Options
The study of posterior airway space changes after orthognathic surgery in skeletal class III patients
Other Title
The study of posterior airway space changes after orthognathic surgery in skeletal class III patients
Type
thesis
Date Issued
2025-05-16
Author(s)
Zay Yar Linn
Advisor
陳立昇 ;鄭信忠
Subjects
系所名稱：牙醫學系碩士班
Publisher
牙醫學系碩士班
Description
學位別：碩士
口試委員：陳立昇; 鄭信忠; 方致元; 吳家佑; 林冠州
關鍵字：Orthognathic surgery、Class III malocclusion、Posterior airway space、Lateral cephalometric、Machine learning
口試委員：陳立昇; 鄭信忠; 方致元; 吳家佑; 林冠州
關鍵字：Orthognathic surgery、Class III malocclusion、Posterior airway space、Lateral cephalometric、Machine learning
Abstract
AIM
The objective was to assess the area and linear changes in the posterior airway space in Class III patients after orthognathic surgery, as well as to evaluate the dental, skeletal, and soft tissue changes following surgery.
MATERIALS & METHODS
This study included 50 patients with Class III malocclusion who underwent mandibular setback orthognathic surgery. Lateral cephalometric radiographs were obtained before treatment (T1) and after treatment (T2). The posterior airway space (PAS) was evaluated by measuring both its area and anterior-posterior linear dimensions. Skeletal, dental, and soft tissue structures were assessed using linear and angular cephalometric measurements. Descriptive statistics and paired t-tests were used to evaluate treatment effects between T1 and T2. The relationships between changes in airway dimensions and craniofacial variables were analyzed using Pearson correlation and Random Forest regression implemented through a machine learning approach in Python.
RESULTS
Most skeletal, dental, soft tissue, landmark, and airway measurements showed significant changes after treatment, except for SNA, maxillary length, maxillary AP position, overbite, chin–throat angle, lower lip to E-plane, and hypopharynx area. Pearson correlation analysis identified significant associations between PAS changes and variables such as SNB, ANB, mandibular length, mandibular AP position, SN-GoGn angle, U1 to A-V, L1 to NB, overjet, Z-angle, posterior facial height, and multiple landmarks (e.g., hyoid, Point A, Point B, Point D, Pog, Me, upper and lower lips). Random Forest regression demonstrated overall low predictive performance, with only a few variables showing positive associations with PAS changes.
CONCLUSION
Significant decreases in posterior airway space were observed following mandibular setback surgery. Both Pearson correlation and Random Forest regression analyses indicated that soft tissue variables had the greatest influence on posterior airway space, followed by skeletal variables, while dental variables demonstrated the least impact. In terms of cephalometric landmark movements, horizontal displacements were found to have a greater effect on airway dimensions than vertical changes.
Keywords: Orthognathic surgery, Class III malocclusion, Posterior Airway Space, Lateral Cephalometric, Machine learning
The objective was to assess the area and linear changes in the posterior airway space in Class III patients after orthognathic surgery, as well as to evaluate the dental, skeletal, and soft tissue changes following surgery.
MATERIALS & METHODS
This study included 50 patients with Class III malocclusion who underwent mandibular setback orthognathic surgery. Lateral cephalometric radiographs were obtained before treatment (T1) and after treatment (T2). The posterior airway space (PAS) was evaluated by measuring both its area and anterior-posterior linear dimensions. Skeletal, dental, and soft tissue structures were assessed using linear and angular cephalometric measurements. Descriptive statistics and paired t-tests were used to evaluate treatment effects between T1 and T2. The relationships between changes in airway dimensions and craniofacial variables were analyzed using Pearson correlation and Random Forest regression implemented through a machine learning approach in Python.
RESULTS
Most skeletal, dental, soft tissue, landmark, and airway measurements showed significant changes after treatment, except for SNA, maxillary length, maxillary AP position, overbite, chin–throat angle, lower lip to E-plane, and hypopharynx area. Pearson correlation analysis identified significant associations between PAS changes and variables such as SNB, ANB, mandibular length, mandibular AP position, SN-GoGn angle, U1 to A-V, L1 to NB, overjet, Z-angle, posterior facial height, and multiple landmarks (e.g., hyoid, Point A, Point B, Point D, Pog, Me, upper and lower lips). Random Forest regression demonstrated overall low predictive performance, with only a few variables showing positive associations with PAS changes.
CONCLUSION
Significant decreases in posterior airway space were observed following mandibular setback surgery. Both Pearson correlation and Random Forest regression analyses indicated that soft tissue variables had the greatest influence on posterior airway space, followed by skeletal variables, while dental variables demonstrated the least impact. In terms of cephalometric landmark movements, horizontal displacements were found to have a greater effect on airway dimensions than vertical changes.
Keywords: Orthognathic surgery, Class III malocclusion, Posterior Airway Space, Lateral Cephalometric, Machine learning