Abdillah Muhammad Izzah, Hsin-Chung Cheng Johnson, De-Shing Chen Daniel, Li-Sheng Chen Sam, Ruslin Muhammad, Ranggang Baharuddin M
School of Dentistry, College of Oral Medicine, Taipei Medical University, Taipei, Taiwan.
Orthodontic Division, Department of Dentistry, Taipei Medical University Hospital, Taipei, Taiwan.
J Dent Sci. 2025 Apr;20(2):919-926. doi: 10.1016/j.jds.2024.08.017. Epub 2024 Sep 8.
BACKGROUND/PURPOSE: In skeletal Class III patients, treatment options include camouflage and orthognathic surgery. This study used machine learning to investigate factors influencing dental, skeletal, and soft tissue morphological changes following skeletal Class III orthognathic surgery.
A retrospective analysis was conducted at Taipei Medical University Hospital. The study analyzed the lateral cephalometric radiographs of 58 patients with skeletal Class III who underwent orthognathic surgery. Web-based cephalometric software was used to obtain cephalometric tracing measurements, including dental, skeletal, and soft tissue parameters at pretreatment (T0) and posttreatment (T1), and assess postsurgical changes (T1-T0). Conventional statistical models were used for data analysis, followed by the application of machine learning-based random forest regression to identify influencing factors, as characterized by the feature of importance (FI).
All cephalometric variables except SNA, A to NP, overbite, and lower lip to E-plane differed significantly between T0 and T1. ANB was significantly influenced by surgery type ( = 0.045), whereas IMPA and lower lip to E-plane were significantly influenced by sex (IMPA = 0.029; lower lip to E-plane = 0.033). According to machine learning results on the influence of pretreatment conditions, overjet was a key factor influencing several dependent variables, namely, changes in ANB (FI = 0.226), B to N-Perp (FH) (FI = 0.259), and Pog to N-Perp (FH) (FI = 0.257).
Machine learning revealed the overjet plays a dominant role in several dependent variables, including changes in ANB, B to N-Perp (FH), and Pog to N-Perp (FH). Future studies should use a larger dataset and three-dimensional data.
背景/目的：在骨性III类患者中，治疗选择包括掩饰性治疗和正颌手术。本研究采用机器学习来探究影响骨性III类正颌手术后牙齿、骨骼和软组织形态变化的因素。
在台北医学大学医院进行了一项回顾性分析。该研究分析了58例行正颌手术的骨性III类患者的头颅侧位片。使用基于网络的头颅测量软件获取头颅测量追踪数据，包括治疗前（T0）和治疗后（T1）的牙齿、骨骼和软组织参数，并评估术后变化（T1 - T0）。采用传统统计模型进行数据分析，随后应用基于机器学习的随机森林回归来识别影响因素，以重要性特征（FI）来表征。
除SNA、A至NP、覆合和下唇至E平面外，所有头颅测量变量在T0和T1之间均有显著差异。ANB受手术类型的显著影响（P = 0.045），而IMPA和下唇至E平面受性别显著影响（IMPA，P = 0.029；下唇至E平面，P = 0.033）。根据机器学习关于治疗前状况影响的结果，覆盖是影响几个因变量的关键因素，即ANB的变化（FI = 0.226）、B至N - Perp（FH）的变化（FI = 0.259）和Pog至N - Perp（FH）的变化（FI = 0.257）。
机器学习表明覆盖在几个因变量中起主导作用，包括ANB、B至N - Perp（FH）和Pog至N - Perp（FH）的变化。未来的研究应使用更大的数据集和三维数据。
