Facial Asymmetry Phenotypes in Adult Patients With Unilateral Cleft Lip and Palate and Skeletal Class III Malocclusion Using Principal Component Analysis and Cluster Analysis.

The purpose of this study was to classify and characterize facial asymmetry (FA) phenotypes in adult patients with unilateral cleft lip and palate (UCLP) and skeletal class III malocclusion. The samples comprised 52 adult UCLP patients (36 men and 16 women; mean age, 22.43 y) who had undergone orthognathic surgery for correction of class III malocclusion. After measurement of 22 cephalometric parameters in posteroanterior cephalograms taken 1 month before orthognathic surgery, principal component analysis was performed to obtain 5 representative parameters [deviation (mm) of ANS (ANS-dev), maxillary central incisor contact point (Mx1-dev), and menton (Me-dev); cant (degree) of the maxillary anterior occlusal plane (MxAntOP-cant) and mandibular border (MnBorder-cant)]. K-means cluster analysis was conducted using these representative parameters. The differences in cephalometric parameters among the clusters were statistically analyzed. The FA phenotypes were classified into 4 types: No-cant-and-No-deviation type (cluster-4, n=16, 30.8%); MxMn-cant-MxMn-dev to the cleft-side type (cluster-3, n=4, 7.7%); Mx-cant-Mn-shift to the cleft-side type (cluster-2, n=15, 28.8%); and Mn-cant-Mn-dev to the noncleft-side type (cluster-1, n=17, 32.7%). Asymmetry in the maxilla and/or mandible were observed in 70% of patients. One third of patients (cluster-2 and cluster-3; sum, 36.5%) exhibited significant cant of MxAntOP induced by cleft and cant or shift of the mandible to the cleft side. Another one third of patients (cluster-1, 32.7%) demonstrated significant deviation and cant of the mandible to the noncleft-side despite cleft in the maxilla. This FA phenotype classification might be a basic guideline for diagnosis and treatment planning for UCLP patients.

Minimum required length of orthodontic microimplant: a numerical simulation and clinical validation.

INTRODUCTION: This study aimed to determine the minimum required length of microimplants (MIs) to prevent excessive micromotion during MI healing that can lead to MI failure. METHODS: Hypothesizing that the implantation depth of MI in cancellous bone (IDcancel) is the key to the control of micromotion during MI healing, we numerically investigated the minimum IDcancel required to maintain MI micromotion to below the threshold (30 µm) that would threaten MI survival. Twenty MI and bone models were built using MIs of 4 lengths and bone specimens with 5 different cortical bone thicknesses to create IDcancel in the 0.5-5.5 mm. Then, applying a horizontal force of 1.5 N on the MI head, we calculated the micromotion (peak and average MI micromotions) and determined the minimum IDcancel. A clinical test was performed to verify the numerical result by placing 160 MIs in the posterior maxilla and mandible. RESULTS: A strong correlation (r2= 0.694) was found to exist between IDcancel and MI micromotion. A minimum of 2.5 mm of IDcancel was needed to maintain the level of MI micromotion (peak micromotion) <30 µm threshold. The 6-month survival rate of MI was strongly correlated with IDcancel (r2= 0.744) and decreased sharply when IDcancel was ≤2 mm. CONCLUSIONS: The minimum lengths of MIs to provide the minimum IDcancel of 2.5 mm required to promote successful MI healing in the posterior maxilla and mandible are 5.2 and 6.5 mm, respectively.

Correlation between insertion torque and peri-implant bone strain during placement of orthodontic mini-implants: A finite element study.

