ArticleJournal of orthodontic science2026
Artificial intelligence-powered prediction of orthodontic treatment duration: Identifying short and prolonged cases from a dataset of more than 10,000 patients.
Article in Journal of orthodontic science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundTreatment duration is a major factor influencing patient motivation, acceptance, and clinical workflow in orthodontics. Accurately estimating treatment length and identifying cases likely to finish very early or require extended time can significantly enhance treatment planning and patient communication. This study aimed to develop and compare machine learning (ML) models for predicting continuous orthodontic treatment duration and for classifying short-duration (≤12 months) and long-duration (≥30 months) treatments using a large, diverse clinical dataset.
methodsA total of 10,553 completed orthodontic cases were extracted from 120,000 records in the FMBO database. Seventy percent of the data was used for training and 30% for testing. Seven regression algorithms (Linear Regression, OLS, Bagging, Random Forest, Gradient Boost, AdaBoost, XGBoost) were evaluated. Classification models (Logistic Regression, Random Forest, Gradient Boost, XGBoost, AdaBoost, Bagging) were developed to identify short and long treatments. Class imbalance was addressed using class weighting and optimized probability thresholds (0.24 for short, 0.16 for long). Model performance was assessed using MSE,
resultsThe Gradient Boost Regression model demonstrated the strongest performance for continuous duration prediction (
conclusionsML models demonstrated effective performance in predicting orthodontic treatment duration and in identifying fast- and slow-finishing cases. Key demographic, dental, and skeletal features-particularly age, extraction plan, incisor AP position, and vertical/incisal characteristics-were consistently strong predictors. The integration of big data and ML provides a scalable approach to enhance orthodontic diagnosis, optimize scheduling, improve communication of expectations, and support individualized treatment planning. Future studies incorporating compliance, biomechanics, and 3D imaging may further strengthen prediction accuracy.
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