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...

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Autores principales:	Cuong Van Pham, Su-Jin Lee, So-Yeon Kim, Sookyoung Lee, Soo-Hyung Kim, Hyung-Seok Kim
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/98459b3665734ba5b6c9bc4e1f2a2d2f
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spelling	oai:doaj.org-article:98459b3665734ba5b6c9bc4e1f2a2d2f2021-12-02T20:05:39ZAge estimation based on 3D post-mortem computed tomography images of mandible and femur using convolutional neural networks.1932-620310.1371/journal.pone.0251388https://doaj.org/article/98459b3665734ba5b6c9bc4e1f2a2d2f2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0251388https://doaj.org/toc/1932-6203Age 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.Cuong Van PhamSu-Jin LeeSo-Yeon KimSookyoung LeeSoo-Hyung KimHyung-Seok KimPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 5, p e0251388 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Cuong Van Pham Su-Jin Lee So-Yeon Kim Sookyoung Lee Soo-Hyung Kim Hyung-Seok Kim Age estimation based on 3D post-mortem computed tomography images of mandible and femur using convolutional neural networks.
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	article
author	Cuong Van Pham Su-Jin Lee So-Yeon Kim Sookyoung Lee Soo-Hyung Kim Hyung-Seok Kim
author_facet	Cuong Van Pham Su-Jin Lee So-Yeon Kim Sookyoung Lee Soo-Hyung Kim Hyung-Seok Kim
author_sort	Cuong Van Pham
title	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_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_sort	age estimation based on 3d post-mortem computed tomography images of mandible and femur using convolutional neural networks.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/98459b3665734ba5b6c9bc4e1f2a2d2f
work_keys_str_mv	AT cuongvanpham ageestimationbasedon3dpostmortemcomputedtomographyimagesofmandibleandfemurusingconvolutionalneuralnetworks AT sujinlee ageestimationbasedon3dpostmortemcomputedtomographyimagesofmandibleandfemurusingconvolutionalneuralnetworks AT soyeonkim ageestimationbasedon3dpostmortemcomputedtomographyimagesofmandibleandfemurusingconvolutionalneuralnetworks AT sookyounglee ageestimationbasedon3dpostmortemcomputedtomographyimagesofmandibleandfemurusingconvolutionalneuralnetworks AT soohyungkim ageestimationbasedon3dpostmortemcomputedtomographyimagesofmandibleandfemurusingconvolutionalneuralnetworks AT hyungseokkim ageestimationbasedon3dpostmortemcomputedtomographyimagesofmandibleandfemurusingconvolutionalneuralnetworks
_version_	1718375439396765696

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

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