Korean Journal of Orthodontics最新文献

Objective: The purpose of this study was to compare the differences in dentoskeletal and soft tissue changes following conventional tooth-borne rapid maxillary expansion (RME) between adolescents and adults.

Methods: Dentoskeletal and soft tissue variables of 17 adolescents and 17 adults were analyzed on posteroanterior and lateral cephalograms and frontal photographs at pretreatment (T1) and after conventional RME using tooth-borne expanders (T2). Changes in variables within each group between T1 and T2 were analyzed using Wilcoxon signed-rank test. Mann-Whitney U test was used to determine the differences in the pretreatment age, expansion and post-expansion durations, and dentoskeletal and soft tissue changes after RME between the groups. Spearman's correlation between pretreatment age and transverse dentoskeletal changes in the adolescent group was calculated.

Results: Despite similar amounts of expansion at the crown level in both groups, the adult group underwent less skeletal expansion with less intermolar root expansion after RME than the adolescent group. The skeletal vertical dimension increased significantly in both groups without significant intergroup difference. The anteroposterior position of the maxilla was maintained in both groups, while a greater backward displacement of the mandible was evident in the adult group than that in the adolescent group after RME. The soft tissue alar width increased in both groups without a significant intergroup difference. In the adolescent group, pretreatment age was not significantly correlated with transverse dentoskeletal changes.

Conclusions: Conventional RME may induce similar soft tissue changes but different dentoskeletal changes between adolescents and adults.

Objective: To classify facial asymmetry (FA) phenotypes in adult patients with skeletal Class III (C-III) malocclusion.

Methods: A total of 120 C-III patients who underwent orthognathic surgery (OGS) and whose three-dimensional computed tomography images were taken one month prior to OGS were evaluated. Thirty hard tissue landmarks were identified. After measurement of 22 variables, including cant (°, mm), shift (mm), and yaw (°) of the maxilla, maxillary dentition (Max-dent), mandibular dentition, mandible, and mandibular border (Man-border) and differences in the frontal ramus angle (FRA, °) and ramus height (RH, mm), K-means cluster analysis was conducted using three variables (cant in the Max-dent [mm] and shift [mm] and yaw [°] in the Man-border). Statistical analyses were conducted to characterize the differences in the FA variables among the clusters.

Results: The FA phenotypes were classified into five types: 1) non-asymmetry type (35.8%); 2) maxillary-cant type (14.2%; severe cant of the Max-dent, mild shift of the Man-border); 3) mandibular-shift and yaw type (16.7%; moderate shift and yaw of the Man-border, mild RH-difference); 4) complex type (9.2%; severe cant of the Max-dent, moderate cant, severe shift, and severe yaw of the Man-border, moderate differences in FRA and RH); and 5) maxillary reverse-cant type (24.2%; reverse-cant of the Max-dent). Strategic decompensation by pre-surgical orthodontic treatment and considerations for OGS planning were proposed according to the FA phenotypes.

Conclusions: This FA phenotype classification may be an effective tool for differential diagnosis and surgical planning for Class III patients with FA.

Objective: To develop and explore the usefulness of an artificial intelligence system for the prediction of the need for dental extractions during orthodontic treatments based on gender, model variables, and cephalometric records.

Methods: The gender, model variables, and radiographic records of 214 patients were obtained from an anonymized data bank containing 314 cases treated by two experienced orthodontists. The data were processed using an automated machine learning software (Auto-WEKA) and used to predict the need for extractions.

Results: By generating and comparing several prediction models, an accuracy of 93.9% was achieved for determining whether extraction is required or not based on the model and radiographic data. When only model variables were used, an accuracy of 87.4% was attained, whereas a 72.7% accuracy was achieved if only cephalometric information was used.

Conclusions: The use of an automated machine learning system allows the generation of orthodontic extraction prediction models. The accuracy of the optimal extraction prediction models increases with the combination of model and cephalometric data for the analytical process.

Objective: This study aimed to present and evaluate a new deep learning model for determining cervical vertebral maturation (CVM) degree and growth spurts by analyzing lateral cephalometric radiographs.

