Publication: Automated machine learning for image-based detection of dental plaque on permanent teeth
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Issued Date
2024-01-01
Resource Type
eISSN
26734915
Scopus ID
2-s2.0-85211586827
Journal Title
Frontiers in Dental Medicine
Volume
5
Rights Holder(s)
SCOPUS
Bibliographic Citation
Frontiers in Dental Medicine Vol.5 (2024)
Suggested Citation
Nantakeeratipat T., Apisaksirikul N., Boonrojsaree B., Boonkijkullatat S., Simaphichet A. Automated machine learning for image-based detection of dental plaque on permanent teeth. Frontiers in Dental Medicine Vol.5 (2024). doi:10.3389/fdmed.2024.1507705 Retrieved from: https://hdl.handle.net/20.500.14740/20544
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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.