干旱区生态环境知识资源中心(Arid): Early diagnosis of Alzheimer's disease using machine learning: a multi-diagnostic, generalizable approach

Arid

DOI	10.1186/s13195-022-01047-y
	Early diagnosis of Alzheimer's disease using machine learning: a multi-diagnostic, generalizable approach
	Diogo, Vasco Sa; Ferreira, Hugo Alexandre; Prata, Diana
通讯作者	Diogo, VS ; Prata, D
来源期刊	ALZHEIMERS RESEARCH & THERAPY
EISSN	1758-9193
出版年	2022
卷号	14 期号:1
英文摘要	Background: Early and accurate diagnosis of Alzheimer's disease (AD) is essential for disease management and therapeutic choices that can delay disease progression. Machine learning (ML) approaches have been extensively used in attempts to develop algorithms for reliable early diagnosis of AD, although clinical usefulness, interpretability, and generalizability of the classifiers across datasets and MRI protocols remain limited. Methods: We report a multi-diagnostic and generalizable approach for mild cognitive impairment (MCI) and AD diagnosis using structural MRI and ML. Classifiers were trained and tested using subjects from the AD Neuroimaging Initiative (ADNI) database (n = 570) and the Open Access Series of Imaging Studies (OASIS) project database (n = 531). Several classifiers are compared and combined using voting for a decision. Additionally, we report tests of generalizability across datasets and protocols (IR-SPGR and MPRAGE), the impact of using graph theory measures on diagnostic classification performance, the relative importance of different brain regions on classification for better interpretability, and an evaluation of the potential for clinical applicability of the classifier. Results: Our healthy controls (HC) vs. AD classifier trained and tested on the combination of ADNI and OASIS datasets obtained a balanced accuracy (BAC) of 90.6% and a Matthew's correlation coefficient (MCC) of 0.811. Our HC vs. MCI vs. AD classifier trained and tested on the ADNI dataset obtained a 62.1% BAC (33.3% being the by-chance cut-off) and 0.438 MCC. Hippocampal features were the strongest contributors to the classification decisions (approx. 25-45%), followed by temporal (approx. 13%), cingulate, and frontal regions (approx. 8-13% each), which is consistent with our current understanding of AD and its progression. Classifiers generalized well across both datasets and protocols. Finally, using graph theory measures did not improve classification performance. Conclusions: In sum, we present a diagnostic tool for MCI and AD trained using baseline scans and a follow-up diagnosis regardless of progression, which is multi-diagnostic, generalizable across independent data sources and acquisition protocols, and with transparently reported performance. Rated as potentially clinically applicable, our tool may be clinically useful to inform diagnostic decisions in dementia, if successful in real-world prospective clinical trials.
英文关键词	Alzheimer's disease Mild cognitive impairment Dementia Early diagnosis Prognosis Classification Machine learning Graph theory
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:000835701700002
WOS关键词	MILD COGNITIVE IMPAIRMENT ; BRAIN ATROPHY ; MRI ; BIOMARKERS ; NETWORKS ; PATTERNS ; MCI ; CLASSIFICATION ; SEGMENTATION ; PROGRESSION
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/2XILL650/391805
推荐引用方式 GB/T 7714	Diogo, Vasco Sa,Ferreira, Hugo Alexandre,Prata, Diana. Early diagnosis of Alzheimer's disease using machine learning: a multi-diagnostic, generalizable approach[J],2022,14(1).
APA	Diogo, Vasco Sa,Ferreira, Hugo Alexandre,&Prata, Diana.(2022).Early diagnosis of Alzheimer's disease using machine learning: a multi-diagnostic, generalizable approach.ALZHEIMERS RESEARCH & THERAPY,14(1).
MLA	Diogo, Vasco Sa,et al."Early diagnosis of Alzheimer's disease using machine learning: a multi-diagnostic, generalizable approach".ALZHEIMERS RESEARCH & THERAPY 14.1(2022).