Related Experiment Video
Updated: May 14, 2026
Accuracy in Dental Medicine, A New Way to Measure Trueness and Precision
Published on: April 29, 2014
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.
Background:
Previous automated models inaccurately scored non-standardized plaque images. The objectives were to develop and test automated image selection and intraoral plaque-scoring (primary outcome measure in a prevention trial for preschoolers).
Methods:
Evaluating 1650 plaque-disclosed primary teeth (teeth D, E, F, G) from 435 photographs from UCSF/UCLA clinical trials, data were cleaned, transformed, and modeled with statistical and machine learning (ML) algorithms; data visualizations utilized Jupyter Notebooks, Python, OpenCV, and Sci-kit Learn libraries, with Laplacian filter preprocessing. Image selection and plaque-scoring used 8 ML classification models. Mean plaque-scoring used 8 ML regression models. Models were tuned with 80:20 train:test split, stratified 5-fold cross-validation (5-CV) (unstratified in regression models), and hyperparameter optimization. Area-under-the-curve receiver operating characteristic (AUC-ROC) curve and R2 determined the best classification and regression models, respectively, compared to calibrated dentist researcher ratings. Training time was a secondary metric. Manual segmentation used Photoshop's lasso tool. Average and dominant hue, saturation, and brightness values were features for training plaque-scoring algorithms.
Results:
Best performing models were: Support Vector Machine-Gaussian for image selection, 5-CV AUC-ROC of 0.99 and 0.76s of training time; Gradient-Boosting classification and regression models for individual teeth (5-CV AUC-ROC of 0.99 with 105s training); and mean plaque-scoring algorithms (5-CV R2 of 0.72 with 1415s training).
Conclusions:
Accurate automated plaque-scoring is attainable without the high computational and financial costs of deep learning (DL) models. Automated plaque-scoring is attainable with little user-manipulation.
Practical Implications:
Implementing automated tooth segmentation and synthetic sample generation with DL training may strengthen reliability, validity, and efficiency for clinical, research, and teledentistry applications by eliminating manual image preprocessing.