INTRODUCTION: Insertion torque is the amount of torque exerted on the implant to tighten into the bone. We investigated whether insertion torque values could be correlated with the strain level in the peri-implant cortical bone resulting from mini-implant insertion. METHODS: The insertion of a standard size mini-implant (φ 1.4 mm × 7 mm) into maxillary alveolar bone was simulated using the finite element method. A total of 3600 calculation steps were employed to numerically reproduce the mini-implant insertion process and analyze the insertion torque and strain distribution in bone. Special attention was given to the relationship between insertion torque values and strain level in the cortical bone at the final tightening. The strain level was quantified using the following 3 strain parameters: (1) average insertion strain, (2) peak insertion strain recorded near the mini-implant thread tips, and (3) the size of the damage zone in the cortical bone. Correlations between the insertion torque values and these 3 parameters were analyzed using linear regression. RESULTS: Direct proportionality and strong correlation were found between the insertion torque values and each of the 3 strain parameters: average insertion strain (r2 = 0.91), peak insertion strain (r2 = 0.91), and the size of damage zone (r2 = 0.90) in the peri-implant cortical bone. CONCLUSIONS: The results of this finite element method study demonstrated that insertion torque could serve as a reliable indicator of the strain level in the peri-implant cortical bone resulting from mini-implant insertion.

Accuracy of auto-identification of the posteroanterior cephalometric landmarks using cascade convolution neural network algorithm and cephalometric images of different quality from nationwide multiple centers.

INTRODUCTION: The purpose of this study was to evaluate the accuracy of auto-identification of the posteroanterior (PA) cephalometric landmarks using the cascade convolution neural network (CNN) algorithm and PA cephalogram images of a different quality from nationwide multiple centers nationwide. METHODS: Of the 2798 PA cephalograms from 9 university hospitals, 2418 images (2075 training set and 343 validation set) were used to train the CNN algorithm for auto-identification of 16 PA cephalometric landmarks. Subsequently, 99 pretreatment images from the remaining 380 test set images were used to evaluate the accuracy of auto-identification of the CNN algorithm by comparing with the identification by a human examiner (gold standard) using V-Ceph 8.0 (Ostem, Seoul, South Korea). Pretreatment images were used to eliminate the effects of orthodontic bracket, tube and wire, surgical plate, and surgical screws. Paired t test was performed to compare the x- and y-coordinates of each landmark. The point-to-point error and the successful detection rate (range, within 2.0 mm) were calculated. RESULTS: The number of landmarks without a significant difference between the location identified by the human examiner and by auto-identification by the CNN algorithm were 8 on the x-coordinate and 5 on the y-coordinate, respectively. The mean point-to-point error was 1.52 mm. The low point-to-point error (<1.0 mm) was observed at the left and right antegonion (0.96 mm and 0.99 mm, respectively) and the high point-to-point error (>2.0 mm) was observed at the maxillary right first molar root apex (2.18 mm). The mean successful detection rate of auto-identification was 83.3%. CONCLUSIONS: Cascade CNN algorithm for auto-identification of PA cephalometric landmarks showed a possibility of an effective alternative to manual identification.

Vertical bony step between proximal and distal segments after mandibular setback is related with relapse: A cone-beam computed tomographic study.

INTRODUCTION: Vertical bony step (VBS) occurs between proximal and distal segments of the mandible during mandibular setback surgery with bilateral sagittal split ramus osteotomy. The purpose of this study was to investigate whether VBS is correlated with the relapse of mandibular setback using 3-dimensional models constructed from cone-beam computed tomography. METHODS: The subjects consisted of 30 patients who underwent bilateral sagittal split ramus osteotomy for a mandibular setback. Double jaw surgery was performed in 18 patients, and isolated mandibular setback surgery was performed in 12 patients. Cone-beam computed tomography scans were taken at pretreatment (T0), postsurgery (T1), and posttreatment (T2). Treatment changes and the correlations between measurements were evaluated. RESULTS: The mean mandibular setback was -11.9 mm, and the mean VBS was -5.6 mm. Correlations with the relapse of mandibular setback were found in the amount of mandibular setback (T1 - T0), development of VBS (T1 - T0), posterior movement of the proximal segment (T1 - T0), counterclockwise rotation of symphysis (T2 - T1), and the resolution of VBS (T2 - T1). CONCLUSIONS: The development and resolution of VBS were correlated with the relapse of mandibular setback. Minimizing VBS is recommended to reduce the relapse of mandibular setback.

