Cargando…

Age estimation based on 3D post-mortem computed tomography images of mandible and femur using convolutional neural networks

Age assessment has attracted increasing attention in the field of forensics. However, most existing works are laborious and requires domain-specific knowledge. Modern computing power makes it is possible to leverage massive amounts of data to produce more reliable results. Therefore, it is logical t...

Descripción completa

Detalles Bibliográficos
Autores principales: Pham, Cuong Van, Lee, Su-Jin, Kim, So-Yeon, Lee, Sookyoung, Kim, Soo-Hyung, Kim, Hyung-Seok
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8115850/
https://www.ncbi.nlm.nih.gov/pubmed/33979376
http://dx.doi.org/10.1371/journal.pone.0251388
_version_ 1783691275238637568
author Pham, Cuong Van
Lee, Su-Jin
Kim, So-Yeon
Lee, Sookyoung
Kim, Soo-Hyung
Kim, Hyung-Seok
author_facet Pham, Cuong Van
Lee, Su-Jin
Kim, So-Yeon
Lee, Sookyoung
Kim, Soo-Hyung
Kim, Hyung-Seok
author_sort Pham, Cuong Van
collection PubMed
description Age assessment has attracted increasing attention in the field of forensics. However, most existing works are laborious and requires domain-specific knowledge. Modern computing power makes it is possible to leverage massive amounts of data to produce more reliable results. Therefore, it is logical to use automated age estimation approaches to handle large datasets. In this study, a fully automated age prediction approach was proposed by assessing 3D mandible and femur scans using deep learning. A total of 814 post-mortem computed tomography scans from 619 men and 195 women, within the age range of 20–70, were collected from the National Forensic Service in South Korea. Multiple preprocessing steps were applied for each scan to normalize the image and perform intensity correction to create 3D voxels that represent these parts accurately. The accuracy of the proposed method was evaluated by 10-fold cross-validation. The initial cross-validation results illustrated the potential of the proposed method as it achieved a mean absolute error of 5.15 years with a concordance correlation coefficient of 0.80. The proposed approach is likely to be faster and potentially more reliable, which could be used for age assessment in the future.
format Online
Article
Text
id pubmed-8115850
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-81158502021-05-24 Age estimation based on 3D post-mortem computed tomography images of mandible and femur using convolutional neural networks Pham, Cuong Van Lee, Su-Jin Kim, So-Yeon Lee, Sookyoung Kim, Soo-Hyung Kim, Hyung-Seok PLoS One Research Article Age assessment has attracted increasing attention in the field of forensics. However, most existing works are laborious and requires domain-specific knowledge. Modern computing power makes it is possible to leverage massive amounts of data to produce more reliable results. Therefore, it is logical to use automated age estimation approaches to handle large datasets. In this study, a fully automated age prediction approach was proposed by assessing 3D mandible and femur scans using deep learning. A total of 814 post-mortem computed tomography scans from 619 men and 195 women, within the age range of 20–70, were collected from the National Forensic Service in South Korea. Multiple preprocessing steps were applied for each scan to normalize the image and perform intensity correction to create 3D voxels that represent these parts accurately. The accuracy of the proposed method was evaluated by 10-fold cross-validation. The initial cross-validation results illustrated the potential of the proposed method as it achieved a mean absolute error of 5.15 years with a concordance correlation coefficient of 0.80. The proposed approach is likely to be faster and potentially more reliable, which could be used for age assessment in the future. Public Library of Science 2021-05-12 /pmc/articles/PMC8115850/ /pubmed/33979376 http://dx.doi.org/10.1371/journal.pone.0251388 Text en © 2021 Pham et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Pham, Cuong Van
Lee, Su-Jin
Kim, So-Yeon
Lee, Sookyoung
Kim, Soo-Hyung
Kim, Hyung-Seok
Age estimation based on 3D post-mortem computed tomography images of mandible and femur using convolutional neural networks
title Age estimation based on 3D post-mortem computed tomography images of mandible and femur using convolutional neural networks
title_full Age estimation based on 3D post-mortem computed tomography images of mandible and femur using convolutional neural networks
title_fullStr Age estimation based on 3D post-mortem computed tomography images of mandible and femur using convolutional neural networks
title_full_unstemmed Age estimation based on 3D post-mortem computed tomography images of mandible and femur using convolutional neural networks
title_short Age estimation based on 3D post-mortem computed tomography images of mandible and femur using convolutional neural networks
title_sort age estimation based on 3d post-mortem computed tomography images of mandible and femur using convolutional neural networks
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8115850/
https://www.ncbi.nlm.nih.gov/pubmed/33979376
http://dx.doi.org/10.1371/journal.pone.0251388
work_keys_str_mv AT phamcuongvan ageestimationbasedon3dpostmortemcomputedtomographyimagesofmandibleandfemurusingconvolutionalneuralnetworks
AT leesujin ageestimationbasedon3dpostmortemcomputedtomographyimagesofmandibleandfemurusingconvolutionalneuralnetworks
AT kimsoyeon ageestimationbasedon3dpostmortemcomputedtomographyimagesofmandibleandfemurusingconvolutionalneuralnetworks
AT leesookyoung ageestimationbasedon3dpostmortemcomputedtomographyimagesofmandibleandfemurusingconvolutionalneuralnetworks
AT kimsoohyung ageestimationbasedon3dpostmortemcomputedtomographyimagesofmandibleandfemurusingconvolutionalneuralnetworks
AT kimhyungseok ageestimationbasedon3dpostmortemcomputedtomographyimagesofmandibleandfemurusingconvolutionalneuralnetworks