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Development of a deep-learning algorithm for age estimation on CT images of the vertebral column

https://hiroshima.repo.nii.ac.jp/records/2007642
https://hiroshima.repo.nii.ac.jp/records/2007642
06358f3e-e9d0-4140-9550-fc6baccf6f09
名前 / ファイル ライセンス アクション
LegalMed_69_102444.pdf LegalMed_69_102444.pdf (5.3 MB)
Item type デフォルトアイテムタイプ_(フル)(1)
公開日 2024-12-16
タイトル
タイトル Development of a deep-learning algorithm for age estimation on CT images of the vertebral column
言語 en
作成者 Kawashita, Ikuo

× Kawashita, Ikuo

en Kawashita, Ikuo

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Fukumoto, Wataru

× Fukumoto, Wataru

en Fukumoto, Wataru

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Mitani, Hidenori

× Mitani, Hidenori

en Mitani, Hidenori

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Narita, Keigo

× Narita, Keigo

en Narita, Keigo

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Chosa, Keigo

× Chosa, Keigo

en Chosa, Keigo

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Nakamura, Yuko

× Nakamura, Yuko

en Nakamura, Yuko

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Nagao, Masataka

× Nagao, Masataka

en Nagao, Masataka

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Awai, Kazuo

× Awai, Kazuo

en Awai, Kazuo

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アクセス権
アクセス権 open access
アクセス権URI http://purl.org/coar/access_right/c_abf2
権利情報
言語 en
権利情報 © 2024. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/
権利情報
言語 en
権利情報 This is not the published version. Please cite only the published version.
権利情報
言語 ja
権利情報 この論文は出版社版ではありません。引用の際には出版社版をご確認、ご利用ください。
主題
言語 en
主題Scheme Other
主題 Cadaver
主題
言語 en
主題Scheme Other
主題 Deep learning
主題
言語 en
主題Scheme Other
主題 CT
主題
言語 en
主題Scheme Other
主題 Spine
内容記述
内容記述タイプ Abstract
内容記述 Purpose The accurate age estimation of cadavers is essential for their identification. However, conventional methods fail to yield adequate age estimation especially in elderly cadavers. We developed a deep learning algorithm for age estimation on CT images of the vertebral column and checked its accuracy. Method For the development of our deep learning algorithm, we included 1,120 CT data of the vertebral column of 140 patients for each of 8 age decades. The deep learning model of regression analysis based on Visual Geometry Group-16 (VGG16) was improved in its estimation accuracy by bagging. To verify its accuracy, we applied our deep learning algorithm to estimate the age of 219 cadavers who had undergone postmortem CT (PMCT). The mean difference and the mean absolute error (MAE), the standard error of the estimate (SEE) between the known- and the estimated age, were calculated. Correlation analysis using the intraclass correlation coefficient (ICC) and Bland-Altman analysis were performed to assess differences between the known- and the estimated age. Results For the 219 cadavers, the mean difference between the known- and the estimated age was 0.30 years; it was 4.36 years for the MAE, and 5.48 years for the SEE. The ICC (2,1) was 0.96 (95 % confidence interval: 0.95–0.97, p < 0.001). Bland-Altman analysis showed that there were no proportional or fixed errors (p = 0.08 and 0.41). Conclusions Our deep learning algorithm for estimating the age of 219 cadavers on CT images of the vertebral column was more accurate than conventional methods and highly useful.
言語 en
出版者
出版者 Elsevier
言語 en
言語
言語 eng
資源タイプ
資源タイプ識別子 http://purl.org/coar/resource_type/c_6501
資源タイプ journal article
出版タイプ
出版タイプ AM
出版タイプResource http://purl.org/coar/version/c_ab4af688f83e57aa
関連情報
関連タイプ isVersionOf
識別子タイプ DOI
関連識別子 https://doi.org/10.1016/j.legalmed.2024.102444
開始ページ
開始ページ 102444
書誌情報 en : Legal Medicine

巻 69, p. 102444, 発行日 2024-04-07
旧ID 55856
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