ArticleDiagnostics (Basel, Switzerland)2023
A Novel Machine Learning Model for Predicting Orthodontic Treatment Duration.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed.
- [Comparative study on the clinical performance of concealable traction hook spherical buccal tubes versus conventional rectangular buccal tubes].Hua xi kou qiang yi xue za zhi = Huaxi kouqiang yixue zazhi = West China journal of stomatology · 2026Trial
- A novel AI system for preliminary triage support in single-tooth edentulous spaces using intraoral images.Scientific reports · 2026Article
- Prediction of Orthodontic Extraction Decisions Using Machine Learning Algorithms: A Retrospective Study.Journal of clinical and experimental dentistry · 2026Review
- Machine learning algorithms for predicting quality of life improvements after digital orthodontic treatment: a retrospective analysis.Biomedical engineering online · 2026Article
- Predicting patient satisfaction in digital orthodontics using a KAC based model integrating AI support and risk stratification.Scientific reports · 2026Article
- Applications of artificial intelligence in diagnosis and treatment planning of orthodontics: a narrative review.The Saudi dental journal · 2025Review
- Automated classification of skeletal malocclusion in German orthodontic patients.Clinical oral investigations · 2025Article
- AI-Driven Advancements in Orthodontics for Precision and Patient Outcomes.Dentistry journal · 2025Review
- Long-Term Predictive Modelling of the Craniofacial Complex Using Machine Learning on 2D Cephalometric Radiographs.International dental journal · 2025Article
- Integrating Artificial Intelligence Into Orthodontic Education and Practice.International dental journal · 2024Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the field of orthodontics, providing patients with accurate treatment time estimates is of utmost importance. As orthodontic practices continue to evolve and embrace new advancements, incorporating machine learning (ML) methods becomes increasingly valuable in improving orthodontic diagnosis and treatment planning. This study aimed to develop a novel ML model capable of predicting the orthodontic treatment duration based on essential pre-treatment variables. Patients who completed comprehensive orthodontic treatment at the Indiana University School of Dentistry were included in this retrospective study. Fifty-seven pre-treatment variables were collected and used to train and test nine different ML models. The performance of each model was assessed using descriptive statistics, intraclass correlation coefficients, and one-way analysis of variance tests. Random Forest, Lasso, and Elastic Net were found to be the most accurate, with a mean absolute error of 7.27 months in predicting treatment duration. Extraction decision, COVID, intermaxillary relationship, lower incisor position, and additional appliances were identified as important predictors of treatment duration. Overall, this study demonstrates the potential of ML in predicting orthodontic treatment duration using pre-treatment variables.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.