Head & Face Medicine最新文献

Objective: To determine the efficacy of Centella asiatica extract, ECa233, on alleviating pain symptoms and functional improvement of acute temporomandibular disorders (TMD).

Materials and methods: A randomized, double-blind, placebo-controlled, pilot clinical trial was performed using 23 adults with acute TMD. They were randomly assigned into four treatment groups, an ibuprofen (positive control) group, two test groups of ECa233 each of 250 mg, and 500 mg extracts, and a placebo (negative control) group. All subjects were requested to self-administer the test/control capsules, twice a day for 14 days. The pain intensity score, range of mandibular motion and tenderness of the masticatory muscles and temporomandibular joint (TMJ) were recorded at baseline, 7- and 14-days post-treatment.

Results: One week after intervention, the pain intensity score significantly decreased in participants receiving 500 mg of ECa233 (P = 0.016), as well as the placebo group (P = 0.030) but not in the other groups. Additionally, those receiving 500 mg of ECa233 displayed the highest percentage reduction in self-reported pain intensity and pain on TMJ palpation compared with the other groups (P > 0.050). On day 14, there were no significant differences observed among the evaluated parameters in the four groups.

Conclusions: The orally administered ECa233 has the potential to induce rapid, short term, dose-dependent analgesia in individuals with TMD pain. However, longer-term RCT with a larger cohort is necessary to confirm these findings.

Clinical relevance: ECa 233 at 500 mg has the potential to induce a more rapid analgesic response in individuals with acute TMD after a 7-day period.

Trial registration: This trial was registered on the ClinicalTrials.gov, the number is NCT06231212, date of registration: 30/01/2024.

Background: Obstructive sleep apnea (OSA) can be treated with mandibular advancement devices (MAD), preventing collapse of the upper airway and decreasing apnea-hypopnea index (AHI)/ h. Disease alleviation is expected to vary depending on specific predictors including OSA-severity and cephalometric parameters. This retrospective cohort study aimed to identify predictors of disease alleviation with MAD in adult patients with mild to moderate and severe OSA. Secondary outcomes included assessing the necessity of lateral cephalograms and the therapeutic success in severe OSA-cases.

Methods: OSA-patients, treated with MAD at the orthodontic department of the University Hospital Regensburg, Germany, were allocated to mild to moderate (AHI ≤ 30/ h) and severe OSA groups (AHI > 30/ h). BMI, poly(somno)graphic, demographic and cephalometric variables were evaluated before (T0) and after 3 to 6 months of MAD-treatment (T1). Applying linear regression analyses, predictors were identified, following an assessment of their effect on disease alleviation by independent two-tailed t-tests for continuous, and absolute and relative frequencies for categorical variables. Then, the need for cephalometric analysis and the disease alleviation in severe OSA-patients were evaluated.

Results: Sixty-six predominantly male patients (mean age 55 ± 11 years; male:female = 52:14) were stratified to mild to moderate (n = 45) and severe (n = 21) OSA-groups. Regression analysis revealed baseline-AHI as a significant and relevant predictor, whereas few cephalometric parameters proved significance with small effect sizes (absolute AHI/ h-reduction, univariate model: -0.64 (95% CI: -0.75; -0.53), p < 0.001, R² = 0.666). Compared to mild to moderate OSA-cases, severe OSA-patients had a significantly higher AHI (19.1 ± 11.7 vs. 6.0 ± 4.0, p < 0.001) at T1, but also a higher disease alleviation according to absolute AHI-reduction (-26.1 ± 16.0 vs. -9.6 ± 6.4, p < 0.001), indicating comparable treatment success in all OSA-degrees.

Conclusions: Disease alleviation with MAD in adult OSA-patients can be predicted with specific poly(somno)graphic parameters (especially baseline-AHI), whereas cephalometric variables appeared inappropriate. Still, lateral cephalograms are helpful in the diagnostics and follow-up of MAD-treatment, e.g. for evaluating side effects. Providing good patient selection, therapeutic success can be achieved in both severe and mild to moderate OSA.

Background: The operation accuracy of dynamic navigation is affected by deviation tolerance settings. This in vitro study was aimed to assess the influence of distance and angle deviation tolerances (DDT and ADT) on positioning accuracy in endodontic surgery using dynamic navigation.

Materials and methods: Standardized models were designed and three-dimensional (3D) printed. The drilling depth was 15 mm, where hemispherical cavities were reserved. According to the DDTs and ADTs, they were divided into five groups (n = 10), and the tolerances of distance/angle deviation were set at 0.3 mm/5°, 0.6 mm/3°, 0.6 mm/5°, 0.6 mm/7°, and 0.9 mm/5°. During navigation guidance, the operation was completed from the model surface to the cavity, the trajectory of the approach was fitted and compared with the design path, and the operational accuracy was calculated and analyzed using one-way ANOVA.

