Journal of Orofacial Orthopedics-Fortschritte Der Kieferorthopadie最新文献

Purpose: To investigate the effectiveness of sugar-free chewing gum on pain reduction in orthodontic patients after placement of separators and initial archwires, and to compare the impact of pain on daily functions between patients using and not using chewing gum.

Materials and methods: A total of 93 patients were randomized into the intervention group (n = 47, mean age = 22.62 ± 4.73) and the control group (n = 46, mean age = 21.88 ± 5.79). The patients completed a set of questionnaires at three different time points (6 h, 24 h, and 48 h) after the placement of separators and initial archwires. Numerical rating scales (NRS) were used to assess the intensity of pain. The impact on orthodontic pain towards patients' oral health-related quality of life (OHRQoL) was recorded.

Results: There were no statistically significant differences in the mean NRS pain scores between the two groups at any time points. However, the intervention group showed higher mean NRS pain scores for both procedures from a clinical perspective. Similarly, no statistically significant differences were found between the groups in terms of the impact of orthodontic pain on patients' OHRQoL. Nonetheless, chewing gum appeared to reduce the impact of orthodontic pain on daily functions following separators placement.

Conclusion: While sugar-free chewing gum may not significantly reduce orthodontic pain, it could help mitigate the pain's impact on OHRQoL after separators' placement, potentially reducing the need for analgesics.

Objective: To evaluate the association between tooth agenesis and root morphology alterations.

Methods: This retrospective observational study initially analyzed 27,707 medical records of patients from radiological centers in southeastern Brazil. After applying the inclusion and exclusion criteria, 1193 sets of periapical radiographs of maxillary incisors were selected from eligible patients aged between 8 and 30 years. The root morphology of the upper incisors was classified into five categories: normal, short, blunt, apically bent, or pipette-shaped, based on criteria established in the literature. In addition, missing teeth were recorded applying the tooth agenesis code (TAC). Two previously calibrated specialists analyzed the radiographs visually. Logistic regression with odds ratios (OR) estimated the associations between tooth agenesis and root alterations at a 5% significance level.

Results: Patients with any form of tooth agenesis were more likely to have pipette-shaped (i.e., roots with a narrow cervical third and gradual tapering toward the apex; OR = 7.04; 95% confidence interval [CI] 4.23-11.72) or blunt (OR = 5.31; 95% CI 2.62-10.75) roots. Conversely, the agenesis group showed a significantly lower prevalence of normal roots than the group without agenesis (OR = 0.12; 95% CI 0.07-0.20).

Conclusion: Root morphology alterations are strongly associated with tooth agenesis. This knowledge should be used for customized approaches and multidisciplinary strategies to minimize complications and improve treatment in this group.

Purpose: The aim of this study was to evaluate the potential association between common nasal deformities (ND) and temporomandibular disorders (TMD) according to the diagnostic criteria (DC/TMD).

Patients and methods: Enrolled subjects were initially screened using the DC/TMD and then evaluated by an otorhinolaryngologist (ENT specialist) for the presence of ND, relative severity of these, as well as the signs and symptoms. The identified ND were categorized into four major groups: nasal septal deviation (NSD), inferior turbinate hypertrophy (ITH), a combination of both NSD and ITH, and other deformities. The relationships between variables and differences between groups were analyzed using Χ² test and Mann-Whitney U test. To identify the contribution of nasal deformity towards TMD diagnosis, hierarchical logistic regression analysis was conducted.

Results: The study population comprised 368 participants (185 females and 183 males). A total of 98 (26.7%) patients were diagnosed with TMD, while 152 (41.3%) participants had one or more types of ND. The presence of ND was associated with an increased incidence of intra-articular disorders (IAD; p = 0.05) and joint clicking (p = 0.027). Nasal signs and symptoms were significantly associated with clicking (p = 0.025). NSD was associated with a 1.77 increase in the odds of having a TMD diagnosis (p = 0.020), a 2.45 increase in the odds of having IAD (p = 0.005), and a 1.62 increase in the odds of having joint clicking (p = 0.044).

Conclusion: The presence of ND, especially NSD, appears to be associated with TMD, most notably with IAD, in addition to joint clicking. Therefore, individuals with ND should be carefully screened for TMD and referred for further evaluation whenever indicated. Further studies are required to validate these findings.

Purpose: To evaluate the knowledge and attitude of specialists in orthodontics and oral radiology in relation to artificial intelligence (AI) and its use in cephalometric analysis.

Methods: A cross-sectional study was conducted with electronic questionnaires distributed to Brazilian orthodontists and oral radiologists. Demographic and professional data were collected, as well data on general use of AI and cephalometric-related use of AI. Data analysis was performed using descriptive statistics and χ² test and Student's t‑test.

Results: A total of 139 questionnaires were answered. Reported knowledge regarding AI showed a significant association with the questions 1) presence of AI in daily life (P = 0.021), 2) having AI as a topic in lectures during their education (P = 0.010), and 3) willingness to incorporate AI into dentistry (P = 0.002). Oral radiologists were more willing to incorporate AI into clinical practice (P = 0.004), while orthodontists agreed more with the idea of including the AI diagnosis together with the one delivered by the clinician (P = 0.014). Most participants welcomed the use of fully automated software to identify landmarks in cephalometric analysis (78.4%) and believed that human error is greater than AI's during landmark identification (52.5%).

