干旱区生态环境知识资源中心(Arid): Machine Learning for Diagnosis of AD and Prediction of MCI Progression From Brain MRI Using Brain Anatomical Analysis Using Diffeomorphic Deformation

Arid

DOI	10.3389/fneur.2020.576029
	Machine Learning for Diagnosis of AD and Prediction of MCI Progression From Brain MRI Using Brain Anatomical Analysis Using Diffeomorphic Deformation
	Syaifullah, Ali Haidar; Shiino, Akihiko; Kitahara, Hitoshi; Ito, Ryuta; Ishida, Manabu; Tanigaki, Kenji
通讯作者	Shiino, A (corresponding author), Shiga Univ Med Sci, Mol Neurosci Res Ctr, Otsu, Shiga, Japan.
来源期刊	FRONTIERS IN NEUROLOGY
ISSN	1664-2295
出版年	2021
卷号	11
英文摘要	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. 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. 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 A beta 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. 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.
英文关键词	artificial inteligence cognitive impairment Alzheheimer&apos s disease machine learning support vector machine magnetic resonance imaging ADNI
类型	Article
语种	英语
开放获取类型	Green Published, gold
收录类别	SCI-E
WOS记录号	WOS:000619463700001
WOS类目	Clinical Neurology ; Neurosciences
WOS研究方向	Neurosciences & Neurology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/350318
作者单位	[Syaifullah, Ali Haidar; Shiino, Akihiko] Shiga Univ Med Sci, Mol Neurosci Res Ctr, Otsu, Shiga, Japan; [Syaifullah, Ali Haidar] Shiga Univ Med Sci, Ctr Epidemiol Res Asia CERA, Otsu, Shiga, Japan; [Kitahara, Hitoshi; Ito, Ryuta] Shiga Univ Med Sci, Dept Radiol, Otsu, Shiga, Japan; [Ishida, Manabu] Shimane Univ, Dept Neurol, Matsue, Shimane, Japan; [Tanigaki, Kenji] Shiga Med Ctr, Res Inst, Moriyama, Shiga, Japan
推荐引用方式 GB/T 7714	Syaifullah, Ali Haidar,Shiino, Akihiko,Kitahara, Hitoshi,et al. Machine Learning for Diagnosis of AD and Prediction of MCI Progression From Brain MRI Using Brain Anatomical Analysis Using Diffeomorphic Deformation[J],2021,11.
APA	Syaifullah, Ali Haidar,Shiino, Akihiko,Kitahara, Hitoshi,Ito, Ryuta,Ishida, Manabu,&Tanigaki, Kenji.(2021).Machine Learning for Diagnosis of AD and Prediction of MCI Progression From Brain MRI Using Brain Anatomical Analysis Using Diffeomorphic Deformation.FRONTIERS IN NEUROLOGY,11.
MLA	Syaifullah, Ali Haidar,et al."Machine Learning for Diagnosis of AD and Prediction of MCI Progression From Brain MRI Using Brain Anatomical Analysis Using Diffeomorphic Deformation".FRONTIERS IN NEUROLOGY 11(2021).