Next Article in Journal
Gastroenteropancreatic Neuroendocrine Tumors—Current Status and Advances in Diagnostic Imaging
Previous Article in Journal
Systematic Review for Risks of Pressure Injury and Prediction Models Using Machine Learning Algorithms
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Diagnostics
Volume 13
Issue 17
10.3390/diagnostics13172740
diagnostics-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

A Novel Machine Learning Model for Predicting Orthodontic Treatment Duration

by
James Volovic
1,
Sarkhan Badirli
2,
Sunna Ahmad
1,
Landon Leavitt
1,
Taylor Mason
1,
Surya Sruthi Bhamidipalli
3,
George Eckert
3,
David Albright
1 and
Hakan Turkkahraman
1,*
1
Department of Orthodontics and Oral Facial Genetics, Indiana University School of Dentistry, Indianapolis, IN 46202, USA
2
Eli Lilly and Company, Indianapolis, IN 46285, USA
3
Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN 46202, USA
*
Author to whom correspondence should be addressed.
Diagnostics 2023, 13(17), 2740; https://doi.org/10.3390/diagnostics13172740
Submission received: 28 June 2023 / Revised: 14 August 2023 / Accepted: 21 August 2023 / Published: 23 August 2023
(This article belongs to the Section Machine Learning and Artificial Intelligence in Diagnostics)
Browse Figures
Versions Notes

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.
Keywords: orthodontics; treatment duration; machine learning; artificial intelligence orthodontics; treatment duration; machine learning; artificial intelligence

Share and Cite

MDPI and ACS Style

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. https://doi.org/10.3390/diagnostics13172740

AMA Style

Volovic J, Badirli S, Ahmad S, Leavitt L, Mason T, Bhamidipalli SS, Eckert G, Albright D, Turkkahraman H. A Novel Machine Learning Model for Predicting Orthodontic Treatment Duration. Diagnostics. 2023; 13(17):2740. https://doi.org/10.3390/diagnostics13172740

Chicago/Turabian Style

Volovic, James, Sarkhan Badirli, Sunna Ahmad, Landon Leavitt, Taylor Mason, Surya Sruthi Bhamidipalli, George Eckert, David Albright, and Hakan Turkkahraman. 2023. "A Novel Machine Learning Model for Predicting Orthodontic Treatment Duration" Diagnostics 13, no. 17: 2740. https://doi.org/10.3390/diagnostics13172740

APA Style

Volovic, J., Badirli, S., Ahmad, S., Leavitt, L., Mason, T., Bhamidipalli, S. S., Eckert, G., Albright, D., & Turkkahraman, H. (2023). A Novel Machine Learning Model for Predicting Orthodontic Treatment Duration. Diagnostics, 13(17), 2740. https://doi.org/10.3390/diagnostics13172740

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop