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Aging-related volume changes in the brain and cerebrospinal fluid using artificial intelligence-automated segmentation.
Yamada, Shigeki; Otani, Tomohiro; Ii, Satoshi; Kawano, Hiroto; Nozaki, Kazuhiko; Wada, Shigeo; Oshima, Marie; Watanabe, Yoshiyuki.
Afiliación
  • Yamada S; Department of Neurosurgery, Nagoya City University Graduate School of Medical Science, 1 Kawasumi, Mizuho-cho, Mizuho-ku, NagoyaNagoya, Aichi, 467-8601, Japan. shigekiyamada393@gmail.com.
  • Otani T; Interfaculty Initiative in Information Studies / Institute of Industrial Science, The University of Tokyo, Tokyo, Japan. shigekiyamada393@gmail.com.
  • Ii S; Department of Neurosurgery, Shiga University of Medical Science, Otsu, Shiga, Japan. shigekiyamada393@gmail.com.
  • Kawano H; Department of Mechanical Science and Bioengineering, Graduate School of Engineering Science, Osaka University, Osaka, Japan.
  • Nozaki K; Faculty of System Design, Tokyo Metropolitan University, Hachioji, Tokyo, Japan.
  • Wada S; Department of Neurosurgery, Shiga University of Medical Science, Otsu, Shiga, Japan.
  • Oshima M; Department of Neurosurgery, Shiga University of Medical Science, Otsu, Shiga, Japan.
  • Watanabe Y; Department of Mechanical Science and Bioengineering, Graduate School of Engineering Science, Osaka University, Osaka, Japan.
Eur Radiol ; 33(10): 7099-7112, 2023 Oct.
Article en En | MEDLINE | ID: mdl-37060450
ABSTRACT

OBJECTIVES:

To verify the reliability of the volumes automatically segmented using a new artificial intelligence (AI)-based application and evaluate changes in the brain and CSF volume with healthy aging.

METHODS:

The intracranial spaces were automatically segmented in the 21 brain subregions and 5 CSF subregions using the AI-based application on the 3D T1-weighted images in healthy volunteers aged > 20 years. Additionally, the automatically segmented volumes of the total ventricles and subarachnoid spaces were compared with the manually segmented volumes of those extracted from 3D T2-weighted images using the intra-class correlation and Bland-Altman analysis.

RESULTS:

In this study, 133 healthy volunteers aged 21-92 years were included. The mean intra-class correlations between the automatically and manually segmented volumes of the total ventricles and subarachnoid spaces were 0.986 and 0.882, respectively. The increase in the CSF volume was estimated to be approximately 30 mL (2%) per decade from 265 mL (18.7%) in the 20s to 488 mL (33.7%) in ages above 80 years; however, the increase in the volume of total ventricles was approximately 20 mL (< 2%) until the 60s and increased in ages above 60 years.

CONCLUSIONS:

This study confirmed the reliability of the CSF volumes using the AI-based auto-segmentation application. The intracranial CSF volume increased linearly because of the brain volume reduction with aging; however, the ventricular volume did not change until the age of 60 years and above and then gradually increased. This finding could help elucidate the pathogenesis of chronic hydrocephalus in adults. KEY POINTS • The brain and CSF spaces were automatically segmented using an artificial intelligence-based application. • The total subarachnoid spaces increased linearly with aging, whereas the total ventricle volume was around 20 mL (< 2%) until the 60s and increased in ages above 60 years. • The cortical gray matter gradually decreases with aging, whereas the subcortical gray matter maintains its volume, and the cerebral white matter increases slightly until the 40s and begins to decrease from the 50s.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Inteligencia Artificial / Imagen por Resonancia Magnética Límite: Adult / Humans Idioma: En Revista: Eur Radiol Asunto de la revista: RADIOLOGIA Año: 2023 Tipo del documento: Article País de afiliación: Japón

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Inteligencia Artificial / Imagen por Resonancia Magnética Límite: Adult / Humans Idioma: En Revista: Eur Radiol Asunto de la revista: RADIOLOGIA Año: 2023 Tipo del documento: Article País de afiliación: Japón