Results: When the ADT was 5°, the positioning two-dimensional (2D) distance deviation of the DDT 0.3 mm group and the 0.6 mm group were 0.52 ± 0.14 mm and 0.50 ± 0.07 mm, respectively, smaller than 0.73 ± 0.17 mm of the 0.9 mm group (P <.01). The positioning 3D distance deviation of the 0.3 mm group and the 0.6 mm group were 0.55 ± 0.15 mm and 0.53 ± 0.07 mm, respectively, smaller than 0.74 ± 0.17 mm of the 0.9 mm group (P <.01). When the DDT was set as 0.6 mm, the positioning angle deviation of the ADT 3° group and the 5° group were 2.21 ± 0.42° and 2.60 ± 0.59°, respectively, smaller than 4.72 ± 0.64° of the 7° group (P <.01).

Conclusion: A 0.6 mm DDT and 5° ADT can reduce the positioning deviation of dynamic navigation and obtain better operability. The deviation tolerance of 0.6 mm/5° is suggested for application of dynamic navigation in endodontic surgery. It might improve the operation efficiency and ensure positioning accuracy.

Introduction: Artificial intelligence (AI) has significantly transformed the diagnosis and treatment of dental caries, a prevalent issue in oral health care. Traditional diagnostic procedures such as eye inspection and radiography have limitations in detecting early-stage degradation. Artificial intelligence (AI) provides a viable alternative to improve diagnostic precision and effectiveness. This systematic review examines the diagnostic precision of artificial intelligence systems in identifying dental caries using X-ray images.

Methodology: The literature search utilized electronic web resources such as PubMed, Scopus, Web of Science, IEEE Explore, Google Scholar, Embase, and Cochrane. We conducted the search using specific MeSH key phrases and collected data up to January 2024. The QUADAS-2 assessment method was used to assess the risk of bias using a graph and a heat map. We conducted the statistical analysis using R v 4.3.1 software, which included the "meta," "metafor," "metaviz," and "ggplot2" packages. We displayed the results using odds ratios (OR) and forest plots with a 95% confidence interval (CI).

Results: We used a comprehensive search approach in accordance with the PRISMA guidelines to find appropriate studies. The meta-analysis incorporates fourteen of the 21 articles included in this review. The research mostly uses convolutional neural networks (CNNs) for analyzing images, showing outstanding accuracy, sensitivity, and specificity in detecting caries. Significant variability in study results highlights the need for additional research to comprehend the components affecting AI effectiveness.

Conclusion: Despite challenges in implementation and data availability, this systematic review provides essential information about AI and shows great potential caries detection, improve diagnostic consistency, and ultimately enhance patient care in dentistry.

Background: The aim of this investigation was to evaluate if the hard and soft tissue dentoalveolar process of the mandible follows the tooth movements after lower premolar extractions and anterior retraction in Class III camouflage treatment.

Methods: This retrospective study included 25 patients in retention (f/m 12,13) who had previously been treated with lower premolar extractions for Class III camouflage with a completely customized lingual appliance (Wits at T0 -6.7, ± 2.5 mm). The periodontal and dental health of the lower 6 anterior teeth was evaluated (T1) by a thermal sensitivity test, probing and visual inspection after a mean retention period of 3.1 years (± 2.5, min/max 1.0/9.6 years). A novel non-invasive method was used to measure the thickness of the hard and soft tissue dentoalveolar process on the labial and lingual side of the teeth before treatment (T0) and in retention (T1) at 3 different levels using superimposed intraoral scans. A paired t-test with α = 5% was used to evaluate differences between the endpoints.

Results: At T1, all 25 patients (mean age 26.8 ± 9.7 years, min/max 16.3/49.5 years) presented uncompromised periodontal and dental situations in the lower anterior segment. The presented digital method for evaluating dimensional changes of the dentoalveolar process had excellent reliability (ICC) with a method error of 0.01 mm. The mean total labio-lingual dimension of the hard and soft tissue dentoalveolar process (min/max 7.89/10.02 mm at T0) was identical at T0 and T1 (mean change of 0.00 ± 0.33 mm, min/max -0.98/0.8 mm). At all levels, the teeth moved only 0.12 mm to the lingual side within the dentoalveolar process and therefore, they moved with the dentoalveolar process and not through it.

Conclusion: In non-surgical camouflage treatment with lower premolar extractions in moderate to severe Class III malocclusions, the dentoalveolar process can follow the movement of the mandibular incisors and canines during controlled retraction without any adverse effects.

