Accuracy and clinical validity of automated cephalometric analysis using convolutional neural networks.

BACKGROUND: This study aimed to assess the error range of cephalometric measurements based on the landmarks detected using cascaded CNNs and determine how horizontal and vertical positional errors of individual landmarks affect lateral cephalometric measurements. METHODS: In total, 120 lateral cephalograms were obtained consecutively from patients (mean age, 32.5 ± 11.6) who visited the Asan Medical Center, Seoul, Korea, for orthodontic treatment between 2019 and 2021. An automated lateral cephalometric analysis model previously developed from a nationwide multi-centre database was used to digitize the lateral cephalograms. The horizontal and vertical landmark position error attributable to the AI model was defined as the distance between the landmark identified by the human and that identified by the AI model on the x- and y-axes. The differences between the cephalometric measurements based on the landmarks identified by the AI model vs those identified by the human examiner were assessed. The association between the lateral cephalometric measurements and the positioning errors in the landmarks comprising the cephalometric measurement was assessed. RESULTS: The mean difference in the angular and linear measurements based on AI vs human landmark localization was .99 ± 1.05°, and .80 ± .82 mm, respectively. Significant differences between the measurements derived from AI-based and human localization were observed for all cephalometric variables except SNA, pog-Nperp, facial angle, SN-GoGn, FMA, Bjork sum, U1-SN, U1-FH, IMPA, L1-NB (angular) and interincisal angle. CONCLUSIONS: The errors in landmark positions, especially those that define reference planes, may significantly affect cephalometric measurements. The possibility of errors generated by automated lateral cephalometric analysis systems should be considered when using such systems for orthodontic diagnoses.

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.

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.

Fully automated condyle segmentation using 3D convolutional neural networks.

The aim of this study was to develop an auto-segmentation algorithm for mandibular condyle using the 3D U-Net and perform a stress test to determine the optimal dataset size for achieving clinically acceptable accuracy. 234 cone-beam computed tomography images of mandibular condyles were acquired from 117 subjects from two institutions, which were manually segmented to generate the ground truth. Semantic segmentation was performed using basic 3D U-Net and a cascaded 3D U-Net. A stress test was performed using different sets of condylar images as the training, validation, and test datasets. Relative accuracy was evaluated using dice similarity coefficients (DSCs) and Hausdorff distance (HD). In the five stages, the DSC ranged 0.886-0.922 and 0.912-0.932 for basic 3D U-Net and cascaded 3D U-Net, respectively; the HD ranged 2.557-3.099 and 2.452-2.600 for basic 3D U-Net and cascaded 3D U-Net, respectively. Stage V (largest data from two institutions) exhibited the highest DSC of 0.922 ± 0.021 and 0.932 ± 0.023 for basic 3D U-Net and cascaded 3D U-Net, respectively. Stage IV (200 samples from two institutions) had a lower performance than stage III (162 samples from one institution). Our results show that fully automated segmentation of mandibular condyles is possible using 3D U-Net algorithms, and the segmentation accuracy increases as training data increases.

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.

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 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.

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 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.

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.

Projected lifetime cancer risk from cone-beam computed tomography for orthodontic treatment.

OBJECTIVE: To estimate the projected cancer risk attributable to diagnostic cone-beam computed tomography (CBCT) performed under different exposure settings for orthodontic purposes in children and adults. METHODS: We collected a list of CBCT machines and their specifications from 38 orthodontists. Organ doses were estimated using median and maximum exposure settings of 105 kVp/156.8 mAs and 130 kVp/200 mAs, respectively. The projected cancer risk attributable to CBCT procedures performed 1-3 times within 2 years was calculated for children (aged 5 and 10 years) and adult (aged 20, 30, and 40 years) male and female patients. RESULTS: For maximum exposure settings, the mean lifetime fractional ratio (LFR) was 14.28% for children and 0.91% for adults; this indicated that the risk to children was 16 times the risk to adults. For median exposure settings, the mean LFR was 5.25% and 0.58% for children and adults, respectively. The risk of cancer decreased with increasing age. For both median and maximum exposure settings, females showed a higher risk of cancer than did males in all age groups. Cancer risk increased with an increase in the frequency of CBCT procedures within a given period. CONCLUSIONS: The projected dental CBCT-associated cancer risk spans over a wide range depending on the machine parameters and image acquisition settings. Children and female patients are at a higher risk of developing cancer associated with diagnostic CBCT. Therefore, the use of diagnostic CBCT should be justified, and protective measures should be taken to minimize the harmful biological effects of radiation.

