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最多引用的100篇关于人工智能在阿尔茨海默氏症和轻度认知障碍中的文章:一个参考文献分析

Zhi Tao¹, Rui Zhou¹, Yinggang Zheng¹

¹Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Department of Anesthesiology and Perioperative Medicine, Clinical Research Center for Anesthesiology and Perioperative Medicine, Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People's Hospital, School of Medicine, Tongji University, Shanghai, China.

Frontiers in aging neuroscience

|January 28, 2026

概括

人工智能 (AI) 越来越多地用于阿尔茨海默病 (AD) 和轻度认知障碍 (MCI) 研究. 对最受引用的论文的图书统计分析显示,使用MRI数据进行诊断和预测,支持矢量机 (SVM) 和卷积神经网络 (CNN) 是重点关注点.

科学领域:

图书统计学图书统计学
人工智能的人工智能
神经科学是一个神经科学.
医疗信息学医疗信息学

背景情况:

阿尔茨海默氏症 (AD) 和轻度认知障碍 (MCI) 代表着一个重大的全球健康挑战.
人工智能 (AI) 方法越来越多地被用于解决AD和MCI研究中的挑战.
对100篇关于人工智能在AD和MCI中的应用最常被引用的文章进行了参考文献分析.

研究的目的:

系统地分析高度引用的AI在阿尔茨海默氏症和轻度认知障碍方面的研究.
确定这一领域的关键趋势,有影响力的作者,机构,国家,期刊和人工智能方法.
了解人工智能驱动的AD/MCI研究中使用的主要研究重点和数据模式.

主要方法:

通过使用有关AD,MCI和AI的关键词对Web of Science数据库进行了全面的搜索.
确定了最多引用的前100篇文章,并进行了手动选.
数据提取包括出版物细节,作者信息,原产国/机构,关键词,AI方法和临床数据类型. 分析使用VOSviewer进行.

主要成果:

大多数前100名文章 (85%) 是临床研究,77%专注于诊断和预测.
磁共振成像 (MRI) 是最常用的数据来源.
支持向量机 (SVM) 和卷积神经网络 (CNN) 是最普遍的AI方法,其中"整体学习"和"转移学习"是新兴的关键词.

结论:

该分析强调了人工智能在诊断和预测AD/MCI方面的重要关注,特别是使用MRI数据.
在这个研究领域,SVM和CNN是主要的AI技术.
这些发现提供了关于AI在AD和MCI研究中的当前状态和未来方向的见解.

关键词:

阿尔茨海默氏症是阿尔茨海默氏症的一种疾病.人工智能的人工智能是人工智能.文献计量分析的分析机器学习是机器学习.轻度的认知障碍轻度的认知障碍在前100名中排名第一.

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