Introduction: Thermoplastic aligner materials are made from copolymers, and in the oral environment, their mechanical properties change over time. The effects of intraoral temperatures and the wet environments on the stress relaxation properties of these materials remain poorly understood. The aim of this study is to investigate the separate effects of the temperature and wet environment on the stress relaxation behavior of five available commercial orthodontic thermoplastic materials consisting of three chemical compositions.

Method: A modified temperature-controlled water bath system was used to eliminate the confounding effect of water. The residual stresses of five commercial orthodontic thermoplastic materials with different chemical compositions (Biolon, Duran, and Erkodur (PETG), Essix ACE (copolyester), and Essix C + (PP/PE)) were examined at room temperature (22 °C), 37 °C, and 55 °C. After the materials were immersed in deionized water and artificial saliva for two weeks (37 °C), the 30 min stress relaxation curves of the five materials were measured.

Results: Compared with those at room temperature (22 °C), the stress relaxation rates of the five materials increased and ranged from 0.7% to 18.11% at 37 °C and from 20.54% to 88.31% at 55 °C, and Ekodur and Essix ACEs exhibited relatively smaller increases. After two weeks of immersion in deionized water and artificial saliva, the stress relaxation rate of Essix ACE significantly decreased (p < 0.05), whereas that of the other four materials did not significantly change.

Conclusion: Elevated intraoral temperature accelerated the stress relaxation of thermoplastic aligner materials. The intraoral liquid immersion had no accelerating effect on the stress relaxation of any of the tested materials and even had a significant decelerating effect on that of Essix ACE.

Background: Facial aesthetics is one of major motivations for seeking orthodontic treatment. However, even for experienced professionals, the impact and extent of incisor and soft tissue changes remain largely empirical. With the application of interdisciplinary approach, we aim to predict the changes of incisor and profile, while identifying significant predictors.

Methods: A three-layer back-propagation artificial neural network model (BP-ANN) was constructed to predict incisor and profile changes of 346 patients, they were randomly divided into training, validation and testing cohort in the ratio of 7:1.5:1.5. The input data comprised of 28 predictors (model measurements, cephalometric analysis and other relevant information). Changes of U1-SN, LI-MP, Z angle and facial convex angle were set as continuous outcomes, mean square error (MSE), mean absolute error (MAE) and coefficient of determination (R²) were used as evaluation index. Change trends of Z angle and facial convex angle were set as categorical outcomes, accuracy, precision, recall, and F1 score were used as evaluation index. Furthermore, we utilized SHapley Additive exPlanations (SHAP) method to identify significant predictors in each model.

Results: MSE/MAE/R² values for U1-SN were 0.0042/0.055/0.84, U1-SN, MP-SN and ANB were identified as the top three influential predictors. MSE/MAE/R² values for L1-MP were 0.0062/0.063/0.84, L1-MP, ANB and extraction pattern were identified as the top three influential predictors. MSE/MAE/R² values for Z angle were 0.0027/0.043/0.80, Z angle, MP-SN and LL to E-plane were considered as the top three influential indicators. MSE/MAE/R² values for facial convex angle were 0.0042/0.050/0.73, LL to E-plane, UL to E-plane and Z angle were considered as the top three influential indicators. Accuracy/precision/recall/F1 Score of the change trend of Z angle were 0.89/1.0/0.80/0.89, Z angle, Lip incompetence and LL to E-plane made the largest contributions. Accuracy/precision/recall/F1 Score of the change trend of facial convex angel were 0.93/0.87/0.93/0.86, key contributors were LL to E-plane, UL to E-plane and Z angle.

Conclusion: BP-ANN could be a promising method for objectively predicting incisor and profile changes prior to orthodontic treatment. Such model combined with key influential predictors could provide valuable reference for decision-making process and personalized aesthetic predictions.

Background: Virtual simulation has advanced in dental healthcare, but the impact of different tomographic techniques on virtual patient (VP) creation remains unclear. This study primarily aimed to automatically create VP from facial scans (FS), intraoral scans (IOS), multislice (MSCT), and cone beam computed tomography (CBCT); Secondarily, to quantitatively compare artificial intelligence (AI)-driven, AI-refined and semi automatically registered (SAR) VP creation from MSCT and CBCT and to compare the effect of soft tissue on the registration with MSCT and CBCT.

Methods: A dataset of 20 FS, IOS, and (MS/CB)CT scans was imported into the Virtual Patient Creator platform to generate automated VPs. The accuracy (percentage of corrections required), consistency, and time efficiency of the AI-driven VP registration were then compared to those of the AI-refined and SAR (clinical reference) using Mimics software. The surface distance between the registered FS and the (MS/CB)CT surface rendering using SAR and AI-driven methods was measured to assess the effect of soft tissue on registration.