Accuracy of automated identification of lateral cephalometric landmarks using cascade convolutional neural networks on lateral cephalograms from nationwide multi-centres.

OBJECTIVE: To investigate the accuracy of automated identification of cephalometric landmarks using the cascade convolutional neural networks (CNN) on lateral cephalograms acquired from nationwide multi-centres. SETTINGS AND SAMPLE POPULATION: A total of 3150 lateral cephalograms were acquired from 10 university hospitals in South Korea for training. MATERIALS AND METHODS: We evaluated the accuracy of the developed model with independent 100 lateral cephalograms as an external validation. Two orthodontists independently identified the anatomic landmarks of the test data set using the V-ceph software (version 8.0, Osstem, Seoul, Korea). The mean positions of the landmarks identified by two orthodontists were regarded as the gold standard. The performance of the CNN model was evaluated by calculating the mean absolute distance between the gold standard and the automatically detected positions. Factors associated with the detection accuracy for landmarks were analysed using the linear regression models. RESULTS: The mean inter-examiner difference was 1.31 ± 1.13 mm. The overall automated detection error was 1.36 ± 0.98 mm. The mean detection error for each landmark ranged between 0.46 ± 0.37 mm (maxillary incisor crown tip) and 2.09 ± 1.91 mm (distal root tip of the mandibular first molar). A significant difference in the detection accuracy among cephalograms was noted according to hospital (P = .011), sensor type (P < .01), and cephalography machine model (P < .01). CONCLUSION: The automated cephalometric landmark detection model may aid in preliminary screening for patient diagnosis and mid-treatment assessment, independent of the type of the radiography machines tested.

Analysis of dental compensation in patients with facial asymmetry using cone-beam computed tomography.

INTRODUCTION: The purpose of this research was to evaluate dental compensation in facial asymmetry and its correlation with skeletal variables using cone-beam computed tomography. METHODS: Sixty adult patients were retrospectively divided into asymmetry (mean age, 21.8 ± 5.4 years) and symmetry groups (mean age, 28.1 ± 4.1 years); both groups comprised 30 patients. Independent and paired t tests were used for comparisons between the asymmetry and symmetry groups and between deviated (Dv) and nondeviated (NDv) sides of the asymmetry group, respectively. Pearson correlation between dental and skeletal variables was performed. RESULTS: The mean value of menton deviation was 9.4 mm in the asymmetry group. Compared with the symmetry group, the direction and amount of dental compensation of the asymmetry group were as follows: 2.5-mm extrusion of the maxillary first molar (UM6) at NDv (P <0.05); 1.8-mm higher position of the mandibular canine (LC) from the mandibular horizontal plane using mental foramen (MHP_mf) at NDv (P <0.05); 6°-more buccoversion of UM6 at Dv; 3.7°-more linguoversion of UM6 at NDv; 4.8°-more buccoversion of the maxillary canine (UC) at Dv; 4.9°-more buccoversion of the mandibular molar (LM6) at NDv; and 2.6°-more linguoversion of LC at Dv. Dental compensation correlated or marginally correlated with skeletal variables of the deviated mandible. CONCLUSIONS: Dental compensations, extrusion of the maxillary molars on the NDv, and buccal tipping of the maxillary teeth and lingual tipping of the mandibular teeth on the Dv, were observed. The mandibular body length was associated with linguoversion of the mandibular molars on the Dv. The ramal inclination was related to the extrusion of the maxillary molars on the NDv.

Differences in the Alignment Pattern of the Maxillary Dental Arch Following Fixed Orthodontic Treatment in Patients With Bilateral Cleft Lip and Palate: Anteroposterior-Collapsed Arch Versus Transverse-Collapsed Arch.

