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Among Artificial Intelligence/Machine Learning Methods, Automated Gradient-Boosting Models Accurately Score Intraoral
Eric Coy1, William Santo2, Bonnie Jue1
1University of California San Francisco School of Dentistry, San Francisco, Califonia, USA.
Journal of the California Dental Association
|February 24, 2025
Summary
Accurate automated plaque scoring for preschoolers is now possible using machine learning models, offering a cost-effective alternative to deep learning. This method simplifies image analysis for dental research and clinical applications.
Area of Science:
- Dentistry
- Machine Learning
- Image Analysis
Background:
- Previous automated dental plaque detection models struggled with non-standardized images.
- Accurate plaque scoring is crucial for preschooler dental prevention trials.
Purpose of the Study:
- Develop and validate automated methods for image selection and intraoral plaque scoring.
- Establish a reliable primary outcome measure for preschooler dental prevention trials.
Main Methods:
- Utilized 1650 plaque-disclosed primary teeth images from clinical trials.
- Employed machine learning (ML) algorithms, including Support Vector Machine-Gaussian and Gradient-Boosting, for classification and regression.
- Preprocessed images using Laplacian filters and extracted features like hue, saturation, and brightness.
Main Results:
- Achieved high performance with ML models: Support Vector Machine-Gaussian for image selection (AUC-ROC 0.99) and Gradient-Boosting for plaque scoring (AUC-ROC 0.99, R² 0.72).
- Demonstrated efficient training times, with image selection models training in under 2 seconds.
Conclusions:
- Accurate automated plaque scoring is achievable without expensive deep learning models.
- The developed automated system requires minimal user intervention, enhancing practicality.