Results: All three registration methods achieved 100% accuracy for VP creation with both MSCT and CBCT (p > 0.999), with no significant differences between tomographic techniques either (p > 0.999). Perfect consistency (1.00) was obtained with AI-driven and AI-refined methods, and slightly lower for SAR (0.977 for MSCT and 0.895 for CBCT). Average registration times were 24.9 and 28.5 s for AI-driven and AI-refined, and 242.3 and 275.7 s for SAR with MSCT and CBCT respectively. The total time was significantly shorter for MSCT (313.7 s) compared to CBCT (850.3 s) (p < 0.001). While the average surface distance between MSCT- and CBCT-based VP showed no significant difference (p > 0.05), AI-driven resulted in a smaller surface distance than SAR (p < 0.05).

Conclusions: AI enables fast, accurate, and consistent VP creation using FS, IOS, and (MS/CB)CT data. AI-driven, AI-refined, and semi-automated methods all achieve good accuracy. Additionally, soft tissue registration shows no significant difference between MSCT and CBCT.

Objectives: This study aimed to enhance clinical diagnostics for quantitative cervical vertebral maturation (QCVM) staging with precise landmark localization. Existing methods are often subjective and time-consuming, while deep learning alternatives withstand the complex anatomical variations. Therefore, we designed an advanced two-stage convolutional neural network customized for improved accuracy in cervical vertebrae analysis.

Methods: This study analyzed 2100 cephalometric images. The data distribution to an 8:1:1 for training, validation, and testing. The CVnet system was designed as a two-step method with a comprehensive evaluation of various regions of interest (ROI) sizes to locate 19 cervical vertebral landmarks and classify precision maturation stages. The accuracy of landmark localization was assessed by success detection rate and student t-test. The QCVM diagnostic accuracy test was conducted to evaluate the assistant performances of our system for six junior orthodontists.

Results: Upon precise calibration with optimal ROI size, the landmark localization registered an average error of 0.66 ± 0.46 mm and a success detection rate of 98.10% within 2 mm. Additionally, the identification accuracy of QCVM stages was 69.52%, resulting in an enhancement of 10.95% in the staging accuracy of junior orthodontists in the diagnostic test.

Conclusions: This study presented a two-stage neural network that successfully automated the identification of cervical vertebral landmarks and the staging of QCVM. By streamlining the workflow and enhancing the accuracy of skeletal maturation estimation, this method offered valuable clinical support, particularly for practitioners with limited experience or access to advanced diagnostic resources, facilitating more consistent and reliable treatment planning.

Background: Migraine is a highly prevalent and disabling disease, affecting nearly 14% of the global population. Preventive medications involve drugs like beta-adrenergic blockers, antidepressants, and anticonvulsants. However, these drugs lacked effectiveness, and patients showed poor tolerance and low adherence to them. Erenumab, a calcitonin gene-related peptide receptor blocker, has recently shown promising results in migraine management. In this meta-analysis, the efficacy of Erenumab is investigated by employing a subgroup analysis approach.

Methods: We conducted a systematic search of six electronic databases until July 2024. Review Manager 5.4 software was utilized for the analysis, based on either weighted mean difference (MD) and standard deviation (SD) for continuous outcomes or risk ratio (RR) for dichotomous outcomes, with a confidence interval (CI) of 95%. A P-value < 0.05 indicated statistical significance. The study was registered on PROSPERO with registration number CRD42024573300. Additionally, we conducted subgroup analyses and assessed the quality of evidence using GRADE.

Results: A total of 20 randomized controlled trials (n = 5212) were included in our analysis. At three months, Erenumab showed statistically significant improvements in monthly migraine days (MMD), monthly acute migraine-specific medication days (MSMD), Headache Impact Test (HIT-6) score, and ≥ 50% reduction from baseline in MMD (MD: -1.78, 95% CI: [-2.37 to -1.20], P < 0.00001), (MD: -1.36, 95% CI: [-1.92 to -0.81], P < 0.00001), (MD: -2.83, 95% CI: [-3.83 to -1.82], P < 0.00001), and (RR: 1.52, 95% CI: [1.31 to 1.76], P < 0.00001), respectively. Subgroup analysis revealed that Erenumab was significantly more effective in patients with prior preventive treatment failures compared to patients with no prior failure. No significant difference in Erenumab`s response existed between episodic and chronic migraine or between 140 and 70 mg, except for MSMD in dose subgrouping. Only constipation emerged as a significant adverse effect in the Erenumab group.

Conclusions: This meta-analysis found that Erenumab significantly reduced migraine attack frequency, medication days, and physical impairment. It was more effective for patients with prior treatment failures. The 140 mg dose showed better MSMD reduction than 70 mg. Erenumab's safety profile was similar to that of placebo, with only constipation noted as significant.