The purpose of this study was to investigate differences in the alignment pattern of the collapsed maxillary arch following fixed orthodontic treatment (FOT) in bilateral cleft lip and palate (BCLP) patients according to collapse type. Fifteen BCLP patients were divided into Group 1 (anteroposterior-collapsed arch, n = 7) and Group 2 (transverse-collapsed arch, n = 8) according to maxillary arch shape before FOT. Linear and angular variables of lateral cephalograms and dental models were evaluated before (T1) and after FOT (T2), and statistical analysis was performed. In cephalometric measurements at T1, both the groups exhibited retrusive maxillae, a Class III relationship, and lingual inclination of U1-SN. At T2, significant improvement of U1-SN was observed in both the groups (P < 0.01 in Group 1; P < 0.05 in Group 2). In the model measurements at T1, the inter-second premolar width (IP2W), inter-first molar width (IM1W), and left segmental angle were smaller in Group 2 than in Group 1 (IP2W, P < 0.001; IM1W, P < 0.05; LSA, P < 0.05). From T1 to T2, the arch width increased significantly in Group 2 (inter-canine width, IP1W, IP2W, and IM1W; all P < 0.05), but not in Group 1. Therefore, there were significant differences in ΔIP2W (2.4 mm versus 14.9 mm, P < 0.01) and ΔIM1W (-0.7 mm versus 5.9 mm, P < 0.001) between Groups 1 and 2. In both the groups, the molar depth and right and left segmental angles increased significantly, while the premaxillary rotation angle decreased significantly (molar depth, RSA, LSA, and PMRA; all P < 0.05). The alignment strategy for the maxillary premolar and molar areas should be modified according to arch shape in BCLP patients.

Accuracy of posteroanterior cephalogram landmarks and measurements identification using a cascaded convolutional neural network algorithm: A multicenter study.

Objective: : To quantify the effects of midline-related landmark identification on midline deviation measurements in posteroanterior (PA) cephalograms using a cascaded convolutional neural network (CNN). Methods: : A total of 2,903 PA cephalogram images obtained from 9 university hospitals were divided into training, internal validation, and test sets (n = 2,150, 376, and 377). As the gold standard, 2 orthodontic professors marked the bilateral landmarks, including the frontozygomatic suture point and latero-orbitale (LO), and the midline landmarks, including the crista galli, anterior nasal spine (ANS), upper dental midpoint (UDM), lower dental midpoint (LDM), and menton (Me). For the test, Examiner-1 and Examiner-2 (3-year and 1-year orthodontic residents) and the Cascaded-CNN models marked the landmarks. After point-to-point errors of landmark identification, the successful detection rate (SDR) and distance and direction of the midline landmark deviation from the midsagittal line (ANS-mid, UDM-mid, LDM-mid, and Me-mid) were measured, and statistical analysis was performed. Results: : The cascaded-CNN algorithm showed a clinically acceptable level of point-to-point error (1.26 mm vs. 1.57 mm in Examiner-1 and 1.75 mm in Examiner-2). The average SDR within the 2 mm range was 83.2%, with high accuracy at the LO (right, 96.9%; left, 97.1%), and UDM (96.9%). The absolute measurement errors were less than 1 mm for ANS-mid, UDM-mid, and LDM-mid compared with the gold standard. Conclusions: : The cascaded-CNN model may be considered an effective tool for the auto-identification of midline landmarks and quantification of midline deviation in PA cephalograms of adult patients, regardless of variations in the image acquisition method.

A Deep Learning-Based Automatic Collateral Assessment in Patients with Acute Ischemic Stroke.

