Objective: To compare the short-term treatment effects of face mask therapy with miniplates (FM-MP) and face mask therapy with rapid maxillary expansion appliance (FM-RME) in growing Class III malocclusion patients with maxillary hypoplasia. Materials and Methods: Twenty patients were allocated into two groups according to the anchorage device: FM-MP group (n 5 10; mean age 5 11.2 6 1.2 years; miniplates in the zygomatic buttress area) and FM-RME group (n 5 10; mean age 5 10.7 6 1.3 years; bonded or banded RME). The face mask was applied for 12 to 14 hours/day in both groups with a force of 400 g/side directed 30u downward and forward from the occlusal plane. Lateral cephalograms were taken before (T1) and after FM-MP or FM-RME therapy (T2). Skeletodental and soft-tissue variables were measured. Paired and independent t-tests were performed for statistical analysis. Results: Both groups exhibited significant forward movement of point A and posterior repositioning and opening rotation of the mandible from T2 to T1. The FM-MP group showed significant protraction of orbitale (DSNO), and the FM-RME group showed a decrease in overbite and an increase in Bjö rk sum. Comparing the amount of changes between the two groups, the FM-MP group displayed greater forward movement of the maxilla than the FM-RME group (DSNA, DA to N perp, all P , .05). However, the FM-RME group exhibited a greater opening rotation of the mandible (DSNB, Bjö rk sum, all P , .01; DPog to N-perp, P , .05) and labioversion of the maxillary incisors (DU1-FH, P , .05). Conclusion: FM-MP therapy induces a greater advancement of the maxilla, less posterior repositioning and opening rotation of the mandible, and less proclination of the maxillary incisors than FM-RME therapy. (Angle Orthod. 2012;82:846-852.)

Objective: To determine the difference in the effects of facemask with miniplate (FM-MP) anchorage on maxillary protraction in growing cleft patients between unilateral (UCLP) and bilateral cleft lip and palate (BCLP) groups. Materials and Methods: The samples consisted of a UCLP group (N 5 15, 13 boys and 2 girls; mean age 10.98 years; mean protraction duration 2.37 years) and a BCLP group (N 5 15, all boys; mean age 11.42 years; mean protraction duration 2.36 years), who were treated with the same surgical technique (rotation and advancement flap and double opposing Z-plasty) by one surgeon and with FM-MP by one orthodontist. Lateral cephalograms were taken before (T1) and after FM-MP (T2). Fourteen skeletal and dental variables were measured. Independent and paired t-tests were performed for statistical analysis. Results: There were no differences in mean age and values of variables at the T1 stage and in the duration of protraction between the two groups. The BCLP group showed less advancement of point A than the UCLP group (DA-vertical reference plane, 2.51 mm vs 4.06 mm, P , .05; DA-N perpendicular, 0.79 mm vs 2.26 mm, P , .05; DSNA, 0.45u vs 2.85u, P , .01). Since counterclockwise rotation of the palatal plane in two groups was minimal (20.36u vs 20.87u), no difference was observed with regard to clockwise rotation of the mandible (0.46u vs 20.07u). There were no differences in the degree of labioversion of the maxillary incisor (8.16u vs 7.10u), linguoversion of the mandibular incisor (22.66u vs 22.14u), and increase in overjet (5.39 mm vs 5.70 mm) between the two groups. Conclusion: In FM-MP therapy of growing cleft patients under the conditions of this study, the UCLP group shows a more favorable change in maxillary advancement than the BCLP group. (Angle Orthod. 2012;82:935-941.)

Objective: To evaluate whether mandibular setback surgery (MSS) for Class III patients would produce gradients of three-dimensional (3D) soft tissue changes in the vertical and transverse aspects. Materials and Methods: The samples consisted of 26 Class III patients treated with MSS using bilateral sagittal split ramus osteotomy. Lateral cephalograms and 3D facial scan images were taken before and 6 months after MSS, and changes in landmarks and variables were measured using a Rapidform 2006. Paired and independent t-tests were performed for statistical analysis. Results: Landmarks in the upper lip and mouth corner (cheilion, Ch) moved backward and downward (respectively, cupid bow point, 1.0 mm and 0.3 mm, P , .001 and P , .01; alar curvature-Ch midpoint, 0.6 mm and 0.3 mm, both P , .001; Ch, 3.4 mm and 0.8 mm, both P , .001). However, landmarks in stomion (Stm), lower lip, and chin moved backward (Stm, 1.6 mm; labrale inferius [Li], 6.9 mm; LLBP, 6.9 mm; B9, 6.7 mm; Pog9, 6.7 mm; Me9, 6.6 mm; P , .001, respectively). Width and height of upper and lower lip were not altered significantly except for a decrease of lower vermilion height (Stm-Li, 1.7 mm, P , .001). Chin height (B9-Me9) was decreased because of backward and upward movement of Me9 (3.1 mm, P , .001). Although upper lip projection angle and Stm-transverse projection angle became acute (Ch Rt -Ls-Ch Lt , 5.7u; Ch Rt -Stm-Ch Lt , 6.4u, both P , .001) because of the greater backward movement of Ch than Stm, lower lip projection angle and Stm-vertical projection angle became obtuse (Ch Rt -Li-Ch Lt , 10.8u; LsStm-Li, 23.5u, both P , .001) because of the larger backward movement of Li than labrale superius (Ls). Conclusions: Three-dimensional soft tissue changes in Class III patients after MSS exhibited increased gradients from upper lip and lower lip to chin as well as from Stm to Ch. (Angle Orthod. 2010;80:896-903.)

