Objective: To investigate which types of environmental exposure during pregnancy are risk and protective factors for cleft lip with or without cleft palate (CL/P). Methods: This case-control study included 278 orthodontic patients with CL/P (CL/P group) and 51 without CL/P (non-CL/P group). Demographic and environmental exposure data were collected using questionnaires completed by the parents. Statistical analyses were performed to identify the potential risk and protective factors for CL/P. Results The two groups did not show significant difference in (1) body weight at birth and number of previous births;(2) fathers’ ages at birth and occupation; (3) parents’ chronic diseases, alcohol consumption, and exposure to harmful substances; and (4) mothers’ smoking, secondhand smoking, and vitamin and calcium intake. Most patients with CL/ P were born at normal term (≥ 37 weeks, 93.2%) with normal body weight (2.9–3.7 kg, 63.7%) and as either the first or second child (90.3%). In the CL/ P group, the percentages of mothers who were very young or old (≤ 19 years, ≥ 40 years) and with physical labor in their occupation were low (1.8% and 2.2%, respectively). Compared with the non-CL/P group, the CL/P group showed a lower percentage of maternal folic acid intake (68.6% vs. 20.9%, odds ratio [OR] = 0.121; P < 0.001) and higher percentages of mothers’ drug intake and fathers’ smoking habits (3.9% vs. 16.2%, OR = 4.73, P < 0.05; 39.2% vs. 61.2%, OR = 2.44, P < 0.01). Conclusions: The findings of this study may explain the association between environmental factors and CL/P risk.

Objective: To investigate which types of environmental exposure during pregnancy are risk and protective factors for cleft lip with or without cleft palate (CL/P). Methods: This case-control study included 278 orthodontic patients with CL/P (CL/P group) and 51 without CL/P (non-CL/P group). Demographic and environmental exposure data were collected using questionnaires completed by the parents. Statistical analyses were performed to identify the potential risk and protective factors for CL/P. Results The two groups did not show significant difference in (1) body weight at birth and number of previous births;(2) fathers’ ages at birth and occupation; (3) parents’ chronic diseases, alcohol consumption, and exposure to harmful substances; and (4) mothers’ smoking, secondhand smoking, and vitamin and calcium intake. Most patients with CL/ P were born at normal term (≥ 37 weeks, 93.2%) with normal body weight (2.9–3.7 kg, 63.7%) and as either the first or second child (90.3%). In the CL/ P group, the percentages of mothers who were very young or old (≤ 19 years, ≥ 40 years) and with physical labor in their occupation were low (1.8% and 2.2%, respectively). Compared with the non-CL/P group, the CL/P group showed a lower percentage of maternal folic acid intake (68.6% vs. 20.9%, odds ratio [OR] = 0.121; P < 0.001) and higher percentages of mothers’ drug intake and fathers’ smoking habits (3.9% vs. 16.2%, OR = 4.73, P < 0.05; 39.2% vs. 61.2%, OR = 2.44, P < 0.01). Conclusions: The findings of this study may explain the association between environmental factors and CL/P risk.

Objective: To investigate which types of environmental exposure during pregnancy are risk and protective factors for cleft lip with or without cleft palate (CL/P). Methods: This case-control study included 278 orthodontic patients with CL/P (CL/P group) and 51 without CL/P (non-CL/P group). Demographic and environmental exposure data were collected using questionnaires completed by the parents. Statistical analyses were performed to identify the potential risk and protective factors for CL/P. Results The two groups did not show significant difference in (1) body weight at birth and number of previous births;(2) fathers’ ages at birth and occupation; (3) parents’ chronic diseases, alcohol consumption, and exposure to harmful substances; and (4) mothers’ smoking, secondhand smoking, and vitamin and calcium intake. Most patients with CL/ P were born at normal term (≥ 37 weeks, 93.2%) with normal body weight (2.9–3.7 kg, 63.7%) and as either the first or second child (90.3%). In the CL/ P group, the percentages of mothers who were very young or old (≤ 19 years, ≥ 40 years) and with physical labor in their occupation were low (1.8% and 2.2%, respectively). Compared with the non-CL/P group, the CL/P group showed a lower percentage of maternal folic acid intake (68.6% vs. 20.9%, odds ratio [OR] = 0.121; P < 0.001) and higher percentages of mothers’ drug intake and fathers’ smoking habits (3.9% vs. 16.2%, OR = 4.73, P < 0.05; 39.2% vs. 61.2%, OR = 2.44, P < 0.01). Conclusions: The findings of this study may explain the association between environmental factors and CL/P risk.

Objective: To investigate which types of environmental exposure during pregnancy are risk and protective factors for cleft lip with or without cleft palate (CL/P). Methods: This case-control study included 278 orthodontic patients with CL/P (CL/P group) and 51 without CL/P (non-CL/P group). Demographic and environmental exposure data were collected using questionnaires completed by the parents. Statistical analyses were performed to identify the potential risk and protective factors for CL/P. Results The two groups did not show significant difference in (1) body weight at birth and number of previous births;(2) fathers’ ages at birth and occupation; (3) parents’ chronic diseases, alcohol consumption, and exposure to harmful substances; and (4) mothers’ smoking, secondhand smoking, and vitamin and calcium intake. Most patients with CL/ P were born at normal term (≥ 37 weeks, 93.2%) with normal body weight (2.9–3.7 kg, 63.7%) and as either the first or second child (90.3%). In the CL/ P group, the percentages of mothers who were very young or old (≤ 19 years, ≥ 40 years) and with physical labor in their occupation were low (1.8% and 2.2%, respectively). Compared with the non-CL/P group, the CL/P group showed a lower percentage of maternal folic acid intake (68.6% vs. 20.9%, odds ratio [OR] = 0.121; P < 0.001) and higher percentages of mothers’ drug intake and fathers’ smoking habits (3.9% vs. 16.2%, OR = 4.73, P < 0.05; 39.2% vs. 61.2%, OR = 2.44, P < 0.01). Conclusions: The findings of this study may explain the association between environmental factors and CL/P risk.

Objective: To investigate which types of environmental exposure during pregnancy are risk and protective factors for cleft lip with or without cleft palate (CL/P). Methods: This case-control study included 278 orthodontic patients with CL/P (CL/P group) and 51 without CL/P (non-CL/P group). Demographic and environmental exposure data were collected using questionnaires completed by the parents. Statistical analyses were performed to identify the potential risk and protective factors for CL/P. Results The two groups did not show significant difference in (1) body weight at birth and number of previous births;(2) fathers’ ages at birth and occupation; (3) parents’ chronic diseases, alcohol consumption, and exposure to harmful substances; and (4) mothers’ smoking, secondhand smoking, and vitamin and calcium intake. Most patients with CL/ P were born at normal term (≥ 37 weeks, 93.2%) with normal body weight (2.9–3.7 kg, 63.7%) and as either the first or second child (90.3%). In the CL/ P group, the percentages of mothers who were very young or old (≤ 19 years, ≥ 40 years) and with physical labor in their occupation were low (1.8% and 2.2%, respectively). Compared with the non-CL/P group, the CL/P group showed a lower percentage of maternal folic acid intake (68.6% vs. 20.9%, odds ratio [OR] = 0.121; P < 0.001) and higher percentages of mothers’ drug intake and fathers’ smoking habits (3.9% vs. 16.2%, OR = 4.73, P < 0.05; 39.2% vs. 61.2%, OR = 2.44, P < 0.01). Conclusions: The findings of this study may explain the association between environmental factors and CL/P risk.

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 ANSmid, 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.

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.

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.

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 twojaw 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 timepoints (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.

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.