This study aimed to develop a supervised deep learning (DL) model for grading collateral status from dynamic susceptibility contrast magnetic resonance perfusion (DSC-MRP) images from patients with large vessel occlusion (LVO) acute ischemic stroke (AIS) and compare its performance against experts' manual grading. Among consecutive LVO-AIS at three medical center sites, DSC-MRP data were processed to generate collateral flow maps consisting of arterial, capillary, and venous phases. With the use of expert readings as a reference, a DL model was developed to analyze collateral status with output classified into good and poor grades. The resulting model was externally validated in a later-collected population from one medical center site. The model was trained on 255 patients and externally validated on 72 patients. In the all-site internal validation population, DL grading of good collateral probability yielded a c statistic of 0.91; in the external validation population, the c statistic was 0.85. In the external validation population, there was moderate agreement between the experts' grades and DL grades (kappa = 0.53, 95% CI = 0.32-0.73, p < 0.0001). Day 7 infarct growth volume was higher in DL-graded poor collateral group than good collateral group patients (median volume [26 mL vs. 6 mL], p = 0.01) in patients with successful reperfusion (modified treatment in cerebral infarction (mTICI) = 2b-3). In all patients with a 90-day modified Rankin Scale (mRS) score, there was a shift to more favorable outcomes in the good collateral group, with a common odds ratio of 2.99 (95% CI = 1.89-4.76, p < 0.0001). The DL-based collateral grading was in good agreement with expert manual grading in both development and validation populations. After exclusion of patients with large infarct volume, early reperfusion is more likely to benefit patients with the poor collateral flow, and the DL method has the potential to aid the assessment of collateral status.

The prediction of sagittal chin point relapse following two-jaw surgery using machine learning.

The study aimed to identify critical factors associated with the surgical stability of pogonion (Pog) by applying machine learning (ML) to predict relapse following two-jaw orthognathic surgery (2 J-OGJ). The sample set comprised 227 patients (110 males and 117 females, 207 training and 20 test sets). Using lateral cephalograms taken at the initial evaluation (T0), pretreatment (T1), after (T2) 2 J-OGS, and post treatment (T3), 55 linear and angular skeletal and dental surgical movements (T2-T1) were measured. Six ML modes were utilized, including classification and regression trees (CART), conditional inference tree (CTREE), and random forest (RF). The training samples were classified into three groups; highly significant (HS) (≥ 4), significant (S) (≥ 2 and < 4), and insignificant (N), depending on Pog relapse. RF indicated that the most important variable that affected relapse rank prediction was ramus inclination (RI), CTREE and CART revealed that a clockwise rotation of more than 3.7 and 1.8 degrees of RI was a risk factor for HS and S groups, respectively. RF, CTREE, and CART were practical tools for predicting surgical stability. More than 1.8 degrees of CW rotation of the ramus during surgery would lead to significant Pog relapse.

Orthognathic surgical planning using graph CNN with dual embedding module: External validations with multi-hospital datasets.

BACKGROUND AND OBJECTIVE: Despite recent development of AI, prediction of the surgical movement in the maxilla and mandible by OGS might be more difficult than that of tooth movement by orthodontic treatment. To evaluate the prediction accuracy of the surgical movement using pairs of pre-(T0) and post-surgical (T1) lateral cephalograms (lat-ceph) of orthognathic surgery (OGS) patients and dual embedding module-graph convolution neural network (DEM-GCNN) model. METHODS: 599 pairs from 3 institutions were used as training, internal validation, and internal test sets and 201 pairs from other 6 institutions were used as external test set. DEM-GCNN model (IEM, learning the lat-ceph images; LTEM, learning the landmarks) was developed to predict the amount and direction of surgical movement of ANS and PNS in the maxilla and B-point and Md1crown in the mandible. The distance between T1 landmark coordinates actually moved by OGS (ground truth) and predicted by DEM-GCNN model and pre-existed CNN-based Model-C (learning the lat-ceph images) was compared. RESULTS: In both internal and external tests, DEM-GCNN did not exhibit significant difference from ground truth in all landmarks (ANS, PNS, B-point, Md1crown, all P > 0.05). When the accumulated successful detection rate for each landmark was compared, DEM-GCNN showed higher values than Model-C in both the internal and external tests. In violin plots exhibiting the error distribution of the prediction results, both internal and external tests showed that DEM-GCNN had significant performance improvement in PNS, ANS, B-point, Md1crown than Model-C. DEM-GCNN showed significantly lower prediction error values than Model-C (one-jaw surgery, B-point, Md1crown, all P < 0.005; two-jaw surgery, PNS, ANS, all P < 0.05; B point, Md1crown, all P < 0.005). CONCLUSION: We developed a robust OGS planning model with maximized generalizability despite diverse qualities of lat-cephs from 9 institutions.

