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非侵入性成像在心肌梗死中的应用:2003年1月至2022年12月的文献计量分析

Application of non-invasive imaging in myocardial infarction: a bibliometric analysis from January 2003 to December 2022.

作者信息

Yang Pei, Wu Xinwen, Zhao Yaya, Ye Qingyou, Hu Mengyao, Leng Yinping, Xiao Xuan, Zhang Jiahui, Ren Haibo, Gong Lianggeng

机构信息

Department of Radiology, the Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China.

Jiangxi Provincial Key Laboratory of Intelligent Medical Imaging, Nanchang, China.

出版信息

Quant Imaging Med Surg. 2025 Jul 1;15(7):6340-6359. doi: 10.21037/qims-24-878. Epub 2025 Jun 30.


DOI:10.21037/qims-24-878
PMID:40727351
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12290736/
Abstract

BACKGROUND: Myocardial infarction (MI) is a leading cause of death and disability worldwide. Non-invasive cardiac imaging has garnered significant attention in both MI diagnostic and prognostic research. Despite the growing body of published articles, no comprehensive, quantitative analysis has been conducted to delineate key trends and emerging areas in the field of non-invasive imaging for MI. This bibliometric analysis aimed to systematically evaluate the landscape of non-invasive cardiac imaging-focused research on MI in terms of the research status, publication patterns, influential contributors, hotspots, and focal development trends. METHODS: We systematically searched the Web of Science Core Collection (WoSCC) to retrieve publications related to MI imaging between 2003 and 2022. Only "articles" and "reviews" written in English were included in the final analysis. Subsequently, a manual screening process was employed to eliminate articles not relevant to the topic. The search results were exported as a plain text file in "Full Record and Cited References" format, and stored as "download_*.txt". CiteSpace and VOSviewer software were used to conduct the bibliometric and visualization analyses. These tools allowed us to analyze countries/regions and institutions' contributions, the top journals, prolific authors, influential references, and keywords. RESULTS: Our bibliometric analysis included 33,480 publications from 138 countries, involving contributions from 19,554 institutions and 146,043 authors. Annual publications in this field have shown a rapid increase since approximately 2004. The United States of America (USA) leads in MI imaging research, boasting the highest number of publications (n=11,431), prolific research institutions, and numerous core authors. Notably, Harvard University and the University of California System emerged as the primary research institutions. Among the authors, Budoff was the most prolific contributor (n=209). The co-citation analysis, which measures how frequently an author's work is cited together with other key studies in the field, showed that Kim (n=2,794) was the most influential author based on co-citations, highlighting his significant influence in the area. Most articles appeared in the journal of the , while was the most frequently co-cited journal. Key topics included MI, risk-factor, magnetic resonance imaging (MRI), and atherosclerosis. Recent popular keywords such as ST-elevation myocardial infarction (STEMI), coronary computed tomography angiography (CTA), guidelines, and recommendations indicated future research directions. CONCLUSIONS: Our bibliometric analysis revealed that MI non-invasive imaging is a rapidly developing field, with emerging trends in multimodal imaging and artificial intelligence (AI). These findings suggest that the integration of various imaging techniques and the use of AI could enhance the clinical application of MI non-invasive imaging, particularly in terms of early diagnosis, risk stratification, and personalized treatment strategies.

