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.

Three-Dimensional Quantitative Assessment of Condylar Displacement and Adaptive Remodeling in Asymmetrical Mandibular Prognathism Patients After Sagittal Split Ramus Osteotomy.

This study was performed to evaluate the condylar displacement and associated condylar remodeling in class III patients following mandibular setback surgery via sagittal split ramus osteotomy (SSRO). The sample comprised of 26 condyles of 13 subjects (mean age of 21.2±2.6 y). We evaluated patients with mandibular prognathism and facial asymmetry who had undergone SSRO for mandibular setback at Korea University Hospital between January 2016 and December 2018. Three-dimensional segmentation of the mandibular condyles was done using the initial cone-beam computed tomography scan and scan taken 12 months postoperatively or later. Quantitative assessments of the 3-dimensional condylar displacement from T0 to T1 and bony remodeling of 8 regions of the condylar head were performed. The correlation between the condylar displacement and condylar head remodeling on the deviated (D) and nondeviated (ND) sides was analyzed. Significant correlations between condylar displacement and surface remodeling were observed in both D and ND condyles. The anteroposterior condylar displacement was significantly different between the D and ND sides (P=0.007). There was no significant difference in condylar remodeling between the 2 sides. Condylar displacement and adaptive remodeling after SSRO varied greatly among individuals. Compared with displacement in the ND condyle, displacement in the D condyle has a greater association with condylar remodeling in both D and ND condyles. There is no significant difference in condylar head remodeling between D and ND condyles.

Three-Dimensional Changes in the Mandibular Proximal Segment After Using a Surgery-First Approach in Patients With Class III Malocclusion and Facial Asymmetry.

ABSTRACT: This study was performed to evaluate condylar position and angulation after asymmetric mandibular setback between a conventional (CA) and surgery-first approach (SFA) using three-dimensional analysis. The condylar positions of 30 patients with skeletal Class III malocclusion and facial asymmetry who underwent 1-jaw (sagittal split ramus osteotomy) or 2-jaw orthognathic surgery (Le Fort I osteotomy and sagittal split ramus osteotomy) with CA (n = 18) or SFA (n = 12) from 2 university hospitals were studied. The three-dimensional assessment of condylar changes was performed using computed tomography images at the initial time point (T0) and at least 6 months after surgery (T1). Segmentation of condyles and cranial base assessment from cone-beam computed tomography images were performed using ITK-SNAP software (version 3.4.0). Condylar position and angulation changes were calculated using 3D Slicer software (version 4.10.2), and statistical analysis was performed. No significant translational or rotational condylar changes were observed between the deviated and non-deviated sides in each group or between the CA and SFA groups except yaw ( p = 0.014). Linear mixed-model analysis and multi-variate analysis showed no significant difference between the CA and SFA groups. Surgery-first approach might not be associated with more harmful effects on the condylar position and angulation changes as compared with CA.

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.

Prediction of hand-wrist maturation stages based on cervical vertebrae images using artificial intelligence.

OBJECTIVE: To predict the hand-wrist maturation stages based on the cervical vertebrae (CV) images, and to analyse the accuracy of the proposed algorithms. SETTINGS AND POPULATION: A total of 499 pairs of hand-wrist radiographs and lateral cephalograms of 455 orthodontic patients aged 6-18 years were used for developing the prediction model for hand-wrist skeletal maturation stages. MATERIALS AND METHODS: The hand-wrist radiographs and the lateral cephalograms were collected from two university hospitals and a paediatric dental clinic. After identifying the 13 anatomic landmarks of the CV, the width-height ratio, width-perpendicular height ratio and concavity ratio of the CV were used as the morphometric features of the CV. Patients' chronological age and sex were also included as input data. The ground truth data were the Fishman SMI based on the hand-wrist radiographs. Three specialists determined the ground truth SMI. An ensemble machine learning methods were used to predict the Fishman SMI. Five-fold cross-validation was performed. The mean absolute error (MAE), round MAE and root mean square error (RMSE) values were used to assess the performance of the final ensemble model. RESULTS: The final ensemble model consisted of eight machine learning models. The MAE, round MAE and RMSE were 0.90, 0.87 and 1.20, respectively. CONCLUSION: Prediction of hand-wrist SMI based on CV images is possible using machine learning methods. Chronological age and sex increased the prediction accuracy. An automated diagnosis of the skeletal maturation may aid as a decision-supporting tool for evaluating the optimal treatment timing for growing patients.

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.

Factors associated with spontaneous angular changes of impacted mandibular third molars as a result of second molar protraction.

