Head & Face Medicine最新文献

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.

Background: The aim of this study was to investigate the effect of defined head-motion during x-ray exposure on the identification accuracy of typical cephalometric reference points which form the basis of treatment planning.

Methods: By means of a dry adult human skull and a precise motion simulation system digital Cephs are acquired while certain predefined movement patterns (shift, tilt and nodding with a motion amplitude from 5 - 50 mm) of the skull were executed. They represent the movements of children and adolescents, the main group for cephalometric radiographs.The scanning time was 9.4 s per Ceph. 10 typical landmark points of cephalometric analysis were identified by 20 observers on each Ceph twice. Using a non-motion image (Ceph0) as reference, displacement was computed as vectors relative to this image. Commonly used angles and vertical and horizontal distances were calculated.

Results: Both inter-rater as well as intra-rater-reproducibility were perfect. There was very little change in the vertical distance N-Me, in contrast to the horizontal distance S-N which showed a large variation. So patient motion parallel to the scanning direction of the fan-beam-detector unit, heavily influence distances parallel to this direction. The ANB angle and the Maxillo-Mandibular Plane Angle (ANS-PNS to Me-Go) only varied by about 1-2°, but large enough to greatly influence a treatment plan.

Conclusions: The study observed a severe influence on reference point location of motion patterns parallel to the scanning direction and also on clinically relevant distances parallel to the scanning direction. Therefore, we recommend to use a horizontal scanning direction, to minimise scanning time to a minimum, or to prefer a one-shot technique if possible. Future advancements in this field may include the integration of artificial intelligence or algorithms for the purpose of motion correction.

Background and objective: There are still many controversies about the factors influencing maxillary sinus cysts and their clinical management. This study aims to construct a prediction model of maxillary sinus cyst and explore its clustering pattern by cone beam computerized tomography (CBCT) technique and machine learning (ML) method to provide a theoretical basis for the prevention and clinical management of maxillary sinus cyst.

Methods: In this study, 6000 CBCT images of maxillary sinus from 3093 patients were evaluated to document the possible influencing factors of maxillary sinus cysts, including gender, age, odontogenic factors, and anatomical factors. First, the characteristic variables were screened by multiple statistical methods, and ML methods were applied to construct a prediction model for maxillary sinus cysts. Second, the model was interpreted based on the SHapley Additive exPlanations (SHAP) values, and the risk of maxillary sinus cysts was predicted by generating a web page calculator. Finally, the K-mean clustering algorithm further identified risk factors for maxillary sinus cysts.

Results: By comparing the various metrics in the training and test sets of multiple ML models, eXtreme Gradient Boosting (XGBoost) is the best model. The average area under curve (AUC) values of the XGBoost model in the training, validation, and test sets, respectively, are 0.939, 0.923, and 0.921, which indicates its excellent classification and discrimination ability. The cluster analysis model further categorized maxillary sinus cysts into high-risk and low-risk groups, with apical lesions, severe periodontitis, and age ≥ 53 as high-risk factors for maxillary sinus cysts.

Conclusion: These findings provide valuable insights into the etiology and risk stratification of maxillary sinus cysts, offering a theoretical basis for their prevention and clinical management. The integration of CBCT imaging and ML techniques holds the potential for prevention and personalized treatment strategies of maxillary sinus cysts.

Background: The purpose of this in vitro study was to compare the surface roughness (Ra) changes of different dental ceramic materials with different compositions, which were applied two different surface treatments after exposure to acidic pH. The purpose of this in vitro study was to compare the Ra changes of different CAD-CAM materials with different compositions, which were applied two different surface treatments, after exposure to acidic pH.

Methods: A total of the 168 samples (12 × 14 × 2 mm) were obtained from ceramic blocks (IPS e.max CAD (LDS)), GC Cerasmart (RNC-C), Lava Ultimate (RNC-L), and Vita Enamic (PIC). Half of each group was subjected to mechanical polishing, and the other half was glazed. After the initial Ra evaluations were made, the samples classified with 7 in each subgroup were kept in three different solutions (citric acid, Coca-Cola, and artificial saliva-control group). After 168 h, surface roughness values of the specimens were measured again.

Results: In the RNC-C samples, varying surface treatments and exposure to various solutions did not produce a statistically significant difference. Different acidic solutions did not affect the Ra values of LDS and RNC-C ceramics. The percentage change in Ra values in the glazed samples of PIC exposed to Coca-Cola and RNC-L exposed to artificial saliva were higher than those applied mechanical polishing.

Conclusion: The Ra values of RNC-C ceramics were not affected by both surface treatment and acid exposure. The percentage change in Ra values was highest in PIC ceramics. In general, glazed samples had larger Ra change values and higher percentage change in Ra values than manually polished ones.