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Machine Learning for the Diagnosis of Orthodontic Extractions: A Computational Analysis Using Ensemble Learning
Yasir Suhail1, Madhur Upadhyay2, Aditya Chhibber3
1Department of Biomedical Engineering, University of Connecticut Health Center, Farmington, CT 06032, USA.
Bioengineering (Basel, Switzerland)
|June 18, 2020
Summary
Machine learning models can assist orthodontists in making tooth extraction decisions. Ensemble methods achieve results comparable to human expert agreement, improving treatment reliability.
Area of Science:
- Orthodontic treatment planning
- Artificial intelligence in dentistry
- Clinical decision support systems
Background:
- Tooth extraction is a critical decision in orthodontics.
- Expert systems can aid clinicians in treatment planning and error reduction.
- Objective decision-making tools are needed to enhance reliability and training.
Purpose of the Study:
- To develop and evaluate machine learning models for predicting tooth extraction decisions in orthodontics.
- To assess the suitability of ensemble methods for this complex clinical prediction task.
- To compare model performance against the agreement levels of human orthodontic experts.
Main Methods:
- Training various machine learning models on a dataset of 287 patients.
- Utilizing data evaluated independently by five experienced orthodontists.
- Employing ensemble methods to aggregate predictions from multiple models.
- Analyzing model performance and training behavior.
Main Results:
- Ensemble machine learning methods demonstrated strong performance in predicting tooth extraction.
- Model results closely approximated the level of agreement observed among human orthodontists.
- Interpretation of model training provided insights into the decision-making process.
Conclusions:
- Machine learning, particularly ensemble methods, shows significant potential as a decision support tool in orthodontic tooth extraction.
- These models can help standardize treatment planning, reduce errors, and aid in training.
- The developed system offers a reliability level comparable to expert consensus.