Diagnosis and treatment planning are the most important steps in the orthognathic surgery for the successful treatment. The purpose of this study was to develop a new artificial intelligent model for surgery/non-surgery decision and extraction determination, and to evaluate the performance of this model. The sample used in this study consisted of 316 patients in total. Of the total sample, 160 were planned with surgical treatment and 156 were planned with non-surgical treatment. The input values of artificial neural network were obtained from 12 measurement values of the lateral cephalogram and 6 additional indexes. The artificial intelligent model of machine learning consisted of 2-layer neural network with one hidden layer. The learning was carried out in 3 stages, and 4 best performing models were adopted. Using these models, decision-making success rates of surgery/non-surgery, surgery type, and extraction/non-extraction were calculated. The final diagnosis success rate was calculated by comparing the actual diagnosis with the diagnosis obtained by the artificial intelligent model. The success rate of the model showed 96% for the diagnosis of surgery/non-surgery decision, and showed 91% for the detailed diagnosis of surgery type and extraction decision. This study suggests the artificial intelligent model using neural network machine learning could be applied for the diagnosis of orthognathic surgery cases.

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Individual tooth segmentation from cone beam computed tomography (CBCT) images is an essential prerequisite for an anatomical understanding of orthodontic structures in several applications, such as tooth reformation planning and implant guide simulations. However, the presence of severe metal artifacts in CBCT images hinders the accurate segmentation of each individual tooth. In this study, we propose a neural network for pixel-wise labeling to exploit an instance segmentation framework that is robust to metal artifacts. Our method comprises of three steps: 1) image cropping and realignment by pose regressions, 2) metal-robust individual tooth detection, and 3) segmentation. We first extract the alignment information of the patient by pose regression neural networks to attain a volume-of-interest (VOI) region and realign the input image, which reduces the interoverlapping area between tooth bounding boxes. Then, individual tooth regions are localized within a VOI realigned image using a convolutional detector. We improved the accuracy of the detector by employing non-maximum suppression and multiclass classification metrics in the region proposal network. Finally, we apply a convolutional neural network (CNN) to perform individual tooth segmentation by converting the pixel-wise labeling task to a distance regression task. Metal-intensive image augmentation is also employed for a robust segmentation of metal artifacts. The result shows that our proposed method outperforms other state-of-the-art methods, especially for teeth with metal artifacts. Our method demonstrated 5.68% and 30.30% better accuracy in the F1 score and aggregated Jaccard index, respectively, when compared to the best performing state-of-the-art algorithms. The primary significance of the proposed method is two-fold: 1) an introduction of pose-aware VOI realignment followed by a robust tooth detection and 2) a metal-robust CNN framework for accurate tooth segmentation.Index Terms-Cone beam computed tomography image segmentation, pose-aware tooth detection, pose regression neural network, tooth instance segmentation.

Objective: To investigate which hard and soft tissue factors relate with the amount of buccal corridor area (BCA) during posed smiling. Materials and Methods: The samples consisted of 92 adult patients (19 men and 73 women; 56 four first bicuspids extraction and 36 nonextraction treatment cases; mean age ϭ 23.5 years), who were treated only with a fixed appliance and finished with Angle Class I canine and molar relationships. To eliminate the crowding effect on the buccal corridor area, lateral cephalograms, dental casts, and standardized frontal posed smile photographs were obtained at debonding stage and 28 variables were measured. Pearson correlation analysis, multiple linear regression analysis, and independent t-test were used to find variables that were related with buccal corridor area ratio (BCAR). Results: Among the lateral cephalometric and dental cast variables, FMA, lower anterior facial height, upper incisor (U1) exposure, U1 to facial plane, lower incisor (L1) to mandibular plane, L1 to N-B, Sn (subnasale) to soft tissue menton (MeЈ), Sn to stomodium superius (stms), stms to MeЈ, and interpremolar width were significantly negatively correlated with BCAR. Occlusal plane inclination and buccal corridor linear ratio did not show any significant correlation with BCAR. Multiple linear regression analysis generated a three-variable model: Sn to MeЈ, U1 exposure, and sum of tooth material (STM) (R 2 ϭ 0.324). There was no significant difference in BCAR between extraction and nonextraction groups. Conclusions: To control the amount of BCA for achieving a better esthetic smile, it is necessary to observe the vertical pattern of the face, amount of upper incisor exposure, and sum of the tooth material.

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