INTRODUCTION: This study aimed to identify significant factors affecting the spontaneous angular changes of impacted mandibular third molars as a result of second molar protraction. Temporary skeletal anchorage devices in the missing mandibular first molar (ML-6) or missing deciduous mandibular second molar (ML-E) with missing succedaneous premolar spaces provided traction. METHODS: Forty-one mandibular third molars of 34 patients (10 male and 24 female; mean age 18.3 ± 3.7 years) that erupted after second molar protraction were included in this study. They were classified into upright (U) and tilted (T) groups. Linear and angular measurements were performed at the time of treatment initiation (T1) and of ML-6 or ML-E space closure (T2). Regression analyses were used to identify significant factors related to third molar uprighting. RESULTS: Nolla stage (odds ratio [OR] 4.1), sex (OR 0.003 for male), third molar angulation at T1 (OR 1.1), missing tooth space (OR 0.006), rate of third molar eruption (OR 23.3), and rate of second molar protraction (OR 0.2) significantly affected third molar uprighting. Age, third molar angulation at T1, rate of third molar eruption, and rate of second molar protraction were significant factors for predicting third molar angulation at T2. CONCLUSIONS: Available space for third molar eruption before and after second molar protraction is not associated with uprighting of erupting third molars. Older patients whose third molars are in greater Nolla stage, are in a more upright position at T1, and have a greater eruption rate have a greater chance for third molar uprighting. Alternatively, an increase in second molar protraction rate results in mesial tipping of the third molars.

Change in alveolar bone level of mandibular second and third molars after second molar protraction into missing first molar or second premolar space.

OBJECTIVE: To investigate the factors associated with the change in alveolar bone level of mandibular second and third molars after second molar protraction into the space of the missing first molar (L6) or second premolar (LE). METHODS: Fifty-one patients in whom space of the missing L6 or LE was treated with second molar protraction (13 males, 38 females, mean age 19.6 ± 4.7 years) from 2003 to 2015 were included. The alveolar bone level and position and angulation of the mandibular second and third molars were measured in panoramic radiographs at pre-treatment (T1), and after the alignment of the third molars following second molar protraction (T2). Factors associated with alveolar bone loss on the distal aspect of the mandibular second molars were assessed using linear regression analysis. RESULTS: Age at T1 (P < 0.001) and third molar angulation at T1 (P = 0.002) were significant factors for the prediction of alveolar bone level distal to the second molars. LIMITATION: This study used two-dimensional panoramic radiographs, and we could observe only the interproximal bone level. CONCLUSIONS: After second molar protraction into the missing first molar or second premolar space, mandibular second molars may exhibit alveolar bone resorption in the distal root in older patients and in those with mesially tilted third molars before treatment.

Bond Strengths of Orthodontic Metal Brackets to Tribochemically Silica-coated Zirconia Surfaces Using Different 10-Methacryloyloxydecyl Dihydrogen Phosphate-containing Primers.

PURPOSE: To evaluate the effects of tribochemical silica coating and different 10-methacryloyloxydecyl dihydrogen phosphate (MDP)-containing primers on the shear bond strength (SBS) of orthodontic metal brackets to yttrium-stabilized tetragonal zirconia polycrystal (Y-TZP) surfaces. MATERIALS AND METHODS: One hundred ninety polished Y-TZP specimens were randomly assigned to 19 groups (n = 10): 30 specimens were used for surface analyses after polishing with 600-grit silicon carbide paper, airborneparticle abrasion with 50-µm alumina (A), or tribochemical silica coating (CoJet [C]); 160 specimens were used in SBS testing of orthodontic metal brackets to Y-TZP after alumina airborne-particle abrasion or tribochemical silica coating and application of either ESPE-Sil (S) (ASn, ASa, CSn, CSa), Alloy Primer (AP) (AAPn, AAPa, CAPn, CAPa), Clearfil Ceramic Primer (CP) (ACPn, ACPa, CCPn, CCPa), or Scotchbond Universal (U) (AUn, AUa, CUn, CUa) and either stored in water for 24 h (non-aged, n) or thermocycled 5000 times (aged, a). The surface analyses and SBSs were statistically analyzed with ANOVA and Tukey's tests. RESULTS: Both mechanically treated surfaces had significantly greater surface roughness and surface free energy than did the polished surfaces. The type of primer and aging significantly affected the bond strength. Among the thermocycled specimens, the AAPa, AUa, and CCPa groups showed the greatest SBS. CONCLUSION: After alumina airborne-particle abrasion, the application of Alloy Primer, Clearfil Ceramic Primer, or Scotchbond Universal provided stable bonding to Y-TZP ceramics. After tribochemical silica coating, however, only Clearfil Ceramic Primer produced a durable bond to Y-TZP ceramics.

Treatment outcome and long-term stability of orthognathic surgery for facial asymmetry: A systematic review and meta-analysis.

