| 左安琪，庄贺，孔畅，禚月.人工智能应用于帕金森病研究的文献计量学分析[J].神经损伤功能重建,2025,(知网首发): |
| 人工智能应用于帕金森病研究的文献计量学分析 |
| Bibliometric Analysis of Artificial Intelligence Applied to Parkinson's Disease Research |
| |
| DOI： |
| 中文关键词: 人工智能 帕金森病 文献计量学分析 |
| 英文关键词: artificial intelligence Parkinson's disease bibliometric analysis |
| 基金项目: |
|
| 摘要点击次数: 16 |
| 全文下载次数: 24 |
| 中文摘要: |
| 目的：通过文献计量学分析，对人工智能应用于帕金森病研究的相关文献进行可视化分析。方法：以
Web Of Science核心数据库为本文数据来源。方法采用CiteSpace 6.3.R1和VOSviewer1.6.20对年发文量、
国家/地区、机构、关键词、作者、被引文献等进行文献计量分析。结果：最终纳入符合标准的文献2 043篇，
该领域发文量呈快速增长趋势。共有87个国家/地区发表过该研究领域文章。其中美国、中国、意大利为高
产国家，哈佛医学院为领先机构。发文量排名前三的作者为Gabriella Olmo、Alice Nieuwboer、Lynn Rochester。该领域高频关键词主要有帕金森病、深部脑刺激、步态等；最新突现词为康复。AI应用于PD领域主要
分布于神经科学、工程电气、临床神经学等领域，论文发表期刊中 SENSORS 发文量和 H 指数位居第一，
MOVEMENT DISORDERS的IF最高。共被引频次最高的文献主要研究内容是将脑电图信号与AI技术相
结合进行PD的早期诊断。结论：近年来，人工智能在医疗领域发展迅速，其应用于PD的研究逐年增多，主
要集中于对PD的危险因素预测、诊断和治疗。 |
| 英文摘要: |
| To conduct a visual analysis of relevant literature on the application of artificial
intelligence in Parkinson's disease research through bibliometric analysis. Methods: The Web Of Science core
database was used as the data source for this paper. CiteSpace 6.3.R1 and VOSviewer1.6.20 software were used
to conduct bibliometric analyses on the annual number of publications, country/region, institution, keywords,
authors and cited literature. Results: Ultimately, 2 043 articles meeting the criteria were included, and the
number of publications in this field has been growing rapidly. Articles in this research area have been published
by a total of 87 countries/regions. Among them, the United States, China, and Italy are high-yield countries, with
Harvard Medical School being the leading institution. The top three authors in terms of publication volume are
Gabriella Olmo, Alice Nieuwboer, and Lynn Rochester. High-frequency keywords in this field mainly include
Parkinson's disease, deep brain stimulation, and gait, etc.; the latest emerging term is rehabilitation. The
application of AI in the field of Parkinson's disease (PD) is mainly distributed in neuroscience, electrical
engineering, clinical neurology, and other fields. Among the journals where papers are published, SENSORS
ranks first in both publication volume and H-index, while MOVEMENT DISORDERS has the highest impact
factor (IF). The most frequently co-cited literature primarily focuses on the integration of electroencephalogram
(EEG) signals with AI technology for the early diagnosis of PD. Conclusion: In recent years, AI has been
developing rapidly in the medical field, and its application to PD has been increasing year by year, mainly
focusing on the prediction of risk factors, diagnosis and treatment of PD. |
|
查看全文
查看/发表评论 下载PDF阅读器 |
| 关闭 |
|
|
|