremely severe oral and maxillofacial space infection (OMSI) patients and to use these findings to establish a nomogram model that can be used to predict individual patient prognosis.

Methods: Patients with extremely severe OMSI seen between 2020 and 2024 were enrolled (N = 102). Univariate and multivariate logistic regression were used to analyse the associations between clinical factors and the risk of death, and a nomogram was developed to visualise the model. Model accuracy and clinical utility of the nomogram was evaluated using receiver operating characteristic (ROC) curves, calibration curve and decision curve analysis (DCA).

Results: Six variables were associated with death. Three independent risk factors for death were identified by multivariate logistic regression: history of rheumatoid arthritis (RA) (OR: 7.716; 95% CI: 0.988-52.122; p = 0.050), D-dimer (OR: 1.162; 95% CI: 1.048-1.28; p = 0.004), and serum creatinine level (OR: 1.011; 95% CI: 1.002-1.020; p = 0.018). The nomogram had an area under the receiver operating characteristic curve (AUC) of 0.819.

Conclusion: A history of RA, D-dimer and serum creatinine are independent prognostic factors for overall survival in OMSI patients. The nomogram model showed good concordance and accuracy.