Detecting Dental Caries through Captured Images Using the Machine Learning Technology Teachable Machine
Tran Tuan Anh, Nguyen The Huy, Nguyen Tien Phat, Nguyen Thi Hoai Nhi, Vo Truong Nhu Ngoc, Tran Hoang Anh
Asian Journal of Medicine and Health · pp. 73–78 · Published 4 Mar 2024
10.9734/ajmah/2024/v22i3994Abstract
Objective: The objective is to utilize open-source artificial intelligence tool Teachable Machine to detect dental caries. Subjects and Research Methods: cross-sectional description. The research involved analyzing a total of 2,063 digital images, comprising 1,563 images showing dental caries and 500 images depicting teeth without any signs of dental caries. Results: Among the 1,563 images featuring dental caries, the recognition tool accurately identified 1,512 images (96%), while 51 images remained undetected, representing 4% of the total. Among the entire set of 2,063 images, encompassing both those with and without dental caries, 1,512 images were accurately identified (73.3%), while 551 images (26.7%) were not detected to have dental caries. Conclusion: The study on 1,563 images with dental caries using the Teachable Machine learning tool yielded promising results, achieving a high accuracy rate of 96%. However, when applied to the mixed dataset of 2,063 images, the accuracy rate for identifying images with dental caries dropped to only 73.3. The variance is ascribed to the initial stages of dental caries, which closely resemble the color of healthy tooth enamel. Therefore, the research team proposes the necessity for additional data on this form of decay to enhance classification and identification accuracy.
References (6)
- 1 An artificial intelligence system using machine-learning for automatic detection and classification of dental restorations in panoramic radiography [DOI]
- 2 A computer-aided automated methodology for the detection and classification of occlusal caries from photographic color images [DOI]
- 3 The Impact of Artificial Intelligence and Machine Learning in Radiation Therapy: Considerations for Future Curriculum Enhancement [DOI]
- 4 Detection of unexpected findings in radiology reports: A comparative study of machine learning approaches [DOI]
- 5 The Combination of Adaptive Convolutional Neural Network and Bag of Visual Words in Automatic Diagnosis of Third Molar Complications on Dental X-Ray Images [DOI]
- 6 Detection of Tooth caries in Bitewing Radiographs using Deep Learning [DOI]
Cited by 0
No indexed citations yet.
Related research
- Prevalence and Risk Factors of Dental Caries among Preparatory School Children, Menoufia Governorate, Egypt — shares topic coverage
- Fast Food Consumption and Dental Caries among Teenagers — shares topic coverage
- Smokeless Tobacco – Ammunition against Dental Caries? A Microbial Study — shares topic coverage
- Bacterial Profile Associated with Dental Caries in Jos, Nigeria — shares topic coverage
- Evaluation of the Cariogenic and Erosive Potential of Some Beverages Consumed in Enugu Metropolis, South East Nigeria — shares topic coverage
Article metrics
Real usage data collected on this platform.
74
Page views
0
PDF downloads
26
Outbound clicks
0
Citations
Views over time
Views by country
Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".
Traffic sources
Referring site, by host.
Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.