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A novel machine learning model for class III surgery decision.

Hunter Lee1, Sunna Ahmad2, Michael Frazier1

  • 1Department of Orthodontics and Oral Facial Genetics, Indiana University School of Dentistry, 1121 West Michigan Street, 46202, Indianapolis, IN, USA.

Journal of Orofacial Orthopedics = Fortschritte Der Kieferorthopadie : Organ/Official Journal Deutsche Gesellschaft Fur Kieferorthopadie
|August 26, 2022
PubMed
Summary

Machine learning models, including random forest (RF) and logistic regression (LR), accurately classify skeletal class III patients for surgery. Key clinical features reliably predict surgical needs, aiding treatment decisions.

Keywords:
Artificial intelligenceComputer-assisted decision makingDentofacial deformitiesLogistic modelsOrthognathic surgery

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Area of Science:

  • Orthodontics and Dentofacial Orthopedics
  • Artificial Intelligence in Medicine
  • Biomedical Data Science

Background:

  • Skeletal class III malocclusion presents complex treatment challenges.
  • Accurate prediction of surgical versus non-surgical treatment is crucial for effective management.
  • Machine learning offers potential for improving diagnostic and treatment planning accuracy.

Purpose of the Study:

  • To develop and validate a machine learning model for the surgery/non-surgery decision in skeletal class III patients.
  • To evaluate the reliability and accuracy of the developed model.
  • To identify key predictors for surgical intervention in class III cases.

Main Methods:

  • A dataset of 196 skeletal class III patients was utilized.
  • Patients were randomly allocated to training (136) and testing (60) sets.
  • Binary classifiers, Random Forest (RF) and Logistic Regression (LR), were trained to predict surgical cases.

Main Results:

  • Both RF and LR models demonstrated high accuracy in classifying patients for surgical or non-surgical treatment.
  • The RF model achieved an Area Under the Curve (AUC) of 0.9395 on the test set.
  • The LR model achieved an AUC of 0.937 on the test set, with comparable performance to RF.

Conclusions:

  • Random Forest and Logistic Regression models provide accurate and reliable algorithms for classifying class III patients, achieving success rates up to 90%.
  • The features identified by the models, such as overjet, Wits appraisal, lower incisor angulation, and Holdaway H angle, align with clinical judgment.
  • These validated predictors can significantly aid clinicians in assessing surgical needs and formulating treatment plans for skeletal class III patients.