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Aging-related volume changes in the brain and cerebrospinal fluid using artificial intelligence-automated segmentation
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...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10511609/ https://www.ncbi.nlm.nih.gov/pubmed/37060450 http://dx.doi.org/10.1007/s00330-023-09632-x |
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author | Yamada, Shigeki Otani, Tomohiro Ii, Satoshi Kawano, Hiroto Nozaki, Kazuhiko Wada, Shigeo Oshima, Marie Watanabe, Yoshiyuki |
author_facet | Yamada, Shigeki Otani, Tomohiro Ii, Satoshi Kawano, Hiroto Nozaki, Kazuhiko Wada, Shigeo Oshima, Marie Watanabe, Yoshiyuki |
author_sort | Yamada, Shigeki |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10511609 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-105116092023-09-22 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 Eur Radiol Head and Neck 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. Springer Berlin Heidelberg 2023-04-15 2023 /pmc/articles/PMC10511609/ /pubmed/37060450 http://dx.doi.org/10.1007/s00330-023-09632-x Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Head and Neck Yamada, Shigeki Otani, Tomohiro Ii, Satoshi Kawano, Hiroto Nozaki, Kazuhiko Wada, Shigeo Oshima, Marie Watanabe, Yoshiyuki Aging-related volume changes in the brain and cerebrospinal fluid using artificial intelligence-automated segmentation |
title | Aging-related volume changes in the brain and cerebrospinal fluid using artificial intelligence-automated segmentation |
title_full | Aging-related volume changes in the brain and cerebrospinal fluid using artificial intelligence-automated segmentation |
title_fullStr | Aging-related volume changes in the brain and cerebrospinal fluid using artificial intelligence-automated segmentation |
title_full_unstemmed | Aging-related volume changes in the brain and cerebrospinal fluid using artificial intelligence-automated segmentation |
title_short | Aging-related volume changes in the brain and cerebrospinal fluid using artificial intelligence-automated segmentation |
title_sort | aging-related volume changes in the brain and cerebrospinal fluid using artificial intelligence-automated segmentation |
topic | Head and Neck |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10511609/ https://www.ncbi.nlm.nih.gov/pubmed/37060450 http://dx.doi.org/10.1007/s00330-023-09632-x |
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