干旱区生态环境知识资源中心(Arid): Exploring the Value of MRI Measurement of Hippocampal Volume for Predicting the Occurrence and Progression of Alzheimer's Disease Based on Artificial Intelligence Deep Learning Technology and Evidence-Based Medicine Meta-Analysis

Arid

DOI	10.3233/JAD-230733
	Exploring the Value of MRI Measurement of Hippocampal Volume for Predicting the Occurrence and Progression of Alzheimer's Disease Based on Artificial Intelligence Deep Learning Technology and Evidence-Based Medicine Meta-Analysis
	Zhou, Jianguo; Zhao, Mingli; Yang, Zhou; Chen, Liping; Liu, Xiaoli
通讯作者	Liu, XL
来源期刊	JOURNAL OF ALZHEIMERS DISEASE
ISSN	1387-2877
EISSN	1875-8908
出版年	2024
卷号	97 期号:3 页码:1275-1288
英文摘要	Background: Alzheimer's disease (AD), a major dementia cause, lacks effective treatment. MRI-based hippocampal volume measurement using artificial intelligence offers new insights into early diagnosis and intervention in AD progression. Objective: This study, involving 483 AD patients, 756 patients with mild cognitive impairment (MCI), and 968 normal controls (NC), investigated the predictive capability of MRI-based hippocampus volume measurements for AD risk using artificial intelligence and evidence-based medicine. Methods: Utilizing data from ADNI and OASIS-brains databases, three convolutional neural networks (InceptionResNetv2, Densenet169, and SEResNet50) were employed for automatedADclassification based on structuralMRIimaging. Amultitask deep learning model and a densely connected3Dconvolutional network were utilized. Additionally, a systematic meta-analysis explored the value of MRI-based hippocampal volume measurement in predicting AD occurrence and progression, drawing on 23 eligible articles from PubMed and Embase databases. Results: InceptionResNetv2 outperformed other networks, achieving 99.75% accuracy and 100% AUC for AD-NC classification and 99.16% accuracy and 100% AUC for MCI-NC classification. Notably, at a 512x512 size, InceptionResNetv2 demonstrated a classification accuracy of 94.29% and an AUC of 98% for AD-NC and 97.31% accuracy and 98% AUC for MCI-NC. Conclusions: The study concludes that MRI-based hippocampal volume changes effectively predict AD onset and progression, facilitating early intervention and prevention.
英文关键词	Alzheimer's disease artificial intelligence deep learning evidence-based medicine hippocampal volume magnetic resonance imaging
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001170912400022
WOS类目	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/2XILL650/404328
推荐引用方式 GB/T 7714	Zhou, Jianguo,Zhao, Mingli,Yang, Zhou,et al. Exploring the Value of MRI Measurement of Hippocampal Volume for Predicting the Occurrence and Progression of Alzheimer's Disease Based on Artificial Intelligence Deep Learning Technology and Evidence-Based Medicine Meta-Analysis[J],2024,97(3):1275-1288.
APA	Zhou, Jianguo,Zhao, Mingli,Yang, Zhou,Chen, Liping,&Liu, Xiaoli.(2024).Exploring the Value of MRI Measurement of Hippocampal Volume for Predicting the Occurrence and Progression of Alzheimer's Disease Based on Artificial Intelligence Deep Learning Technology and Evidence-Based Medicine Meta-Analysis.JOURNAL OF ALZHEIMERS DISEASE,97(3),1275-1288.
MLA	Zhou, Jianguo,et al."Exploring the Value of MRI Measurement of Hippocampal Volume for Predicting the Occurrence and Progression of Alzheimer's Disease Based on Artificial Intelligence Deep Learning Technology and Evidence-Based Medicine Meta-Analysis".JOURNAL OF ALZHEIMERS DISEASE 97.3(2024):1275-1288.