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APPLICATION OF ARTIFICIAL INTELLIGENCE IN DENTAL CARIES DETECTION USING THE TEACHABLE MACHINE OPEN-SOURCE TOOL
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Abstract
Objective: Description of the results of the open-source Teachable Machine application training machine learning to detect deep tooth decay on intraoral images Methods and Subjects: cross-sectional description, the study was conducted using 988 digital images, consisting of 868 images with dental caries and 120 images of normal teeth. Results: Out of the total 868 images with dental caries, the identification process yielded accurate results for 849 images (97.8%), with 19 images (2.2%) remaining undetected for dental caries. Among the total of 988 images, including both images with and without dental caries, the correct identification rate was 849 images (85.9%), with 139 images (14.1%) not detecting dental caries. Conclusion: The use of the Teachable Machine open-source tool for identifying images with dental caries produced initially reliable results with a high accuracy rate of 97,8% (on a dataset exclusively containing images of dental caries). However, for the mixed dataset (containing both images with and without dental caries), the accuracy rate dropped to 85,9%. This difference is attributed to the early appearance of dental caries, as the color of the caries is somewhat correlated with that of normal tooth enamel. Additional data on this type of injury is necessary to classify and identify it more accurately.
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Keywords
artificial intelligence; machine learning; dental caries; teachable machine
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