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探索机器学习和磁共振成像在早期中风诊断中的潜力:一篇文献计量分析 (2004-2023)

Jian-Cheng Lou¹, Xiao-Fen Yu¹, Jian-Jun Ying¹

¹Yiwu Hospital of Traditional Chinese Medicine, Yiwu, China.

Frontiers in neurology

|March 31, 2025

概括

机器学习有助于使用磁共振成像进行早期中风诊断. 研究显示,2015年后的出版物激增,深度学习和神经成像作为中风预测的重点领域.

科学领域:

医疗成像医学成像
人工智能的人工智能
神经学神经学

背景情况:

脑卒中诊断在很大程度上依赖于神经成像的及时和准确的解释.
机器学习 (ML) 提供了增强早期中风检测和表征的潜力.
机器学习与磁共振成像 (MRI) 的整合是一个日益增长的研究领域.

研究的目的:

用MRI特征绘制早期中风诊断中ML的研究格局.
确定从2004年到2023年的关键趋势,有影响力的作品和新兴热点.

主要方法:

用Web of Science核心集合对2004-2023年出版物的图书法分析.
使用CiteSpace软件分析出版物,作者,机构和关键词.
数据检索和分析于2023年12月1日进行.

主要成果:

在2015年之后,人们观察到出版物的显著增加,这表明研究兴趣加快了.
美国和中国是中风ML研究领域的领先机构,在全球范围内有广泛的合作.
"深度学习"",磁共振成像"和"中风"等关键词是当前研究的核心.

结论:

使用神经成像用于早期中风预测的ML应用对个性化医学非常有价值.
目前的研究热点集中在优化神经成像标记物和选择适当的ML算法.
该领域展示了跨学科的整合和主要行业内的专业精细化.

关键词:

在 WoSCCCC 中.文献计量分析的分析机器学习是机器学习.磁共振成像技术的使用一次性中风中风中风中风中风

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