Methods: The study sample included 890 cephalograms. The images were classified into six cervical stages independently by two orthodontists. The images were also categorized into three degrees on the basis of the growth spurt: pre-pubertal, growth spurt, and post-pubertal. Subsequently, the samples were fed to a transfer learning model implemented using the Python programming language and PyTorch library. In the last step, the test set of cephalograms was randomly coded and provided to two new orthodontists in order to compare their diagnosis to the artificial intelligence (AI) model's performance using weighted kappa and Cohen's kappa statistical analyses.

Results: The model's validation and test accuracy for the six-class CVM diagnosis were 62.63% and 61.62%, respectively. Moreover, the model's validation and test accuracy for the three-class classification were 75.76% and 82.83%, respectively. Furthermore, substantial agreements were observed between the two orthodontists as well as one of them and the AI model.

Conclusions: The newly developed AI model had reasonable accuracy in detecting the CVM stage and high reliability in detecting the pubertal stage. However, its accuracy was still less than that of human observers. With further improvements in data quality, this model should be able to provide practical assistance to practicing dentists in the future.

Objective: To provide reliable prediction models based on dentoskeletal and soft tissue variables for customizing maxillary incisor positions and to optimize digitalized orthodontic treatment planning.

Methods: This study included 244 Chinese women (age, 18-40 years old) with esthetic profiles after orthodontic treatment with fixed appliances (133 in group I: 1° ≤ The angle between the nasion [N]-A point [A] plane and the N-B point [B] plane [ANB] ≤ 4°; 111 in group II: 4° < ANB ≤ 7°). Dental, skeletal, and soft tissue measurements were performed on lateral cephalograms of the participants. Correlation and multiple linear regression analyses were used to determine the influence of dentoskeletal and soft tissue variables on maxillary incisor position.

Results: The ideal anteroposterior position of the maxillary incisor varied between sagittal skeletal patterns. The position of the maxillary incisor correlated with the sagittal discrepancy between the maxilla and the mandible (ANB), protrusion of the midface, nasal tip projection, development of the chin, and inclination of both the maxillary and mandibular incisors. Distance from the maxillary central incisor to nasion-pogonion plane predicted using multiple linear regression analysis was accurate and could be a practical measurement in orthodontic treatment planning.

Conclusions: Instead of using an average value or norm, orthodontists should customize a patient's ideal maxillary incisor position using dentoskeletal and soft tissue evaluations.

Objective: To evaluate if a remote digital monitoring system added at the end of orthodontic treatment could positively influence the retention phase by reducing the occurrence of misfit of removable appliances, number of emergency appointments (EA), and orthodontic relapse.

Methods: Twenty-seven patients who completed active orthodontic treatment were divided into the study and control groups. In addition to the standard chairside follow-up appointments at month 1 (T1), month 3 (T2), month 6 (T3), the study group patients were monitored using Dental Monitoring^® with monthly intra-oral scans. Occurrence of misfit of removable retainers, number of EAs, and intercanine width change were recorded for both groups. Differences in EAs and retainer fit were assessed using the chi-square test. Intra-group and inter-group differences in the intercanine width were assessed with Friedman test and Mann-Whitney U test, respectively (α = 0.05).

Results: The study group showed a significantly lower occurrence of misfit of removable retainers (p = 0.027) compared to the control group. No significant inter- and intra-group difference was found in the EAs and intercanine width change at each time-point.

Conclusions: Integrating remote monitoring systems, such as Dental Monitoring^®, to the retention phase of the orthodontic treatment may lower the occurrence of misfit of removable retainers. However, a small sample size and a short observation period limit the strength of this evidence. These preliminary results tentatively suggest that remote monitoring technologies may be beneficial, especially during the COVID-19 pandemic, when the regularity of in-office visits might be disrupted.

Objective: To evaluate the trueness and precision of full-arch scans acquired using five intraoral scanners and investigate the factors associated with the dimensional accuracy of the intraoral scan data.

