ArticleJournal of orofacial orthopedics = Fortschritte der Kieferorthopadie : Organ/official journal Deutsche Gesellschaft fur Kieferorthopadie2024
A novel machine learning model for class III surgery decision.
Article in Journal of orofacial orthopedics = Fortschritte der Kieferorthopadie : Organ/official journal Deutsche Gesellschaft fur Kieferorthopadie, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed.
- Artificial Intelligence Applications in Pediatric Craniofacial Surgery.Diagnostics (Basel, Switzerland) · 2025Review
- Long-Term Predictive Modelling of the Craniofacial Complex Using Machine Learning on 2D Cephalometric Radiographs.International dental journal · 2025Article
- Diagnostic accuracy of artificial intelligence in determining extraction protocol in orthodontic patients: A systematic review.Journal of orthodontic science · 2025Review
- Artificial intelligence as a prediction tool for orthognathic surgery assessment.Orthodontics & craniofacial research · 2024Article
- Review
- Radiographic Imaging for the Diagnosis and Treatment of Patients with Skeletal Class III Malocclusion.Diagnostics (Basel, Switzerland) · 2024Review
- Artificial intelligence in dentistry and dental biomaterials.Frontiers in dental medicine · 2024Review
- Artificial Intelligence and Its Clinical Applications in Orthodontics: A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- Application of Artificial Intelligence in Orthodontics: Current State and Future Perspectives.Healthcare (Basel, Switzerland) · 2023Review
- A Novel Machine Learning Model for Predicting Orthodontic Treatment Duration.Diagnostics (Basel, Switzerland) · 2023Article
- Short- and Long-Term Prediction of the Post-Pubertal Mandibular Length andDiagnostics (Basel, Switzerland) · 2023Article
- Prediction of Pubertal Mandibular Growth in Males with Class II Malocclusion by Utilizing Machine Learning.Diagnostics (Basel, Switzerland) · 2023Article
- Embracing the Unprecedented Pace of Change: Artificial Intelligence's Impact on Dentistry and Beyond.European journal of dentistry · 2023Article
- Prediction of the Post-Pubertal Mandibular Length and Y Axis of Growth by Using Various Machine Learning Techniques: A Retrospective Longitudinal Study.Diagnostics (Basel, Switzerland) · 2023Article
- Computation of the distribution of model accuracy statistics in machine learning: Comparison between analytically derived distributions and simulation-based methods.Health science reports · 2023Article
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Authors and funding
5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
purposeThe primary purpose of this study was to develop a new machine learning model for the surgery/non-surgery decision in class III patients and evaluate the validity and reliability of this model.
methodsThe sample consisted of 196 skeletal class III patients. All the cases were allocated randomly, 136 to the training set and the remaining 60 to the test set. Using the test set, the success rate of the artificial neural network model was estimated, along with a 95% confidence interval. To predict surgical cases, we trained a binary classifier using two different methods: random forest (RF) and logistic regression (LR).
resultsBoth the RF and the LR model showed high separability when classifying each patient for surgical or non-surgical treatment. RF achieved an area under the curve (AUC) of 0.9395 on the test set. 95% confidence intervals were computed by bootstrap sampling as lower bound = 0.7908 and higher bound = 0.9799. On the other hand, LR achieved an AUC of 0.937 on the test set. 95% confidence intervals were computed by bootstrap sampling as lower bound = 0.8467 and higher bound = 0.9812.
conclusionsRF and LR machine learning models can be used to generate accurate and reliable algorithms to successfully classify patients up to 90%. The features selected by the algorithms coincide with the clinical features that we as clinicians weigh heavily when determining a treatment plan. This study further supports that overjet, Wits appraisal, lower incisor angulation, and Holdaway H angle can be used as strong predictors in assessing a patient's surgical needs.
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