Conclusion: Brazilian orthodontists and oral radiologists acknowledge the use of AI and express positive attitudes and confidence toward its application in cephalometric analysis. Professionals who reported greater knowledge of AI demonstrated more openness to incorporating this technology into their clinical routine.

Objective: To investigate quality of life changes, family impacts, and predictors of successful overjet (OJ) reduction in patients treated with removable functional appliances for prominent upper front teeth.

Patients and methods: A total of 86 cases were analyzed, whose ages ranged from 11 to 13 years (median 12; 43% female). They were randomly allocated to treatment by twin block (TB) and Sander bite-jumping appliance (SBJ). OJ was measured before and after 1 year, and the Child Perceptions Questionnaire, Parental-Caregivers Perceptions Questionnaire, and Family Impact Scale were self-administered.

Results: Treatment was unsuccessful in 33% of patients (discontinued regular check-ups or OJ reduction ≤ 40%). In successfully treated individuals, there was a reduction in problems related to child emotional well-being (EW) and oral symptoms, parental EW, and family impacts (p ≤ 0.010). In unsuccessfully treated individuals, there was a reduction in problems related to child EW and social well-being (p ≤ 0.036). In the multiple logistic regression model, sex, type of appliance, and parental perception of the child's functional limitations before treatment were predictors of successful OJ reduction (p < 0.001). Success odds were 8.1 times increased with TB compared to SBJ appliance (95% confidence interval [CI] 2.4-27.6; p = 0.001), 5 times in males (95% CI 1.6-15.5; p = 0.006) and 5 times when parents observed lowered functional limitations (≤ 4) in their child (95% CI 1.5-16.4; p = 0.008).

Conclusion: Appliance type, sex, and parental perceptions of a child's functional limitations predicted treatment success. Both successfully and unsuccessfully treated young adolescents reported improvement in oral health-related quality of life, implying that unsuccessfully treated patients convinced themselves that it was worth being treated.

Purpose: This study assessed the accuracy and repeatability of the orthodontics-related information generated by Chat Generative Pre-trained Transformer (ChatGPT, model GPT-4o, 18 July 2024, OpenAI, San Francisco, CA, USA) and evaluated its usefulness for patient education by comparing artificial intelligence (AI)-generated responses to questions that patients frequently search for with responses from two orthodontic specialists.

Materials and methods: In January and February 2025, 30 descriptive questions (15 on basic orthodontics and 15 on clinically advanced orthodontics) on nondecision, informational content in patient education were assessed, including a "briefly summarize within 500 characters" condition. Accuracy was defined as a response match between ChatGPT and the orthodontist, repeatability was the consistency of ChatGPT output over two iterations. Evaluations used a 5-point Likert scale for accuracy and a 5-point Global Quality Score (GQS) for repeatability. Data were analyzed using R, Wilcoxon signed-rank test and the Mann-Whitney U test.

Results: Repeated responses from ChatGPT showed high repeatability with consistent overall accuracy. For basic orthodontics questions, accuracy increased slightly from 4.27 ± 1.03 to 4.53 ± 0.83 (p = 0.203). For clinically advanced orthodontics questions, accuracy remained stable (first: 3.67 ± 1.05, second: 3.73 ± 0.46, p = 0.850). In terms of repeatability and quality assessed by GQS, basic orthodontic questions scored moderately (first: 3.13 ± 0.83, second: 3.27 ± 0.70, p = 0.580), whereas clinically advanced orthodontics questions received higher GQS scores (first: 4.20 ± 0.77, second: 3.80 ± 0.56, p = 0.095), indicating potential applicability in patient education contexts.

Conclusion: The accuracy and repeatability of ChatGPT's responses varied by question type: basic questions were more accurate, while clinically advanced orthodontic questions resulted in higher repeatability and quality. As these findings are limited to patient education and general information delivery, ChatGPT should not be considered a replacement for professional orthodontic expertise.

Aim: The aim of this retrospective study was to evaluate the accuracy of rotational movements of maxillary and mandibular canines and premolars achieved using clear aligners combined with a segmental lingual appliance, as planned in the digital setup, and to compare these results with those obtained using clear aligners alone.

Materials and methods: This retrospective cohort study included 40 adult patients, analyzing 40 teeth (20 canines and 20 premolars), each presenting at least 15° of rotation. The sample was divided into two groups: a study group treated with F22 aligners (Sweden & Martina, Due Carrare, Padua, Italy) combined with a segmental lingual appliance to correct rotation without attachments and a control group treated with F22 aligners using vestibular attachments to facilitate the rotational movement. Digital models acquired at three stages-pretreatment (T0), ideal posttreatment (T1), and actual posttreatment (T2)-were analyzed using visual aesthetic malocclusion (VAM, Vectra, Canfield Scientific, Fairfield, NJ, USA) software to assess rotation values and calculate imprecision and accuracy for each tooth. Statistical analysis was conducted to compare the two groups.