摘要

背景:心肌梗死(MI)是全球范围内导致死亡和残疾的主要原因。非侵入性心脏成像在心肌梗死的诊断和预后研究中受到了广泛关注。尽管已发表的文章数量不断增加,但尚未进行全面的定量分析来描绘心肌梗死非侵入性成像领域的关键趋势和新兴领域。这项文献计量分析旨在系统地评估以心肌梗死非侵入性心脏成像为重点的研究现状、发表模式、有影响力的贡献者、热点以及重点发展趋势。 方法:我们系统地检索了科学网核心合集(WoSCC),以获取2003年至2022年间与心肌梗死成像相关的出版物。最终分析仅纳入用英文撰写的“文章”和“综述”。随后,采用人工筛选过程排除与主题无关的文章。搜索结果以“全记录和被引参考文献”格式导出为纯文本文件,并存储为“download_*.txt”。使用CiteSpace和VOSviewer软件进行文献计量和可视化分析。这些工具使我们能够分析国家/地区和机构的贡献、顶级期刊、多产作者、有影响力的参考文献和关键词。 结果:我们的文献计量分析包括来自138个国家的33480篇出版物,涉及19554个机构和146043名作者的贡献。自2004年左右以来,该领域的年度出版物数量呈快速增长趋势。美国在心肌梗死成像研究方面领先,拥有最多的出版物(n = 11431)、多产的研究机构和众多核心作者。值得注意的是,哈佛大学和加利福尼亚大学系统是主要的研究机构。在作者中,布多夫是贡献最多的(n = 209)。共被引分析衡量了一位作者的作品与该领域其他关键研究一起被引用的频率,结果显示,基于共被引次数,金(n = 2794)是最有影响力的作者,突出了他在该领域的重大影响。大多数文章发表在《 》杂志上,而《 》是被共引频率最高的期刊。关键主题包括心肌梗死、危险因素、磁共振成像(MRI)和动脉粥样硬化。近期流行的关键词,如ST段抬高型心肌梗死(STEMI)、冠状动脉计算机断层扫描血管造影(CTA)、指南和建议,表明了未来的研究方向。 结论:我们的文献计量分析表明,心肌梗死非侵入性成像是一个快速发展的领域,在多模态成像和人工智能(AI)方面有新的趋势。这些发现表明,各种成像技术的整合以及AI的使用可以增强心肌梗死非侵入性成像的临床应用,特别是在早期诊断、风险分层和个性化治疗策略方面。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d40a/12290736/e97825d9b2bd/qims-15-07-6340-f8.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d40a/12290736/a9271b1427f9/qims-15-07-6340-f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d40a/12290736/14a7542df0a8/qims-15-07-6340-f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d40a/12290736/8c1a18667278/qims-15-07-6340-f7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d40a/12290736/e97825d9b2bd/qims-15-07-6340-f8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d40a/12290736/b1d934062424/qims-15-07-6340-f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d40a/12290736/4ac25cd9cd81/qims-15-07-6340-f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d40a/12290736/80f56abcf69c/qims-15-07-6340-f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d40a/12290736/b0ef42e88cde/qims-15-07-6340-f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d40a/12290736/a9271b1427f9/qims-15-07-6340-f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d40a/12290736/14a7542df0a8/qims-15-07-6340-f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d40a/12290736/8c1a18667278/qims-15-07-6340-f7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d40a/12290736/e97825d9b2bd/qims-15-07-6340-f8.jpg

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本文引用的文献

[1]
A Machine Learning Model Using Cardiac CT and MRI Data Predicts Cardiovascular Events in Obstructive Coronary Artery Disease.

Radiology. 2025-1

[2]
Bibliometric analysis of ST elevation myocardial infarction research from 1933 to 2023: Focus on top 100 most-cited articles.

Curr Probl Cardiol. 2025-1

[3]
Artificial intelligence applied to coronary artery calcium scans (AI-CAC) significantly improves cardiovascular events prediction.

NPJ Digit Med. 2024-11-5

[4]
Patient-Specific Myocardial Infarction Risk Thresholds From AI-Enabled Coronary Plaque Analysis.

Circ Cardiovasc Imaging. 2024-10

[5]
Right ventricular function and determining factors of dysfunction in ST-segment-elevation myocardial infarction: a cross-sectional study with cardiac magnetic resonance imaging (MRI).

Quant Imaging Med Surg. 2024-9-1

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Assessment of perioperative cardiac risk using preoperative quantitative flow ratio in patients with coronary artery disease undergoing noncardiac surgery: a retrospective cohort study.

Quant Imaging Med Surg. 2024-8-1

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External validation of the myocardial-ischaemic-injury-index machine learning algorithm for the early diagnosis of myocardial infarction: a multicentre cohort study.

Lancet Digit Health. 2024-7

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Prognostic Value of Follow-up Measures of Left Ventricular Global Longitudinal Strain in Patients With ST-Segment Elevation Myocardial Infarction.

J Am Soc Echocardiogr. 2024-7

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Postprocedural Anticoagulation After Primary Percutaneous Coronary Intervention for ST-Segment-Elevation Myocardial Infarction: A Multicenter, Randomized, Double-Blind Trial.

Circulation. 2024-4-16

[10]
Cardiac magnetic resonance imaging detection of intramyocardial hemorrhage in patients with ST-elevated myocardial infarction: comparison between susceptibility-weighted imaging and T1/T2 mapping techniques.

Quant Imaging Med Surg. 2024-1-3

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