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朱鹤安,陈梦池,梁莹莹,廖世广,黄宏远,黄巧娟,刘江华.人工智能应用于心血管影像的文献计量学分析[J].广西科学,2025,32(2):399-412. [点击复制]
- ZHU He'an,CHEN Mengchi,LIANG Yingying,LIAO Shiguang,HUANG Hongyuan,HUANG Qiaojuan,LIU Jianghua.Bibliometric Analysis of Application of Artificial Intelligence in Cardiovascular Imaging[J].Guangxi Sciences,2025,32(2):399-412. [点击复制]
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| 人工智能应用于心血管影像的文献计量学分析 |
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朱鹤安1, 陈梦池2, 梁莹莹3, 廖世广4, 黄宏远3, 黄巧娟3, 刘江华3
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| (1.南宁市青秀区伶俐镇卫生院, 广西 南宁 530211;2.中山大学第三附属医院, 广东 广州 510000;3.广西医科大学第二附属医院, 广西 南宁 530005;4.广西壮族自治区人民医院, 广西 南宁 530021) |
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| 摘要: |
| 为分析人工智能(Artificial Intelligence，AI)应用于心血管影像的研究现状、热点及演化趋势，本研究采用文献计量学方法和CiteSpace 6.3.R1软件，整理2000年1月1日至2024年9月12日在Web of Science(WOS)核心合集数据库发表的与AI应用于心血管影像相关的文献，并进行可视化分析。结果表明，AI应用于心血管影像领域研究自2020年开始大规模增长，近4年发文量占总发文量的66.00%，预计未来发文量还将持续增加，反映出该领域正处于快速发展阶段。该领域发文量最多的国家、机构、作者分别为美国、伦敦国王学院、Damini Dey。Ronneberger Olaf是共被引频次最高的作者，同时也是共被引频次最高、突现强度最强的文献“U-Net:convolutional networks for biomedical image segmentation”的作者。此外，IEEE Transactions on Medical Imaging、Medical Image Analysis、JACC-Cardiovascular Imaging在发文量排名及期刊共被引排名中均位列前10，表明他们是该领域推动知识传播与学术交流的重要学术期刊。“机器学习”和“深度学习”是目前的研究热点，该领域关键词在进行聚类分析后可以进一步细分为3大研究领域——人工智能技术、心脏影像工具和心脏疾病。人工智能在超声心动图、电子计算机断层扫描(Computed Tomography，CT)和磁共振成像(Magnetic Resonance Imaging，MRI)中的应用提升了心血管疾病的诊断准确性和效率。如何进一步通过结合AI以提高心血管影像技术对心血管疾病识别及评估的准确性、便捷性和效率是未来该领域的研究重点。 |
| 关键词: 人工智能 心血管影像 文献计量学分析 VOSviewer CiteSpace |
| DOI：10.13656/j.cnki.gxkx.20250624.020 |
| 投稿时间：2024-07-04修订日期：2024-09-25 |
| 基金项目:区域高发疾病研究联合专项“环状RNAcirc_HECTD1靶向miRNA-142-3p调控细胞凋亡参与缺血性心肌重构的作用研究”(2023GXNSFAA026202);2023年度广西学位与研究生教改课题(JGY2023072);健康与经济社会发展研究中心2024年开放课题(2024RWB14)资助。 |
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| Bibliometric Analysis of Application of Artificial Intelligence in Cardiovascular Imaging |
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ZHU He'an1, CHEN Mengchi2, LIANG Yingying3, LIAO Shiguang4, HUANG Hongyuan3, HUANG Qiaojuan3, LIU Jianghua3
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| (1.Nanning Qingxiu District Lingli Town Health Center, Nanning, Guangxi, 530211, China;2.The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, 510000, China;3.The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, 530005, China;4.Guangxi Zhuang Autonomous Region People's Hospital, Nanning, Guangxi, 530021, China) |
| Abstract: |
| A bibliometric approach was employed to analyze the current research, hotspots, and future trends of the application of Artificial Intelligence (AI) in cardiovascular imaging based on CiteSpace 6.3.R1. The research literature about the application of AI in cardiovascular imaging was retrieved from Web of Science (WOS) with the time interval from 1 January 2000 to 12 September 2024, and visual analysis was performed.The results revealed that research in this field had undergone significant growth since 2020.Specifically,the number of publications in the past 4 years accounted for 66.00% of the total.It was expected that the number of publications would keep rising in the future,reflecting the rapid development of this field.In terms of the number of publications,the United States,King's College London,and Damini Dey were the leading country,institution,and author,respectively.Furthermore,the academic influence analysis demonstrated that the author with the most co-citations was Ronneberger Olaf,who was also the author of “U-Net:convolutional networks for biomedical image segmentation”,the reference with the most co-citations and the strongest citation bursts.Additionally,IEEE Transactions on Medical Imaging,Medical Image Analysis,and JACC-Cardiovascular Imaging ranked among the top ten in terms of both the number of publications and journal co-citations,which underscored their critical role in advancing knowledge dissemination and fostering academic exchanges in this field.Machine learning and deep learning were current research hotspots.The keyword cluster analysis showed that the keywords in this field were classified into three major research areas:AI technology,heart imaging tools,and heart diseases.The application of AI in echocardiography,Computed Tomography(CT),and Magnetic Resonance Imaging (MRI) significantly improved the diagnostic accuracy and efficiency of cardiovascular diseases.How to improve the accuracy,convenience,and efficiency of cardiovascular imaging for the identification and evaluation of cardiovascular diseases by combining AI is the focus of future research. |
| Key words: artificial intelligence cardiovascular imaging bibliometric analysis VOSviewer CiteSpace |
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