Alveolar bone thickness and height changes following incisor retraction treatment with microimplants.

OBJECTIVES: To evaluate alveolar bone remodeling following incisor retraction treatment with microimplants and to examine the relationship between crown/root distal movement and thickness/height changes of the alveolus. MATERIALS AND METHODS: A total of 24 patients (mean age, 19.29 ± 4.64 years) with bialveolar protrusion treated by incisor retraction with microimplants were included. The distances of the crown and root tip movements as well as the thickness (alveolar bone thickness [ABT]; labial, lingual, and total) and vertical level (vertical bone level [VBL]; labial and lingual) of the alveolar bone were assessed using cone-beam computed tomography images obtained before treatment (T1) and after treatment (T2). All T1 and T2 variables were compared, and further comparisons of alveolar bone changes were conducted between the two groups based on the distance of the crown (low-crown-movement and high-crown-movement groups) and root movements (low-root-movement and high-root-movement groups). To determine the correlation of the crown or root movement with the variables of alveolar bone changes, Pearson correlation coefficients were calculated. RESULTS: Significant differences were found in all VBL and ABT variables after treatment in both jaws but not in total ABT. Based on the crown and root movements, alveolar bone change significantly differed between the root-movement groups, whereas there was no significant difference between the crown-movement groups. In addition, root movement showed significant correlations with the variables. CONCLUSIONS: Remarkable changes in the height and thickness of alveolar bone were found after microimplant-aided incisor retraction treatment in all groups except for total ABT. Root movement was significantly correlated with the alveolar bone changes.

Assessment of pharyngeal airway in Korean adolescents according to skeletal pattern, sex, and cervical vertebral maturation: A cross-sectional CBCT study.

Objective: To investigate airway volumes using cone-beam computed tomography (CBCT) by skeletal patterns, sex, and cervical vertebral maturation (CVM) stages in Korean adolescents. Methods: The sample consisted of pretreatment CBCT and cephalograms of 95 adolescents (aged 12-19) obtained out of 1,611 patients examined for orthodontic treatment from 2018 to 2020 in Kyungpook National University Dental Hospital. The samples were classified into two sex groups; three skeletal pattern groups, four chronological age groups and four CVM stages. Nasopharyngeal volumes (NPV), oropharyngeal volumes (OPV), total pharyngeal airway volume (TAV) and minimum cross-sectional area (MCA) measurements were taken from the CBCT. Multiple linear regression analyses to find out which one of the independent variables are good predictors for airway variables. Significant factors were analyzed by two-way multivariate analysis of variance (MANOVA) then multiple comparisons were analyzed using a t-test, and Fisher least significant difference. Results: Age, sex, CVM, and Sella-Nasion-B point have significant influence on airway variable. Males and females showed similar patterns of change in chronological age groups 1-3; however, males had larger NPV, OPV, and MCA at CVM in group 4. According to CVM stages, males had larger OPV, TAV, and MCA at CVM stage 6 (p-value: 0.019, 0.021, 0.015, respectively) and no sex differences at CVM stages 3, 4, and 5. Conclusions: Skeletal patterns have an effect on airway volume. Sex differences were found in CVM 6.

Biomechanical Efficacy and Effectiveness of Orthodontic Treatment with Transparent Aligners in Mild Crowding Dentition-A Finite Element Analysis.