Objective: : This systematic review aimed to provide a comparative analysis of the treatment outcomes, including hard and soft tissues, postoperative stability, temporomandibular disorders (TMD), and quality of life (QoL), in patients with facial asymmetry who underwent orthognathic surgery. Methods: : The primary objective was to address the question, "How do different factors related to surgery affect the outcomes and stability of orthognathic surgery in the correction of facial asymmetry?" A meta-analysis was conducted on the outcome parameters, such as skeletal, dental, and soft tissue symmetry, TMD, QoL, and relapse, using the Hartung-Knapp-Sidik-Jonkman method for random-effects models. Subgroup analyses were conducted considering surgery-related factors such as surgical techniques (one-jaw vs. two-jaw), use of the surgery-first approach, utilization of computer simulation, and analytical methods employed to evaluate asymmetry (2D vs. 3D). Results: : Forty-nine articles met the inclusion criteria. The meta-analysis demonstrated a significant improvement in the symmetry of hard and soft tissues. The subgroup analysis indicated that the treatment outcomes showed significant improvement, regardless of the factors related to surgery. Changes in TMD signs and symptoms varied according to the surgical technique used. Quality of life improved in the facial, oral, and social domains. Skeletal relapse was observed during the follow-up. Conclusions: : Our findings support the positive outcomes of orthognathic surgery in the treatment of facial asymmetry in terms of skeletal and soft tissue improvements, stability, relief of TMD symptoms, and enhancement of QoL. However, most of the included studies showed a low certainty of evidence and high heterogeneity.

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.

Analysis of 3D soft tissue changes after 1- and 2-jaw orthognathic surgery in mandibular prognathism patients.

PURPOSE: Orthognathic surgery has the objective of altering facial balance to achieve esthetic results in patients who have severe disharmony of the jaws. The purpose was to quantify the soft tissue changes after orthognathic surgery, as well as to assess the differences in 3D soft tissue changes in the middle and lower third of the face between the 1- and 2-jaw surgery groups, in mandibular prognathism patients. MATERIALS AND METHODS: We assessed soft tissue changes of patients who have been diagnosed with mandibular prognathism and received either isolated mandibular surgery or bimaxillary surgery. The quantitative surface displacement was assessed by superimposing preoperative and postoperative volumetric images. An observer measured a surface-distance value that is shown as a contour line. Differences between the groups were determined by the Mann-Whitney U test. The Spearman correlation coefficient was used to evaluate a potential correlation between patients' surgical and cephalometric variables and soft tissue changes after orthognathic surgery in each group. RESULTS: There were significant differences in the middle third of the face between the 1- and 2-jaw surgery groups. Soft tissues in the lower third of the face changed in both surgery groups, but not significantly. The correlation patterns were more evident in the lower third of the face. CONCLUSION: The overall soft tissue changes of the midfacial area were more evident in the 2-jaw surgery group. In 2-jaw surgery, significant changes would be expected in the midfacial area, but caution should be exercised in patients who have a wide alar base.

Novel Procedure for Automatic Registration between Cone-Beam Computed Tomography and Intraoral Scan Data Supported with 3D Segmentation.

In contemporary practice, intraoral scans and cone-beam computed tomography (CBCT) are widely adopted techniques for tooth localization and the acquisition of comprehensive three-dimensional models. Despite their utility, each dataset presents inherent merits and limitations, prompting the pursuit of an amalgamated solution for optimization. Thus, this research introduces a novel 3D registration approach aimed at harmonizing these distinct datasets to offer a holistic perspective. In the pre-processing phase, a retrained Mask-RCNN is deployed on both sagittal and panoramic projections to partition upper and lower teeth from the encompassing CBCT raw data. Simultaneously, a chromatic classification model is proposed for segregating gingival tissue from tooth structures in intraoral scan data. Subsequently, the segregated datasets are aligned based on dental crowns, employing the robust RANSAC and ICP algorithms. To assess the proposed methodology's efficacy, the Euclidean distance between corresponding points is statistically evaluated. Additionally, dental experts, including two orthodontists and an experienced general dentist, evaluate the clinical potential by measuring distances between landmarks on tooth surfaces. The computed error in corresponding point distances between intraoral scan data and CBCT data in the automatically registered datasets utilizing the proposed technique is quantified at 0.234 ± 0.019 mm, which is significantly below the 0.3 mm CBCT voxel size. Moreover, the average measurement discrepancy among expert-identified landmarks ranges from 0.368 to 1.079 mm, underscoring the promise of the proposed 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.

Do patients treated with bimaxillary surgery have more stable condylar positions than those who have undergone single-jaw surgery?

