A Novel Machine Learning Model for Predicting Orthodontic Treatment Duration
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Diagnostics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-4418/13/17/2740 |
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| author | James Volovic Sarkhan Badirli Sunna Ahmad Landon Leavitt Taylor Mason Surya Sruthi Bhamidipalli George Eckert David Albright Hakan Turkkahraman |
| author_facet | James Volovic Sarkhan Badirli Sunna Ahmad Landon Leavitt Taylor Mason Surya Sruthi Bhamidipalli George Eckert David Albright Hakan Turkkahraman |
| author_sort | James Volovic |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T23:26:08Z |
| format | Article |
| id | doaj.art-c54e4891c3ff4c40b6747023581d75c0 |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-10T23:26:08Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-c54e4891c3ff4c40b6747023581d75c02023-11-19T07:58:57ZengMDPI AGDiagnostics2075-44182023-08-011317274010.3390/diagnostics13172740A Novel Machine Learning Model for Predicting Orthodontic Treatment DurationJames Volovic0Sarkhan Badirli1Sunna Ahmad2Landon Leavitt3Taylor Mason4Surya Sruthi Bhamidipalli5George Eckert6David Albright7Hakan Turkkahraman8Department of Orthodontics and Oral Facial Genetics, Indiana University School of Dentistry, Indianapolis, IN 46202, USAEli Lilly and Company, Indianapolis, IN 46285, USADepartment of Orthodontics and Oral Facial Genetics, Indiana University School of Dentistry, Indianapolis, IN 46202, USADepartment of Orthodontics and Oral Facial Genetics, Indiana University School of Dentistry, Indianapolis, IN 46202, USADepartment of Orthodontics and Oral Facial Genetics, Indiana University School of Dentistry, Indianapolis, IN 46202, USADepartment of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN 46202, USADepartment of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN 46202, USADepartment of Orthodontics and Oral Facial Genetics, Indiana University School of Dentistry, Indianapolis, IN 46202, USADepartment of Orthodontics and Oral Facial Genetics, Indiana University School of Dentistry, Indianapolis, IN 46202, USAIn 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.https://www.mdpi.com/2075-4418/13/17/2740orthodonticstreatment durationmachine learningartificial intelligence |
| spellingShingle | James Volovic Sarkhan Badirli Sunna Ahmad Landon Leavitt Taylor Mason Surya Sruthi Bhamidipalli George Eckert David Albright Hakan Turkkahraman A Novel Machine Learning Model for Predicting Orthodontic Treatment Duration Diagnostics orthodontics treatment duration machine learning artificial intelligence |
| title | A Novel Machine Learning Model for Predicting Orthodontic Treatment Duration |
| title_full | A Novel Machine Learning Model for Predicting Orthodontic Treatment Duration |
| title_fullStr | A Novel Machine Learning Model for Predicting Orthodontic Treatment Duration |
| title_full_unstemmed | A Novel Machine Learning Model for Predicting Orthodontic Treatment Duration |
| title_short | A Novel Machine Learning Model for Predicting Orthodontic Treatment Duration |
| title_sort | novel machine learning model for predicting orthodontic treatment duration |
| topic | orthodontics treatment duration machine learning artificial intelligence |
| url | https://www.mdpi.com/2075-4418/13/17/2740 |
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