Orthodontic treatment increasingly involves transparent aligners; however, biomechanical analysis of their treatment effects under clinical conditions is lacking. We compared the biomechanical efficacy and effectiveness of orthodontic treatment with transparent aligners and of fixed appliances in simulated clinical orthodontic treatment conditions using orthodontic finite element (FE) models. In the FE analysis, we used Model Activation/De-Activation analysis to validate our method. Fixed appliances and 0.75-mm and 0.5-mm thick transparent aligners were applied to a tooth-alveolar bone FE model with lingually-inclined and axially-rotated central incisors. Compared to the fixed appliance, the 0.75-mm and 0.5-mm transparent aligners induced 5%, 38%, and 28% and 21%, 62%, and 34% less movement of the central incisors and principal stress of the periodontal ligament and of the alveolar bone, respectively, for lingual inclination correction. For axial-rotation correction, these aligners induced 22%, 37%, and 40% and 28%, 67%, and 48% less tooth movement and principal stress of the periodontal ligament and of the alveolar bone, respectively. In conclusion, transparent aligners induced less tooth movement, it is sufficient for orthodontic treatment, but 0.5-mm aligners should be used for only mild corrections. Additionally, the Model Activation/De-Activation analysis method is suitable for FE analysis of orthodontic treatment reflecting clinical treatment conditions.

Accuracy of artificial intelligence-assisted landmark identification in serial lateral cephalograms of Class III patients who underwent orthodontic treatment and two-jaw orthognathic surgery.

Objective: To investigate the pattern of accuracy change in artificial intelligence-assisted landmark identification (LI) using a convolutional neural network (CNN) algorithm in serial lateral cephalograms (Lat-cephs) of Class III (C-III) patients who underwent two-jaw orthognathic surgery. Methods: A total of 3,188 Lat-cephs of C-III patients were allocated into the training and validation sets (3,004 Lat-cephs of 751 patients) and test set (184 Lat-cephs of 46 patients; subdivided into the genioplasty and non-genioplasty groups, n = 23 per group) for LI. Each C-III patient in the test set had four Lat-cephs: initial (T0), pre-surgery (T1, presence of orthodontic brackets [OBs]), post-surgery (T2, presence of OBs and surgical plates and screws [S-PS]), and debonding (T3, presence of S-PS and fixed retainers [FR]). After mean errors of 20 landmarks between human gold standard and the CNN model were calculated, statistical analysis was performed. Results: The total mean error was 1.17 mm without significant difference among the four time-points (T0, 1.20 mm; T1, 1.14 mm; T2, 1.18 mm; T3, 1.15 mm). In comparison of two time-points ([T0, T1] vs. [T2, T3]), ANS, A point, and B point showed an increase in error (p < 0.01, 0.05, 0.01, respectively), while Mx6D and Md6D showeda decrease in error (all p < 0.01). No difference in errors existed at B point, Pogonion, Menton, Md1C, and Md1R between the genioplasty and non-genioplasty groups. Conclusions: The CNN model can be used for LI in serial Lat-cephs despite the presence of OB, S-PS, FR, genioplasty, and bone remodeling.

Comparison of one-jaw and two-jaw orthognathic surgery in patients with skeletal Class III malocclusion using data from 10 multi-centers in Korea: Part I. Demographic and skeletodental characteristics.

OBJECTIVE: To investigate demographic and skeletodental characteristics of one-jaw (1J-OGS) and two-jaw orthognathic surgery (2J-OGS) in patients with skeletal Class III malocclusion. METHODS: 750 skeletal Class III patients who underwent OGS at 10 university hospitals in Korea between 2015 and 2019 were investigated; after dividing them into the 1J-OGS (n = 186) and 2J-OGS groups (n = 564), demographic and skeletodental characteristics were statistically analyzed. RESULTS: 2J-OGS was more frequently performed than 1J-OGS (75.2 vs. 24.8%), despite regional differences (capital area vs. provinces, 86.6 vs. 30.7%, p < 0.001). Males outnumbered females, and their mean operation age was older in both groups. Regarding dental patterns, the most frequent maxillary arch length discrepancy (ALD) was crowding in the 1J-OGS group (52.7%, p < 0.001) and spacing in the 2J-OGS group (40.4%, p < 0.001). However, the distribution of skeletal pattern was not significantly different between the two groups (all p > 0.05). The most prevalent skeletal patterns in both groups were hyper-divergent pattern (50.0 and 54.4%, respectively) and left-side chin point deviation (both 49.5%). Maxillary spacing (odds ratio [OR], 3.645; p < 0.001) increased the probability of 2J-OGS, while maxillary crowding (OR, 0.672; p < 0.05) and normo-divergent pattern (OR, 0.615; p < 0.05) decreased the probability of 2J-OGS. CONCLUSIONS: In both groups, males outnumbered females, and their mean operation age was older. The most frequent ALD was crowding in the 1J-OGS group, and spacing in the 2J-OGS group, while skeletal characteristics were not significantly different between the two groups.

