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Three-Dimensional Evaluation of Skeletal Stability following Surgery-First Orthognathic Approach: Validation of a Simple and Effective Method

Background  The three-dimensional (3D) evaluation of skeletal stability after orthognathic surgery is a time-consuming and complex procedure. The complexity increases further when evaluating the surgery-first orthognathic approach (SFOA). Herein, we propose and validate a simple time-saving method o...

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Detalles Bibliográficos
Autores principales: Mansour, Nabil M., Abdelshaheed, Mohamed E., El-Sabbagh, Ahmed H., El-Din, Ahmed M. Bahaa, Kim, Young Chul, Choi, Jong-Woo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Thieme Medical Publishers, Inc. 2023
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10226798/
https://www.ncbi.nlm.nih.gov/pubmed/37256039
http://dx.doi.org/10.1055/a-2058-8108
Descripción
Sumario:Background  The three-dimensional (3D) evaluation of skeletal stability after orthognathic surgery is a time-consuming and complex procedure. The complexity increases further when evaluating the surgery-first orthognathic approach (SFOA). Herein, we propose and validate a simple time-saving method of 3D analysis using a single software, demonstrating high accuracy and repeatability. Methods  This retrospective cohort study included 12 patients with skeletal class 3 malocclusion who underwent bimaxillary surgery without any presurgical orthodontics. Computed tomography (CT)/cone-beam CT images of each patient were obtained at three different time points (preoperation [T0], immediately postoperation [T1], and 1 year after surgery [T2]) and reconstructed into 3D images. After automatic surface-based alignment of the three models based on the anterior cranial base, five easily located anatomical landmarks were defined to each model. A set of angular and linear measurements were automatically calculated and used to define the amount of movement (T1–T0) and the amount of relapse (T2–T1). To evaluate the reproducibility, two independent observers processed all the cases, One of them repeated the steps after 2 weeks to assess intraobserver variability. Intraclass correlation coefficients (ICCs) were calculated at a 95% confidence interval. Time required for evaluating each case was recorded. Results  Both the intra- and interobserver variability showed high ICC values (more than 0.95) with low measurement variations (mean linear variations: 0.18 mm; mean angular variations: 0.25 degree). Time needed for the evaluation process ranged from 3 to 5 minutes. Conclusion  This approach is time-saving, semiautomatic, and easy to learn and can be used to effectively evaluate stability after SFOA.