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DOI

10.1016/j.jds.2024.08.017

First Page

919

Last Page

926

Abstract

Abstract 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. Materials and methods 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). Results 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 ( P = 0.045), whereas IMPA and lower lip to E-plane were significantly influenced by sex (IMPA P = 0.029; lower lip to E-plane P = 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). Conclusion 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.

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