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A Novel Machine Learning Model for Predicting Orthodontic Treatment Duration.

James Volovic1, Sarkhan Badirli2, Sunna Ahmad1

  • 1Department of Orthodontics and Oral Facial Genetics, Indiana University School of Dentistry, Indianapolis, IN 46202, USA.

Diagnostics (Basel, Switzerland)
|September 9, 2023
PubMed
Summary

Machine learning models can now predict orthodontic treatment duration using pre-treatment data. Random Forest, Lasso, and Elastic Net models showed the highest accuracy, improving treatment planning for patients.

Keywords:
artificial intelligencemachine learningorthodonticstreatment duration

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Area of Science:

  • Orthodontics
  • Data Science
  • Machine Learning

Background:

  • Accurate orthodontic treatment time estimates are crucial for patient satisfaction.
  • Machine learning (ML) offers advanced diagnostic and treatment planning capabilities in orthodontics.

Purpose of the Study:

  • To develop a novel ML model for predicting orthodontic treatment duration.
  • To identify key pre-treatment variables influencing treatment length.

Main Methods:

  • A retrospective study included patients from Indiana University School of Dentistry.
  • Fifty-seven pre-treatment variables were used to train and test nine ML models.
  • Model performance was evaluated using statistical analyses and intraclass correlation coefficients.

Main Results:

  • Random Forest, Lasso, and Elastic Net models demonstrated the highest accuracy in predicting treatment duration.
  • The most accurate models achieved a mean absolute error of 7.27 months.
  • Key predictors identified include extraction decisions, COVID-19 impact, intermaxillary relationships, lower incisor position, and use of additional appliances.

Conclusions:

  • ML models can effectively predict orthodontic treatment duration using pre-treatment variables.
  • This predictive capability can enhance orthodontic diagnosis and treatment planning.
  • Further research can refine ML models for more precise orthodontic outcome predictions.