Volovic, J.; Badirli, S.; Ahmad, S.; Leavitt, L.; Mason, T.; Bhamidipalli, S.S.; Eckert, G.; Albright, D.; Turkkahraman, H. A Novel Machine Learning Model for Predicting Orthodontic Treatment Duration. Diagnostics2023, 13, 2740.
Volovic, J.; Badirli, S.; Ahmad, S.; Leavitt, L.; Mason, T.; Bhamidipalli, S.S.; Eckert, G.; Albright, D.; Turkkahraman, H. A Novel Machine Learning Model for Predicting Orthodontic Treatment Duration. Diagnostics 2023, 13, 2740.
Volovic, J.; Badirli, S.; Ahmad, S.; Leavitt, L.; Mason, T.; Bhamidipalli, S.S.; Eckert, G.; Albright, D.; Turkkahraman, H. A Novel Machine Learning Model for Predicting Orthodontic Treatment Duration. Diagnostics2023, 13, 2740.
Volovic, J.; Badirli, S.; Ahmad, S.; Leavitt, L.; Mason, T.; Bhamidipalli, S.S.; Eckert, G.; Albright, D.; Turkkahraman, H. A Novel Machine Learning Model for Predicting Orthodontic Treatment Duration. Diagnostics 2023, 13, 2740.
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 9 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.
Medicine and Pharmacology, Dentistry and Oral Surgery
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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