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BRIEF RESEARCH REPORT article

Front. Dent. Med
Sec. Periodontics
Volume 5 - 2024 | doi: 10.3389/fdmed.2024.1507705
This article is part of the Research Topic Diagnostic and Treatment Strategies for Periodontal Disease View all articles

Automated Machine Learning (AutoML) for Image-Based Detection of Dental Plaque on Permanent Teeth

Provisionally accepted
Teerachate Nantakeeratipat Teerachate Nantakeeratipat 1*Natchapon Apisaksirikul Natchapon Apisaksirikul 2Boonyaon Boonrojsaree Boonyaon Boonrojsaree 2Sirapob Boonkijkullatat Sirapob Boonkijkullatat 2Arida Simapichet Arida Simapichet 2
  • 1 Department of Conservative Dentistry and Prosthodontics, Faculty of Dentistry, Srinakharinwirot University, Bangkok, Thailand
  • 2 Faculty of Dentistry, Srinakharinwirot University, Bangkok, Thailand

The final, formatted version of the article will be published soon.

    Introduction: To detect dental plaque, manual assessment and plaque-disclosing dyes are commonly used. However, they are time-consuming and prone to human error. This study aims to investigate the feasibility of using Google Cloud's Vertex artificial intelligence (AI) automated machine learning (AutoML) to develop a model for detecting dental plaque levels on permanent teeth using undyed photographic images.Methods: Photographic images of both undyed and corresponding erythrosine solution-dyed upper anterior permanent teeth from 100 dental students were captured using a smartphone camera. All photos were cropped to individual tooth images. Dyed images were analyzed to classify plaque levels based on the percentage of dyed surface area: mild (<30%), moderate (30-60%), and heavy (>60%) categories. These true labels were used as the ground truth for undyed images. Two AutoML models, a three-class model (mild, moderate, heavy plaque) and a two-class model (acceptable vs. unacceptable plaque), were developed using undyed images in Vertex AI environment. Both models were evaluated based on precision, recall, and F1-score.Results: The three-class model achieved an average precision of 0.907, with the highest precision (0.983) in the heavy plaque category. Misclassifications were more common in the mild and moderate categories. The two-class acceptable-unacceptable model demonstrated improved performance with an average precision of 0.964 and an F1-score of 0.931.This study demonstrated the potential of Vertex AI AutoML for non-invasive detection of dental plaque. While the two-class model showed promise for clinical use, further studies with larger datasets are recommended to enhance model generalization and real-world applicability.

    Keywords: Automated machine learning, image classification, Dental Plaque, Digital Health, Vertex AI

    Received: 08 Oct 2024; Accepted: 14 Nov 2024.

    Copyright: © 2024 Nantakeeratipat, Apisaksirikul, Boonrojsaree, Boonkijkullatat and Simapichet. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Teerachate Nantakeeratipat, Department of Conservative Dentistry and Prosthodontics, Faculty of Dentistry, Srinakharinwirot University, Bangkok, Thailand

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.