Among Artificial Intelligence/Machine Learning Methods, Automated Gradient‐Boosting Models Accurately Score Intraoral Plaque in Non‐Standardized Images

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). Evaluating 1650 plaque-disclosed primary teeth (teeth D, E, F...

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Published in:Journal of the California Dental Association Vol. 52; no. 1
Main Authors: Coy, Eric, Santo, William, Jue, Bonnie, Betts, Helen, Ramos-Gomez, Francisco, Gansky, Stuart A.
Format: Journal Article
Language:English
Published: United States Taylor & Francis Group 2024
Subjects:
Artificial intelligence/classification
automated pattern recognition/classification
dental photography/classification
dental plaque index/diagnostic imaging
machine learning/classification
observer variation
machine learning/classification
dental photography/classification
preschool child
Artificial intelligence/classification
automated pattern recognition/classification
observer variation
dental plaque index/diagnostic imaging
ISSN:1942-4396, 1043-2256, 1942-4396
Online Access:Get full text
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Summary: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). 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 R 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. 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 R of 0.72 with 1415s training). 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. 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.
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ISSN:1942-4396
1043-2256
1942-4396
DOI:10.1080/19424396.2024.2422146