ArticleJournal of dental sciences2025
Factors influencing craniofaciadental changes in skeletal Class III orthognathic surgery by using machine learning.
Article in Journal of dental sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Craniofacial and airway changes after skeletal Class III orthognathic surgery: A machine learning study.Journal of dental sciences · 2026Article
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Authors and funding
6 authors.
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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 ( 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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