Methods: Nine adult participants (mean age, 34.3 ± 8.3 years) were recruited. Four zirconium spheres (Ø 6 mm) were bonded to the canines and the molars. Following acquisition of reference scans using an industrial-grade scanner, five intraoral scanners, namely i500, CS3600, Trios 3, iTero, and CEREC Omnicam, were used to scan the arches. Linear distances between the four reference spheres were automatically calculated, and linear mixed model analysis was performed to compare the trueness and precision of the intraoral scan data among the different scanners.

Results: The absolute mean trueness and precision values for all intraoral scanners were 76.6 ± 79.3 and 56.6 ± 52.4 μm, respectively. The type of scanner and the measured linear distances had significant effects on the accuracy of the intraoral scan data. With regard to trueness, errors in the intermolar dimension and the distance from the canine to the contralateral molar were greater with Omnicam than with the other scanners. With regard to precision, the error in the linear distance from the canine to the molar in the same quadrant was greater with Omnicam and CS3600 than with the other scanners.

Conclusions: The dimensional accuracy of intraoral scan data may differ significantly according to the type of scanner, with the amount of error in terms of trueness being clinically significant.

Objective: This study aimed to investigate the effect of micro-osteoperforations (MOPs) on external apical root resorption (EARR) during the initial orthodontic alignment phase of maxillary anterior crowding.

Methods: Thirty patients (25 females, 5 males; mean age, 22.66 ± 3.27 years) who presented with moderate crowding of the upper labial segment and underwent extraction-based fixed appliance treatment were recruited. They were randomly allocated to receive adjunctive therapy with MOPs (n = 15) or treatment with fixed appliances only (control group; n = 15). EARR was measured from long-cone periapical radiographs taken at the start and the sixth month of treatment. A correction factor for the enlargement difference was used to calculate EARR. Data were analyzed with descriptive statistics and repeated-measures analysis of variance.

Results: The mean root lengths of 168 teeth were measured and showed no statistically significant difference (p > 0.05) after six months of fixed appliance treatment in the MOP (mean difference [MD] = 0.13 mm; 95% confidence interval [CI] = -0.10-0.35) and control group (MD = 0.14 mm; 95% CI = -0.10-0.37). Most of the roots in the MOP and control groups (42.86% and 52.38%, respectively) showed only mild resorption. Less than 8% of the roots in both groups (7.14% in the MOP group and 4.76% in the control group) showed moderate resorption.

Conclusions: Acceleration of orthodontic tooth movement with adjunctive MOPs therapy during the alignment phase does not exacerbate EARR in patients with moderate crowding of the upper labial segment in comparison with controls.

Objective: The purpose of this study was to evaluate the relationship between rotational disk displacement (DD) of the temporomandibular joint (TMJ) and the dentoskeletal morphology.

Methods: Women aged > 17 years were included in this study. Each subject had a primary complaint of malocclusion and underwent routine cephalometric examinations. They were divided into five groups according to the findings on sagittal and coronal magnetic resonance images of their TMJs: bilateral normal disk position, bilateral anterior DD with reduction (ADDR), bilateral rotational DD with reduction (RDDR), bilateral anterior DD without reduction (ADDNR), and bilateral rotational DD without reduction (RDDNR). Twenty-three cephalometric variables were analyzed, and the Kruskal-Wallis test was used to evaluate differences in the dentoskeletal morphology among the five groups.

Results: Patients with TMJ DD exhibited a hyperdivergent pattern with a retrognathic mandible, unlike those with a normal disk position. These specific skeletal characteristics were more severe in patients exhibiting DD without reduction than in those with reduction, regardless of the presence of rotational DD. Rotational DD significantly influenced horizontal and vertical skeletal patterns only in the stage of DD with reduction, and the mandible exhibited a more backward position and rotation in patients with RDDR than in those with ADDR. However, there were no significant dentoskeletal differences between ADDNR and RDDNR.

Conclusions: The results of this study suggest that rotational DD of TMJ plays an important role in the dentoskeletal morphology, particularly in patients showing DD with reduction.