PURPOSE: Because condylar positioning after sagittal split ramus osteotomy of the mandible has been known to affect postoperative skeletal stability, accurate positional assessment of the temporomandibular joint after orthognathic surgery is vital to maximize stability of the surgery. The purpose of this study was to evaluate condylar changes after single-jaw and double-jaw surgeries in mandibular prognathism patients by comparing 3-dimensional angular and positional changes of the condylar heads in groups of patients receiving combined maxillary posterior impaction and mandibular setback and those undergoing only mandibular setback surgeries. PATIENTS AND METHODS: We assessed condylar changes of patients who have been diagnosed with mandibular prognathism and underwent either bimaxillary surgery or isolated mandibular surgery at Kangdong Sacred Heart Hospital and SmileFuture Orthodontic Clinic, Seoul, South Korea, from August 2008 to February 2011. Condylar angulation, intercondylar distance, and amount of condylar displacement were examined based on the 3-dimensional reconstructed images. Preoperative and postoperative changes within each group were assessed by paired t test. Differences between the groups were determined by independent t test. RESULTS: A total of 43 skeletal Class III patients were included in this retrospective, multicenter study. After single-jaw surgery, condylar angulations in all dimensions did not change. In contrast, those who received double-jaw surgery showed forward rotation of 1.93° (P = .027) and medial rotation of 1.48° (P = .032) in the sagittal and axial planes, respectively. The mean distances of condylar displacements were 0.28 ± 0.44 mm in the single-jaw group and 0.31 ± 0.51 mm in the double-jaw group, but there was no statistically significant difference. CONCLUSIONS: Condylar angulations are more stable after sagittal split ramus osteotomy of the mandible as an isolated procedure than in combination with the posterior maxillary impaction in treatment of skeletal Class III malocclusion patients. Condylar displacements in both the single-jaw and double-jaw groups are clinically insignificant.

Automated analysis of three-dimensional CBCT images taken in natural head position that combines facial profile processing and multiple deep-learning models.

BACKGROUND AND OBJECTIVES: Analyzing three-dimensional cone beam computed tomography (CBCT) images has become an indispensable procedure for diagnosis and treatment planning of orthodontic patients. Artificial intelligence, especially deep-learning techniques for analyzing image data, shows great potential for medical and dental image analysis and diagnosis. To explore the feasibility of automating measurement of 13 geometric parameters from three-dimensional cone beam computed tomography images taken in natural head position (NHP), this study proposed a smart system that combined a facial profile analysis algorithm with deep-learning models. MATERIALS AND METHODS: Using multiple views extracted from the cone beam computed tomography data of 170 cases as a dataset, our proposed method automatically calculated 13 dental parameters by partitioning, detecting regions of interest, and extracting the facial profile. Subsequently, Mask-RCNN, a trained decentralized convolutional neural network was applied to detect 23 landmarks. All the techniques were integrated into a software application with a graphical user interface designed for user convenience. To demonstrate the system's ability to replace human experts, 30 CBCT data were selected for validation. Two orthodontists and one advanced general dentist located required landmarks by using a commercial dental program. The differences between manual and developed methods were calculated and reported as the errors. RESULTS: The intraclass correlation coefficients (ICCs) and 95% confidence interval (95% CI) for intra-observer reliability were 0.98 (0.97-0.99) for observer 1; 0.95 (0.93-0.97) for observer 2; 0.98 (0.97-0.99) for observer 3 after measuring 13 parameters two times at two weeks interval. The combined ICC for intra-observer reliability was 0.97. The ICCs and 95% CI for inter-observer reliability were 0.94 (0.91-0.97). The mean absolute value of deviation was around 1 mm for the length parameters, and smaller than 2° for angle parameters. Furthermore, ANOVA test demonstrated the consistency between the measurements of the proposed method and those of human experts statistically (Fdis=2.68, ɑ=0.05). CONCLUSIONS: The proposed system demonstrated the high consistency with the manual measurements of human experts and its applicability. This method aimed to help human experts save time and efforts for analyzing three-dimensional CBCT images of orthodontic patients.

A new rationale for preservation of the mandibular third molar in orthognathic patients with missing molars.

Controversies exist regarding the need for prophylactic extraction of mandibular third molars in patients who plan to undergo orthognathic surgery. An 18-year-old male patient was diagnosed with mandibular prognathism and maxillary retrognathism with mild facial asymmetry. He had a severely damaged mandibular first molar and a horizontally impacted third molar. After extraction of the first molar, the second molar was protracted into the first molar space, and the third molar erupted into the posterior line of occlusion. The orthognathic surgery involved clockwise rotation of the maxillomandibular complex as well as angle shaving and chin border trimming. Patients who are missing or have damaged mandibular molars should be monitored for eruption of third molars to replace the missing posterior tooth regardless of the timing of orthognathic surgery.