Changes in masseter muscle morphology after surgical-orthodontic treatment in patients with skeletal Class III malocclusion with mandibular asymmetry: The automatic masseter muscle segmentation model.

INTRODUCTION: This study evaluated the masseter muscle changes after surgical-orthodontic treatment in patients with a skeletal Class III malocclusion using automatic segmentation. METHODS: Images of 120 patient with skeletal Class III malocclusion were obtained and reconstructed at T0 (pretreatment), T1 (presurgery), and T2 (6-12-month postsurgery). The patients were divided into symmetrical and asymmetrical groups. The volume, major axis length, maximum cross-sectional area, horizontal cross-sectional area 5 mm above the mandibular foramen (CSAF), and orientation were calculated automatically. RESULTS: In the asymmetrical group, the volume and major axis length on the deviated side were lower than on the nondeviated side at T0, T1, and T2 (P <0.05). There were no significant differences in maximum cross-sectional area and CSAF bilaterally. The orientation was coronally more vertical and sagittally more forward on the deviated side (both P <0.001). In the symmetrical group, there were no significant bilateral differences at T0, T1, and T2. The volume, major axis length, and CSAF decreased, and the coronal orientation was more vertical on the nondeviated side at T2 than at T0 in both groups (P <0.05). The coronal plane orientation was more inclined on the deviated side at T2 than at T0 in the asymmetrical group (P <0.05). CONCLUSIONS: The smaller volume on the deviated side at T2 indicates the need for myofunctional training after surgery. The masseter muscle volume and the cross-sectional area did not recover to the preorthodontic levels. Studies with longer follow-up durations are needed to confirm these findings.

Early and 1-year postsurgical stability and its factors in patients with complicated skeletal Class â ¢ malocclusion treated by conventional and surgery-first approach: A prospective cohort study.

INTRODUCTION: This study aimed to compare postsurgical stability between conventional (CSA) and surgery-first (SFA) approaches and investigate its prognostic factors in patients with a skeletal Class Ⅲ extraction. METHODS: Twenty and 19 patients treated with LeFort I osteotomy and bilateral sagittal split ramus osteotomy (BSSRO) with premolar extraction were enrolled in SFA and CSA groups, respectively. Serial cone-beam computed tomography images obtained before surgery, immediately after surgery (T1), 3 months after surgery, and 12 months after surgery were used for 3-dimensional quantitative analysis. The condyle was segmented for analyzing volumetric changes. Repeated measures analysis of variance, independent t test, and chi-square test were used to compare time-course and intergroup differences. Pearson and Kendall correlation and multivariate linear regression analyses were used to explore prognostic factors affecting skeletal stability. RESULTS: In both CSA and SFA, postsurgical relapse mainly occurred in the mandible sagittal and vertical dimensions and during the first 3 months after surgery. Stability in SFA was significantly less than that in CSA. Intraoperatively, inferolateral condylar displacement with proximal segment inwards, clockwise rotation, and return movements after surgery were observed regardless of the treatment approach. The condylar volume remained stable over time. Multivariate regression analysis showed that posterior vertical dimension (VD) at T1 (-1.63 mm), surgical amount of mandibular setback (-10.33 mm), surgical condylar downwards displacement (-1.28 mm), and anterior overjet at T1 (6.43 mm) were the most important predictors of early mandibular relapse (r2 = 0.593). CONCLUSIONS: The risk of early relapse could be reduced by controlling the anterior, middle, and posterior constraints provided by the prediction model.

Automatic Masseter Muscle Accurate Segmentation from CBCT Using Deep Learning-Based Model.

Segmentation of the masseter muscle (MM) on cone-beam computed tomography (CBCT) is challenging due to the lack of sufficient soft-tissue contrast. Moreover, manual segmentation is laborious and time-consuming. The purpose of this study was to propose a deep learning-based automatic approach to accurately segment the MM from CBCT under the refinement of high-quality paired computed tomography (CT). Fifty independent CBCT and 42 clinically hard-to-obtain paired CBCT and CT were manually annotated by two observers. A 3D U-shape network was carefully designed to segment the MM effectively. Manual annotations on CT were set as the ground truth. Additionally, an extra five CT and five CBCT auto-segmentation results were revised by one oral and maxillofacial anatomy expert to evaluate their clinical suitability. CBCT auto-segmentation results were comparable to the CT counterparts and significantly improved the similarity with the ground truth compared with manual annotations on CBCT. The automatic approach was more than 332 times shorter than that of a human operation. Only 0.52% of the manual revision fraction was required. This automatic model could simultaneously and accurately segment the MM structures on CBCT and CT, which can improve clinical efficiency and efficacy, and provide critical information for personalized treatment and long-term follow-up.

On the open problem related to rank equalities for the sum of finitely many idempotent matrices and its applications.

Tian and Styan have shown many rank equalities for the sum of two and three idempotent matrices and pointed out that rank equalities for the sum P1 + ⋯+P k with P1,…, P k be idempotent (k > 3) are still open. In this paper, by using block Gaussian elimination, we obtained rank equalities for the sum of finitely many idempotent matrices and then solved the open problem mentioned above. Extensions to scalar-potent matrices and some related matrices are also included.