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Caries detection with tooth surface segmentation on intraoral photographic images using deep learning
BACKGROUND: Intraoral photographic images are helpful in the clinical diagnosis of caries. Moreover, the application of artificial intelligence to these images has been attempted consistently. This study aimed to evaluate a deep learning algorithm for caries detection through the segmentation of the...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9730679/ https://www.ncbi.nlm.nih.gov/pubmed/36476359 http://dx.doi.org/10.1186/s12903-022-02589-1 |
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author | Park, Eun Young Cho, Hyeonrae Kang, Sohee Jeong, Sungmoon Kim, Eun-Kyong |
author_facet | Park, Eun Young Cho, Hyeonrae Kang, Sohee Jeong, Sungmoon Kim, Eun-Kyong |
author_sort | Park, Eun Young |
collection | PubMed |
description | BACKGROUND: Intraoral photographic images are helpful in the clinical diagnosis of caries. Moreover, the application of artificial intelligence to these images has been attempted consistently. This study aimed to evaluate a deep learning algorithm for caries detection through the segmentation of the tooth surface using these images. METHODS: In this prospective study, 2348 in-house intraoral photographic images were collected from 445 participants using a professional intraoral camera at a dental clinic in a university medical centre from October 2020 to December 2021. Images were randomly assigned to training (1638), validation (410), and test (300) datasets. For image segmentation of the tooth surface, classification, and localisation of caries, convolutional neural networks (CNN), namely U-Net, ResNet-18, and Faster R-CNN, were applied. RESULTS: For the classification algorithm for caries images, the accuracy and area under the receiver operating characteristic curve were improved to 0.813 and 0.837 from 0.758 to 0.731, respectively, through segmentation of the tooth surface using CNN. Localisation algorithm for carious lesions after segmentation of the tooth area also showed improved performance. For example, sensitivity and average precision improved from 0.890 to 0.889 to 0.865 and 0.868, respectively. CONCLUSION: The deep learning model with segmentation of the tooth surface is promising for caries detection on photographic images from an intraoral camera. This may be an aided diagnostic method for caries with the advantages of being time and cost-saving. |
format | Online Article Text |
id | pubmed-9730679 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-97306792022-12-09 Caries detection with tooth surface segmentation on intraoral photographic images using deep learning Park, Eun Young Cho, Hyeonrae Kang, Sohee Jeong, Sungmoon Kim, Eun-Kyong BMC Oral Health Research BACKGROUND: Intraoral photographic images are helpful in the clinical diagnosis of caries. Moreover, the application of artificial intelligence to these images has been attempted consistently. This study aimed to evaluate a deep learning algorithm for caries detection through the segmentation of the tooth surface using these images. METHODS: In this prospective study, 2348 in-house intraoral photographic images were collected from 445 participants using a professional intraoral camera at a dental clinic in a university medical centre from October 2020 to December 2021. Images were randomly assigned to training (1638), validation (410), and test (300) datasets. For image segmentation of the tooth surface, classification, and localisation of caries, convolutional neural networks (CNN), namely U-Net, ResNet-18, and Faster R-CNN, were applied. RESULTS: For the classification algorithm for caries images, the accuracy and area under the receiver operating characteristic curve were improved to 0.813 and 0.837 from 0.758 to 0.731, respectively, through segmentation of the tooth surface using CNN. Localisation algorithm for carious lesions after segmentation of the tooth area also showed improved performance. For example, sensitivity and average precision improved from 0.890 to 0.889 to 0.865 and 0.868, respectively. CONCLUSION: The deep learning model with segmentation of the tooth surface is promising for caries detection on photographic images from an intraoral camera. This may be an aided diagnostic method for caries with the advantages of being time and cost-saving. BioMed Central 2022-12-07 /pmc/articles/PMC9730679/ /pubmed/36476359 http://dx.doi.org/10.1186/s12903-022-02589-1 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Park, Eun Young Cho, Hyeonrae Kang, Sohee Jeong, Sungmoon Kim, Eun-Kyong Caries detection with tooth surface segmentation on intraoral photographic images using deep learning |
title | Caries detection with tooth surface segmentation on intraoral photographic images using deep learning |
title_full | Caries detection with tooth surface segmentation on intraoral photographic images using deep learning |
title_fullStr | Caries detection with tooth surface segmentation on intraoral photographic images using deep learning |
title_full_unstemmed | Caries detection with tooth surface segmentation on intraoral photographic images using deep learning |
title_short | Caries detection with tooth surface segmentation on intraoral photographic images using deep learning |
title_sort | caries detection with tooth surface segmentation on intraoral photographic images using deep learning |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9730679/ https://www.ncbi.nlm.nih.gov/pubmed/36476359 http://dx.doi.org/10.1186/s12903-022-02589-1 |
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