Accuracy of one-step automated orthodontic diagnosis model using a convolutional neural network and lateral cephalogram images with different qualities obtained from nationwide multi-hospitals.

OBJECTIVE: The purpose of this study was to investigate the accuracy of one-step automated orthodontic diagnosis of skeletodental discrepancies using a convolutional neural network (CNN) and lateral cephalogram images with different qualities from nationwide multi-hospitals. METHODS: Among 2,174 lateral cephalograms, 1,993 cephalograms from two hospitals were used for training and internal test sets and 181 cephalograms from eight other hospitals were used for an external test set. They were divided into three classification groups according to anteroposterior skeletal discrepancies (Class I, II, and III), vertical skeletal discrepancies (normodivergent, hypodivergent, and hyperdivergent patterns), and vertical dental discrepancies (normal overbite, deep bite, and open bite) as a gold standard. Pre-trained DenseNet-169 was used as a CNN classifier model. Diagnostic performance was evaluated by receiver operating characteristic (ROC) analysis, t-stochastic neighbor embedding (t-SNE), and gradientweighted class activation mapping (Grad-CAM). RESULTS: In the ROC analysis, the mean area under the curve and the mean accuracy of all classifications were high with both internal and external test sets (all, > 0.89 and > 0.80). In the t-SNE analysis, our model succeeded in creating good separation between three classification groups. Grad-CAM figures showed differences in the location and size of the focus areas between three classification groups in each diagnosis. CONCLUSIONS: Since the accuracy of our model was validated with both internal and external test sets, it shows the possible usefulness of a one-step automated orthodontic diagnosis tool using a CNN model. However, it still needs technical improvement in terms of classifying vertical dental discrepancies.

Comparative Analysis of Stress in the Periodontal Ligament and Center of Rotation in the Tooth after Orthodontic Treatment Depending on Clear Aligner Thickness-Finite Element Analysis Study.

Lately, in orthodontic treatments, the use of transparent aligners for the correction of malocclusions has become prominent owing to their intrinsic advantages such as esthetics, comfort, and minimal maintenance. Attempts at improving upon this technology by varying various parameters to investigate the effects on treatments have been carried out by several researchers. Here, we aimed to investigate the biomechanical and clinical effects of aligner thickness on stress distributions in the periodontal ligament and changes in the tooth's center of rotation. Dental finite element models comprising the cortical and cancellous bones, gingiva, teeth, and nonlinear viscoelastic periodontal ligaments were constructed, validated, and used together with aligner finite element models of different aligner thicknesses to achieve the goal of this study. The finite element analyses were conducted to simulate the actual orthodontic aligner treatment process for the correction of malocclusions by generating pre-stresses in the aligner and allowing the aligner stresses to relax to induce tooth movement. The results of the analyses showed that orthodontic treatment in lingual inclination and axial rotation with a 0.75 mm-thick aligner resulted in 6% and 0.03% higher principal stresses in the periodontal ligament than the same treatment using a 0.05 mm-thick aligner, respectively. Again, for both aligner thicknesses, the tooth's center of rotation moved lingually and towards the root direction in lingual inclination, and diagonally from the long axis of the tooth in axial rotation. Taken together, orthodontic treatment for simple malocclusions using transparent aligners of different thicknesses will produce a similar effect on the principal stresses in the periodontal ligament and similar changes in the tooth's center of rotation, as well as sufficient tooth movement. These findings provide orthodontists and researchers clinical and biomechanical evidence about the effect of transparent aligner thickness selection and its effect on orthodontic treatment.