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Machine Learning for Diagnosis of AD and Prediction of MCI Progression From Brain MRI Using Brain Anatomical Analysis Using Diffeomorphic Deformation.
http://hdl.handle.net/10422/00012920
http://hdl.handle.net/10422/00012920930e451f-c9a8-46ce-8575-8749cab435c0
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
fneur.2020.576029 (3.1 MB)
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This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
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| Item type | 学術雑誌論文 / Journal Article(1) | |||||
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| 公開日 | 2021-03-11 | |||||
| タイトル | ||||||
| タイトル | Machine Learning for Diagnosis of AD and Prediction of MCI Progression From Brain MRI Using Brain Anatomical Analysis Using Diffeomorphic Deformation. | |||||
| 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 | Alzheheimer's disease | |||||
| キーワード | ||||||
| 言語 | en | |||||
| 主題Scheme | Other | |||||
| 主題 | artificial inteligence | |||||
| キーワード | ||||||
| 言語 | en | |||||
| 主題Scheme | Other | |||||
| 主題 | cognitive impairment | |||||
| 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 | magnetic resonance imaging; support vector machine | |||||
| 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 |
SYAIFULLAH, Ali Haidar
× SYAIFULLAH, Ali Haidar× SHIINO, Akihiko× KITAHARA, Hitoshi× ITOH, Ryuta× ISHIDA, Manabu× TANIGAKI, Kenji |
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| 著者別名 |
椎野, 顯彦
× 椎野, 顯彦× 北原, 均× 井藤, 隆太 |
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| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | Background: With the growing momentum for the adoption of machine learning (ML) in medical field, it is likely that reliance on ML for imaging will become routine over the next few years. We have developed a software named BAAD, which uses ML algorithms for the diagnosis of Alzheimer's disease (AD) and prediction of mild cognitive impairment (MCI) progression. |
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| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | Methods: We constructed an algorithm by combining a support vector machine (SVM) to classify and a voxel-based morphometry (VBM) to reduce concerned variables. We grouped progressive MCI and AD as an AD spectrum and trained SVM according to this classification. We randomly selected half from the total 1,314 subjects of AD neuroimaging Initiative (ADNI) from North America for SVM training, and the remaining half were used for validation to fine-tune the model hyperparameters. We created two types of SVMs, one based solely on the brain structure (SVMst), and the other based on both the brain structure and Mini-Mental State Examination score (SVMcog). We compared the model performance with two expert neuroradiologists, and further evaluated it in test datasets involving 519, 592, 69, and 128 subjects from the Australian Imaging, Biomarker & Lifestyle Flagship Study of Aging (AIBL), Japanese ADNI, the Minimal Interval Resonance Imaging in AD (MIDIAD) and the Open Access Series of Imaging Studies (OASIS), respectively. |
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| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | Results: BAAD's SVMs outperformed radiologists for AD diagnosis in a structural magnetic resonance imaging review. The accuracy of the two radiologists was 57.5 and 70.0%, respectively, whereas, that of the SVMst was 90.5%. The diagnostic accuracy of the SVMst and SVMcog in the test datasets ranged from 88.0 to 97.1% and 92.5 to 100%, respectively. The prediction accuracy for MCI progression was 83.0% in SVMst and 85.0% in SVMcog. In the AD spectrum classified by SVMst, 87.1% of the subjects were Aβ positive according to an AV-45 positron emission tomography. Similarly, among MCI patients classified for the AD spectrum, 89.5% of the subjects progressed to AD. |
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| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | Conclusion: Our ML has shown high performance in AD diagnosis and prediction of MCI progression. It outperformed expert radiologists, and is expected to provide support in clinical practice. |
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| 書誌情報 |
en : Frontiers in Neurology 巻 11, 発行日 2021-02-05 |
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| 出版者 | ||||||
| 出版者 | Frontiers Media S.A. | |||||
| ISSN | ||||||
| 収録物識別子タイプ | ISSN | |||||
| 収録物識別子 | 16642295 | |||||
| PMID | ||||||
| 関連タイプ | isIdenticalTo | |||||
| 識別子タイプ | PMID | |||||
| 関連識別子 | 33613411 | |||||
| PMCID | ||||||
| 識別子タイプ | URI | |||||
| 関連識別子 | http://www.ncbi.nlm.nih.gov/pmc/articles/pmc7893082/ | |||||
| 関連名称 | PMC7893082 | |||||
| DOI | ||||||
| 関連タイプ | isIdenticalTo | |||||
| 識別子タイプ | DOI | |||||
| 関連識別子 | https://doi.org/10.3389/fneur.2020.576029 | |||||
| 関連名称 | 10.3389/fneur.2020.576029 | |||||
| 権利 | ||||||
| 権利情報 | © 2021 Syaifullah, Shiino, Kitahara, Ito, Ishida and Tanigaki. | |||||
| フォーマット | ||||||
| 内容記述タイプ | Other | |||||
| 内容記述 | ||||||
| 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 | |||||
