AUTHOR=Nantakeeratipat Teerachate , Apisaksirikul Natchapon , Boonrojsaree Boonyaon , Boonkijkullatat Sirapob , Simaphichet Arida TITLE=Automated machine learning for image-based detection of dental plaque on permanent teeth JOURNAL=Frontiers in Dental Medicine VOLUME=5 YEAR=2024 URL=https://www.frontiersin.org/journals/dental-medicine/articles/10.3389/fdmed.2024.1507705 DOI=10.3389/fdmed.2024.1507705 ISSN=2673-4915 ABSTRACT=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.

Conclusion

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.