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BMC Oral Health
Published in: BMC Oral Health 1/2023

Open Access 01-12-2023 | Caries | Research

Tooth caries classification with quantitative light-induced fluorescence (QLF) images using convolutional neural network for permanent teeth in vivo

Authors: Eun Young Park, Sungmoon Jeong, Sohee Kang, Jungrae Cho, Ju-Yeon Cho, Eun-Kyong Kim

Published in: BMC Oral Health | Issue 1/2023

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Abstract

Background

Owing to the remarkable advancements of artificial intelligence (AI) applications, AI-based detection of dental caries is continuously improving. We evaluated the efficacy of the detection of dental caries with quantitative light-induced fluorescence (QLF) images using a convolutional neural network (CNN) model.

Methods

Overall, 2814 QLF intraoral images were obtained from 606 participants at a dental clinic using Qraypen C® (QC, AIOBIO, Seoul, Republic of Korea) from October 2020 to October 2022. These images included all the types of permanent teeth of which surfaces were smooth or occlusal. Dataset were randomly assigned to the training (56.0%), validation (14.0%), and test (30.0%) subsets of the dataset for caries classification. Moreover, masked images for teeth area were manually prepared to evaluate the segmentation efficacy. To compare diagnostic performance for caries classification according to the types of teeth, the dataset was further classified into the premolar (1,143 images) and molar (1,441 images) groups. As the CNN model, Xception was applied.

Results

Using the original QLF images, the performance of the classification algorithm was relatively good showing 83.2% of accuracy, 85.6% of precision, and 86.9% of sensitivity. After applying the segmentation process for the tooth area, all the performance indics including 85.6% of accuracy, 88.9% of precision, and 86.9% of sensitivity were improved. However, the performance indices of each type of teeth (both premolar and molar) were similar to those for all teeth.

Conclusion

The application of AI to QLF images for caries classification demonstrated a good performance regardless of teeth type among posterior teeth. Additionally, tooth area segmentation through background elimination from QLF images exhibited a better performance.
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Metadata
Title
Tooth caries classification with quantitative light-induced fluorescence (QLF) images using convolutional neural network for permanent teeth in vivo
Authors
Eun Young Park
Sungmoon Jeong
Sohee Kang
Jungrae Cho
Ju-Yeon Cho
Eun-Kyong Kim
Publication date
01-12-2023
Publisher
BioMed Central
Published in
BMC Oral Health / Issue 1/2023
Electronic ISSN: 1472-6831
DOI
https://doi.org/10.1186/s12903-023-03669-6

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