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Image diagnosis models for the oral assessment of older people using convolutional neural networks: A retrospective observational study

AIMS: The purpose of this study was to construct a model for oral assessment using deep learning image recognition technology and to verify its accuracy. BACKGROUND: The effects of oral care on older people are significant, and the Oral Assessment Guide has been used internationally as an effective...

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Autores principales: Muramatsu, Misato, Muramatsu, Masumi, Takahashi, Naoto, Hagiwara, Atsuko, Hagiwara, Jyun, Takamatsu, Yuichiro, Morooka, Ryo, Ochi, Morio, Kaitani, Toshiko
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9787883/
https://www.ncbi.nlm.nih.gov/pubmed/34935230
http://dx.doi.org/10.1111/jocn.16182
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author Muramatsu, Misato
Muramatsu, Masumi
Takahashi, Naoto
Hagiwara, Atsuko
Hagiwara, Jyun
Takamatsu, Yuichiro
Morooka, Ryo
Ochi, Morio
Kaitani, Toshiko
author_facet Muramatsu, Misato
Muramatsu, Masumi
Takahashi, Naoto
Hagiwara, Atsuko
Hagiwara, Jyun
Takamatsu, Yuichiro
Morooka, Ryo
Ochi, Morio
Kaitani, Toshiko
author_sort Muramatsu, Misato
collection PubMed
description AIMS: The purpose of this study was to construct a model for oral assessment using deep learning image recognition technology and to verify its accuracy. BACKGROUND: The effects of oral care on older people are significant, and the Oral Assessment Guide has been used internationally as an effective oral assessment tool in clinical practice. However, additional training, education, development of user manuals and continuous support from a dental hygienist are needed to improve the inter‐rater reliability of the Oral Assessment Guide. DESIGN: A retrospective observational study. METHODS: A total of 3,201 oral images of 114 older people aged >65 years were collected from five dental‐related facilities. These images were divided into six categories (lips, tongue, saliva, mucosa, gingiva, and teeth or dentures) that were evaluated by images, out of the total eight items that comprise components of the Oral Assessment Guide. Each item was classified into a rating of 1, 2 or 3. A convolutional neural network, which is a deep learning method used for image recognition, was used to construct the image recognition model. The study methods comply with the STROBE checklist. RESULTS: We constructed models with a classification accuracy of 98.8% for lips, 94.3% for tongue, 92.8% for saliva, 78.6% for mucous membranes, 93.0% for gingiva and 93.6% for teeth or dentures. CONCLUSIONS: Highly accurate diagnostic imaging models using convolutional neural networks were constructed for six items of the Oral Assessment Guide and validated. In particular, for the five items of lips, tongue, saliva, gingiva, and teeth or dentures, models with a high accuracy of over 90% were obtained. RELEVANCE TO CLINICAL PRACTICE: The model built in this study has the potential to contribute to obtain reproducibility and reliability of the ratings, to shorten the time for assessment, to collaborate with dental professionals and to be used as an educational tool.
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spelling pubmed-97878832022-12-28 Image diagnosis models for the oral assessment of older people using convolutional neural networks: A retrospective observational study Muramatsu, Misato Muramatsu, Masumi Takahashi, Naoto Hagiwara, Atsuko Hagiwara, Jyun Takamatsu, Yuichiro Morooka, Ryo Ochi, Morio Kaitani, Toshiko J Clin Nurs Original Articles AIMS: The purpose of this study was to construct a model for oral assessment using deep learning image recognition technology and to verify its accuracy. BACKGROUND: The effects of oral care on older people are significant, and the Oral Assessment Guide has been used internationally as an effective oral assessment tool in clinical practice. However, additional training, education, development of user manuals and continuous support from a dental hygienist are needed to improve the inter‐rater reliability of the Oral Assessment Guide. DESIGN: A retrospective observational study. METHODS: A total of 3,201 oral images of 114 older people aged >65 years were collected from five dental‐related facilities. These images were divided into six categories (lips, tongue, saliva, mucosa, gingiva, and teeth or dentures) that were evaluated by images, out of the total eight items that comprise components of the Oral Assessment Guide. Each item was classified into a rating of 1, 2 or 3. A convolutional neural network, which is a deep learning method used for image recognition, was used to construct the image recognition model. The study methods comply with the STROBE checklist. RESULTS: We constructed models with a classification accuracy of 98.8% for lips, 94.3% for tongue, 92.8% for saliva, 78.6% for mucous membranes, 93.0% for gingiva and 93.6% for teeth or dentures. CONCLUSIONS: Highly accurate diagnostic imaging models using convolutional neural networks were constructed for six items of the Oral Assessment Guide and validated. In particular, for the five items of lips, tongue, saliva, gingiva, and teeth or dentures, models with a high accuracy of over 90% were obtained. RELEVANCE TO CLINICAL PRACTICE: The model built in this study has the potential to contribute to obtain reproducibility and reliability of the ratings, to shorten the time for assessment, to collaborate with dental professionals and to be used as an educational tool. John Wiley and Sons Inc. 2021-12-21 2022-12 /pmc/articles/PMC9787883/ /pubmed/34935230 http://dx.doi.org/10.1111/jocn.16182 Text en © 2021 The Authors. Journal of Clinical Nursing published by John Wiley & Sons Ltd. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Articles
Muramatsu, Misato
Muramatsu, Masumi
Takahashi, Naoto
Hagiwara, Atsuko
Hagiwara, Jyun
Takamatsu, Yuichiro
Morooka, Ryo
Ochi, Morio
Kaitani, Toshiko
Image diagnosis models for the oral assessment of older people using convolutional neural networks: A retrospective observational study
title Image diagnosis models for the oral assessment of older people using convolutional neural networks: A retrospective observational study
title_full Image diagnosis models for the oral assessment of older people using convolutional neural networks: A retrospective observational study
title_fullStr Image diagnosis models for the oral assessment of older people using convolutional neural networks: A retrospective observational study
title_full_unstemmed Image diagnosis models for the oral assessment of older people using convolutional neural networks: A retrospective observational study
title_short Image diagnosis models for the oral assessment of older people using convolutional neural networks: A retrospective observational study
title_sort image diagnosis models for the oral assessment of older people using convolutional neural networks: a retrospective observational study
topic Original Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9787883/
https://www.ncbi.nlm.nih.gov/pubmed/34935230
http://dx.doi.org/10.1111/jocn.16182
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