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Variational autoencoder-based estimation of chronological age and changes in morphological features of teeth
This study led to the development of a variational autoencoder (VAE) for estimating the chronological age of subjects using feature values extracted from their teeth. Further, it determined how given teeth images affected the estimation accuracy. The developed VAE was trained with the first molar an...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9839705/ https://www.ncbi.nlm.nih.gov/pubmed/36639691 http://dx.doi.org/10.1038/s41598-023-27950-4 |
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author | Joo, Subin Jung, Won Oh, Seung Eel |
author_facet | Joo, Subin Jung, Won Oh, Seung Eel |
author_sort | Joo, Subin |
collection | PubMed |
description | This study led to the development of a variational autoencoder (VAE) for estimating the chronological age of subjects using feature values extracted from their teeth. Further, it determined how given teeth images affected the estimation accuracy. The developed VAE was trained with the first molar and canine tooth images, and a parallel VAE structure was further constructed to extract common features shared by the two types of teeth more effectively. The encoder of the VAE was combined with a regression model to estimate the age. To determine which parts of the tooth images were more or less important when estimating age, a method of visualizing the obtained regression coefficient using the decoder of the VAE was developed. The developed age estimation model was trained using data from 910 individuals aged 10–79. This model showed a median absolute error (MAE) of 6.99 years, demonstrating its ability to estimate age accurately. Furthermore, this method of visualizing the influence of particular parts of tooth images on the accuracy of age estimation using a decoder is expected to provide novel insights for future research on explainable artificial intelligence. |
format | Online Article Text |
id | pubmed-9839705 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-98397052023-01-15 Variational autoencoder-based estimation of chronological age and changes in morphological features of teeth Joo, Subin Jung, Won Oh, Seung Eel Sci Rep Article This study led to the development of a variational autoencoder (VAE) for estimating the chronological age of subjects using feature values extracted from their teeth. Further, it determined how given teeth images affected the estimation accuracy. The developed VAE was trained with the first molar and canine tooth images, and a parallel VAE structure was further constructed to extract common features shared by the two types of teeth more effectively. The encoder of the VAE was combined with a regression model to estimate the age. To determine which parts of the tooth images were more or less important when estimating age, a method of visualizing the obtained regression coefficient using the decoder of the VAE was developed. The developed age estimation model was trained using data from 910 individuals aged 10–79. This model showed a median absolute error (MAE) of 6.99 years, demonstrating its ability to estimate age accurately. Furthermore, this method of visualizing the influence of particular parts of tooth images on the accuracy of age estimation using a decoder is expected to provide novel insights for future research on explainable artificial intelligence. Nature Publishing Group UK 2023-01-13 /pmc/articles/PMC9839705/ /pubmed/36639691 http://dx.doi.org/10.1038/s41598-023-27950-4 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This 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/) . |
spellingShingle | Article Joo, Subin Jung, Won Oh, Seung Eel Variational autoencoder-based estimation of chronological age and changes in morphological features of teeth |
title | Variational autoencoder-based estimation of chronological age and changes in morphological features of teeth |
title_full | Variational autoencoder-based estimation of chronological age and changes in morphological features of teeth |
title_fullStr | Variational autoencoder-based estimation of chronological age and changes in morphological features of teeth |
title_full_unstemmed | Variational autoencoder-based estimation of chronological age and changes in morphological features of teeth |
title_short | Variational autoencoder-based estimation of chronological age and changes in morphological features of teeth |
title_sort | variational autoencoder-based estimation of chronological age and changes in morphological features of teeth |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9839705/ https://www.ncbi.nlm.nih.gov/pubmed/36639691 http://dx.doi.org/10.1038/s41598-023-27950-4 |
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