Age Estimation from Brain Magnetic Resonance Images Using Deep Learning Techniques in Extensive Age Range

Usui, Kousuke; Yoshimura, Takaaki; Tang, Minghui; Sugimori, Hiroyuki

Age Estimation from Brain Magnetic Resonance Images Using Deep Learning Techniques in Extensive Age Range

Usui, Kousuke; Yoshimura, Takaaki; Tang, Minghui et al.

Applied sciences, 2023, 13(3), 1753

Permalink : https://hdl.handle.net/2115/88583

このアイテムのアクセス数:235件（2026-01-23 22:57 集計）

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https://doi.org/10.3390/app13031753

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アクセス権	metadata only access
URI	https://hdl.handle.net/2115/88583
タイトル	Age Estimation from Brain Magnetic Resonance Images Using Deep Learning Techniques in Extensive Age Range
著者	Usui, Kousuke ; Yoshimura, Takaaki ; Tang, Minghui ; Sugimori, Hiroyuki
著者名 Usui, Kousuke
e-Rad_Researcher 70807742 著者名 Yoshimura, Takaaki
e-Rad_Researcher 80794156 著者名 Tang, Minghui
e-Rad_Researcher 20711899 著者名 Sugimori, Hiroyuki
言語	英語
キーワード	deep learning
	age estimation
	regression model
	machine learning
	ResNet-50
発行日	2023-01-30
出版者	MDPI
収録物名	Applied sciences
巻(号)	13(3)
ページ	1753
抄録	Estimation of human age is important in the fields of forensic medicine and the detection of neurodegenerative diseases of the brain. Particularly, the age estimation methods using brain magnetic resonance (MR) images are greatly significant because these methods not only are noninvasive but also do not lead to radiation exposure. Although several age estimation methods using brain MR images have already been investigated using deep learning, there are no reports involving younger subjects such as children. This study investigated the age estimation method using T1-weighted (sagittal plane) two-dimensional brain MR imaging (MRI) of 1000 subjects aged 5-79 (31.64 +/- 18.04) years. This method uses a regression model based on ResNet-50, which estimates the chronological age (CA) of unknown brain MR images by training brain MR images corresponding to the CA. The correlation coefficient, coefficient of determination, mean absolute error, and root mean squared error were used as the evaluation indices of this model, and the results were 0.9643, 0.9299, 5.251, and 6.422, respectively. The present study showed the same degree of correlation as those of related studies, demonstrating that age estimation can be performed for a wide range of ages with higher estimation accuracy.
資源タイプ	学術雑誌論文
出版タイプ	Version of Record
EISSN	2076-3417
関連情報 (isIdenticalTo)	出版社版DOI https://doi.org/10.3390/app13031753