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Machine learning of brain structural biomarkers for Alzheimer's disease (AD) diagnosis, prediction of disease progression, and amyloid beta deposition in the Japanese population
http://hdl.handle.net/10422/00013376
http://hdl.handle.net/10422/000133762c4e9e7f-71ec-4170-9b39-384d2ffa9d53
名前 / ファイル | ライセンス | アクション |
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dad2.12246 (619.6 kB)
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This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations aremade.
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Item type | 学術雑誌論文 / Journal Article(1) | |||||
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公開日 | 2022-07-21 | |||||
タイトル | ||||||
タイトル | Machine learning of brain structural biomarkers for Alzheimer's disease (AD) diagnosis, prediction of disease progression, and amyloid beta deposition in the Japanese population | |||||
言語 | ||||||
言語 | eng | |||||
キーワード | ||||||
言語 | en | |||||
主題Scheme | Other | |||||
主題 | ADNI | |||||
キーワード | ||||||
言語 | en | |||||
主題Scheme | Other | |||||
主題 | Alzheimer's disease | |||||
キーワード | ||||||
言語 | en | |||||
主題Scheme | Other | |||||
主題 | artificial intelligence | |||||
キーワード | ||||||
言語 | en | |||||
主題Scheme | Other | |||||
主題 | machine learning | |||||
キーワード | ||||||
言語 | en | |||||
主題Scheme | Other | |||||
主題 | MRI | |||||
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 | |||||
著者 |
SHIINO, Akihiko
× SHIINO, Akihiko× SHIRAKASHI, Yoshitomo× ISHIDA, Manabu× TANIGAKI, Kenji |
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著者別名 |
椎野, 顯彦
× 椎野, 顯彦 |
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抄録 | ||||||
内容記述タイプ | Abstract | |||||
内容記述 | Introduction: We developed machine learning (ML) designed to analyze structural brain magnetic resonance imaging (MRI), and trained it on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. In this study, we verified its utility in the Japanese population. |
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抄録 | ||||||
内容記述タイプ | Abstract | |||||
内容記述 | Methods: A total of 535 participants were enrolled from the Japanese ADNI database, including 148 AD, 152 normal, and 235 mild cognitive impairment (MCI). Probability of AD was expressed as AD likelihood scores (ADLS). |
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抄録 | ||||||
内容記述タイプ | Abstract | |||||
内容記述 | Results: The accuracy of AD diagnosis was 88.0% to 91.2%. The accuracy of predicting the disease progression in non-dementia participants over a 3-year observation was 76.0% to 79.3%. More than 90% of the participants with low ADLS did not progress to AD within 3 years. In the amyloid positron emission tomography (PET)-positive MCI, the hazard ratio of progression was 2.39 with low ADLS, and 5.77 with high ADLS. When high ADLS was defined as N+ and Pittsburgh compound B (PiB) PET positivity was defined as A+, the time to disease progression for 50% of MCI participants was 23.7 months in A+N+, whereas it was 52.3 months in A+N-. |
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抄録 | ||||||
内容記述タイプ | Abstract | |||||
内容記述 | Conclusion: These results support the feasibility of our ML for the diagnosis of AD and prediction of the disease progression. |
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書誌情報 |
en : Alzheimer's and Dementia: Diagnosis, Assessment and Disease Monitoring 巻 13, 号 1, 発行日 2021-10-14 |
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出版者 | ||||||
出版者 | John Wiley and Sons Inc | |||||
ISSN | ||||||
収録物識別子タイプ | ISSN | |||||
収録物識別子 | 2352-8729 | |||||
PMID | ||||||
関連タイプ | isIdenticalTo | |||||
識別子タイプ | PMID | |||||
関連識別子 | 34692983 | |||||
PMCID | ||||||
識別子タイプ | URI | |||||
関連識別子 | http://www.ncbi.nlm.nih.gov/pmc/articles/pmc8515359/ | |||||
関連名称 | PMC8515359 | |||||
DOI | ||||||
関連タイプ | isIdenticalTo | |||||
識別子タイプ | DOI | |||||
関連識別子 | https://doi.org/10.1002/dad2.12246 | |||||
関連名称 | 10.1002/dad2.12246 | |||||
権利 | ||||||
権利情報 | © 2021 The Authors. | |||||
権利 | ||||||
権利情報 | Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring published by Wiley Periodicals, LLC on behalf of Alzheimer's Association. | |||||
フォーマット | ||||||
内容記述タイプ | Other | |||||
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著者版フラグ | ||||||
出版タイプ | VoR | |||||
出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||
資源タイプ | ||||||
内容記述タイプ | Other | |||||
内容記述 | Journal Article |