Nonsurgical correction of a Class III skeletal anterior open-bite malocclusion using multiple microscrew implants and digital profile prediction.

A 20-year-old woman had a severe anterior skeletal open bite and a moderate skeletal Class III relationship with a prognathic mandible and a straight profile. She declined surgery. However, molar intrusion in a Class III patient with a straight profile can cause forward mandibular rotation and deterioration of the profile to a concave pattern. We used digital facial profile prediction software to determine whether the orthodontic compensation treatment would be acceptable to the patient. The final treatment plan consisted of extraction of the third molars, maxillary molar intrusion, and total distalization of the mandibular dentition with multiple microscrew implants. The patient cooperated with the use of Class III interarch elastics. The active treatment period was 20 months. Proper overbite and overjet, good occlusion, and an acceptable facial profile were achieved.

Finite-element analysis of the center of resistance of the mandibular dentition.

OBJECTIVE: The aim of this study was to investigate the three-dimensional (3D) position of the center of resistance of 4 mandibular anterior teeth, 6 mandibular anterior teeth, and the complete mandibular dentition by using 3D finite-element analysis. METHODS: Finite-element models included the complete mandibular dentition, periodontal ligament, and alveolar bone. The crowns of teeth in each group were fixed with buccal and lingual arch wires and lingual splint wires to minimize individual tooth movement and to evenly disperse the forces onto the teeth. Each group of teeth was subdivided into 0.5-mm intervals horizontally and vertically, and a force of 200 g was applied on each group. The center of resistance was defined as the point where the applied force induced parallel movement. RESULTS: The center of resistance of the 4 mandibular anterior teeth group was 13.0 mm apical and 6.0 mm posterior, that of the 6 mandibular anterior teeth group was 13.5 mm apical and 8.5 mm posterior, and that of the complete mandibular dentition group was 13.5 mm apical and 25.0 mm posterior to the incisal edge of the mandibular central incisors. CONCLUSIONS: Finite-element analysis was useful in determining the 3D position of the center of resistance of the 4 mandibular anterior teeth group, 6 mandibular anterior teeth group, and complete mandibular dentition group.

The effects of alveolar bone loss and miniscrew position on initial tooth displacement during intrusion of the maxillary anterior teeth: Finite element analysis.

OBJECTIVE: The aim of this study was to determine the optimal loading conditions for pure intrusion of the six maxillary anterior teeth with miniscrews according to alveolar bone loss. METHODS: A three-dimensional finite element model was created for a segment of the six anterior teeth, and the positions of the miniscrews and hooks were varied after setting the alveolar bone loss to 0, 2, or 4 mm. Under 100 g of intrusive force, initial displacement of the individual teeth in three directions and the degree of labial tilting were measured. RESULTS: The degree of labial tilting increased with reduced alveolar bone height under the same load. When a miniscrew was inserted between the two central incisors, the amounts of medial-lateral and anterior-posterior displacement of the central incisor were significantly greater than in the other conditions. When the miniscrews were inserted distally to the canines and an intrusion force was applied distal to the lateral incisors, the degree of labial tilting and the amounts of displacement of the six anterior teeth were the lowest, and the maximum von Mises stress was distributed evenly across all the teeth, regardless of the bone loss. CONCLUSIONS: Initial tooth displacement similar to pure intrusion of the six maxillary anterior teeth was induced when miniscrews were inserted distal to the maxillary canines and an intrusion force was applied distal to the lateral incisors. In this condition, the maximum von Mises stresses were relatively evenly distributed across all the teeth, regardless of the bone loss.