Results: The study group showed significantly higher accuracy in rotational movements compared to the control group. The mean accuracy was 88.3% for the hybrid approach, versus 55.7% for clear aligners with attachments alone, and this difference was statistically significant (p < 0.01). This improvement was consistent across both canines and premolars, with statistically significant differences in all subgroups.

Conclusion: The combination of clear aligners with a segmental lingual appliance provided superior control in managing severe rotations of rounded teeth, resulting in greater predictability compared with aligners alone.

Purpose: Lateral photographs are routinely evaluated as part of orthodontic diagnostics and treatment planning. Anthropometric measurements based on soft tissue landmarks are taken to evaluate facial features and attractiveness. The detection of these landmarks is a process performed by specialists and prone to intra- and inter-annotator variability. The aims of this investigation were (1) to train an artificial intelligence (AI) algorithm to automatically detect landmarks on lateral photographs, (2) to establish a high-quality gold standard dataset to evaluate landmark detection accuracy, and (3) to compare the performance of AI with that of clinical experts.

Methods: The AI algorithm was trained on a dataset of 991 photographs, with three clinical experts annotating 14 soft tissue landmarks on each photograph. Eleven experts annotated a separate dataset of 56 photographs, to establish the gold standard. Metric scaling of the photographs was achieved by transferring scaling from corresponding lateral cephalograms. Based on the detected landmarks, 11 anthropometric measurements were taken, and the performance of the experts and AI was compared against the gold standard by comparing errors from the ground truth using Mann-Whitney U tests.

Results: At a 2.0 mm threshold, the AI model achieved successful detection rates exceeding 95% for 12 of 14 landmarks. Compared with individual expert annotations, AI predictions showed reduced variability and lower mean radial errors for landmarks with high inter-annotator disagreement. Anthropometric measurements derived from AI predictions demonstrated smaller absolute errors than expert-based measurements.

Conclusion: This study demonstrates that AI-based landmark detection on lateral photographs can achieve accuracy comparable to expert annotations, demonstrating greater consistency for those landmarks exhibiting high inter-annotator variability.

Purpose: This prospective cohort study aimed to evaluate whether salivary leptin hormone levels correlate with skeletal and dental responses to functional therapy in normal-weight versus overweight skeletal class II malocclusion patients.

Methods: Thirty-two patients with skeletal Class II division 1 malocclusion were divided into two groups: overweight and normal weight. All patients were treated with twin block appliances; their growth stage being about MP3cap. Lateral cephalometric x‑rays and body mass index (BMI) percentiles were assessed before (T0) and at the end of the functional treatment (T1). Unstimulated saliva samples were collected from all patients at four time points (before, in the first month, in the third month and at the end of the functional treatment) using a noninvasive passive pouring method. Leptin levels were measured photometrically using an enzyme-linked immunosorbent assay (ELISA) kit.

Results: Overweight patients exhibited higher salivary leptin levels and earlier skeletal maturation. Overjet correction was achieved in these patients with more incisor movement and they showed greater mandibular length increase. Correlation analysis revealed positive associations between leptin and the change of maxillary parameters as well as of soft tissue convexity. Negative correlations were observed between salivary leptin levels and changes in posterior facial height (PFH), PFH/AFH ratio (AFH: anterior facial height), and lower incisor inclination.

Conclusion: Body weight significantly affected treatment timing and outcomes in Class II correction and should be considered in adolescent treatment planning.

Purpose: The aim of this study was to develop an artificial intelligence (AI) model capable of quantifying crowding and spacing in the upper arch and to validate its accuracy by comparing the model's results with those of manual interactive digital space analysis.

Materials and methods: This study included intraoral photographs and occlusal scans of the upper dental arch of orthodontic patients treated at the University of Sharjah (2022-2024). The YOLO (You Only Look Once) 8 Pose Model was generated using a training and validation dataset (832 images). The AI model performed tooth segmentation and tooth point detection on the occlusal images, followed by automated quantification of tooth size-arch length discrepancy (TSALD). Manual space analysis was conducted using OrthoCAD (Cadent, Fairview, NJ, USA) software and the data were compared with the results of the AI model using a testing dataset (300 images). TSALD was categorized based on the index of treatment complexity, outcome, and need (ICON). Qualitative data were presented as frequency and distribution, and comparisons were performed by using Fisher's exact test. Correlation between manual and AI-measured TSALD was evaluated using Pearson's correlation coefficient.

Results: The model achieved an overall accuracy of 90%. The largest discrepancies were found in cases presenting mild crowding (< 2 mm, 7%), severe spacing (5.1-9 mm, 5%), and moderate spacing (2.1-5 mm, 3.3%). A strong correlation (> 0.92) between manual and AI TSALD measurements indicated high reliability and potential interchangeability.

Conclusion: The AI model was successfully developed and validated, achieving 90% accuracy, demonstrating its potential as a reliable tool for quantifying TSALD in orthodontic diagnostics.