Stress distributions in peri-miniscrew areas from cylindrical and tapered miniscrews inserted at different angles.

OBJECTIVE: The purpose of this study was to analyze stress distributions in the roots, periodontal ligaments (PDLs), and bones around cylindrical and tapered miniscrews inserted at different angles using a finite element analysis. METHODS: We created a three-dimensional (3D) maxilla model of a dentition with extracted first premolars and used 2 types of miniscrews (tapered and cylindrical) with 1.45-mm diameters and 8-mm lengths. The miniscrews were inserted at 30°, 60°, and 90° angles with respect to the bone surface. A simulated horizontal orthodontic force of 2 N was applied to the miniscrew heads. Then, the stress distributions, magnitudes during miniscrew placement, and force applications were analyzed with a 3D finite element analysis. RESULTS: Stresses were primarily absorbed by cortical bone. Moreover, very little stress was transmitted to the roots, PDLs, and cancellous bone. During cylindrical miniscrew insertion, the maximum von Mises stress increased as insertion angle decreased. Tapered miniscrews exhibited greater maximum von Mises stress than cylindrical miniscrews. During force application, maximum von Mises stresses increased in both groups as insertion angles decreased. CONCLUSIONS: For both cylindrical and tapered miniscrew designs, placement as perpendicular to the bone surface as possible is recommended to reduce stress in the surrounding bone.

Finite element analysis of maxillary incisor displacement during en-masse retraction according to orthodontic mini-implant position.

OBJECTIVE: Orthodontic mini-implants (OMI) generate various horizontal and vertical force vectors and moments according to their insertion positions. This study aimed to help select ideal biomechanics during maxillary incisor retraction by varying the length in the anterior retraction hook (ARH) and OMI position. METHODS: Two extraction models were constructed to analyze the three-dimentional finite element: a first premolar extraction model (Model 1, M1) and a residual 1-mm space post-extraction model (Model 2, M2). The OMI position was set at a height of 8 mm from the arch wire between the second maxillary premolar and the first molar (low OMI traction) or at a 12-mm height in the mesial second maxillary premolar (high OMI traction). Retraction force vectors of 200 g from the ARH (-1, +1, +3, and +6 mm) at low or high OMI traction were resolved into X-, Y-, and Z-axis components. RESULTS: In M1 (low and high OMI traction) and M2 (low OMI traction), the maxillary incisor tip was extruded, but the apex was intruded, and the occlusal plane was rotated clockwise. Significant intrusion and counter-clockwise rotation in the occlusal plane were observed under high OMI traction and -1 mm ARH in M2. CONCLUSIONS: This study observed orthodontic tooth movement according to the OMI position and ARH height, and M2 under high OMI traction with short ARH showed retraction with maxillary incisor intrusion.

Comparison of tooth displacement between buccal mini-implants and palatal plate anchorage for molar distalization: a finite element study.

The purposes of this study were to mechanically evaluate distalization modalities through the application of skeletal anchorage using finite element analysis. Base models were constructed from commercial teeth models. A finite element model was created and three treatment modalities were modified to make 10 models. Modalities 1 and 2 placed mini-implants in the buccal side, and modality 3 placed a plate on the palatal side. Distalization with the palatal plate in modality 3 showed bodily molar movement and insignificant displacement of the incisors. Placing mini-implants on the buccal side in modalities 1 and 2 caused the first molar to be distally tipped and extruded, while the incisors were labially flared and intruded. Distalization with the palatal plate rather than mini-implants on the buccal side provided bodily molar movement without tipping or extrusion. It is recommended to use our findings as a clinical guide for the application of skeletal anchorage devices for molar distalization.