Application of non-invasive imaging in myocardial infarction: a bibliometric analysis from January 2003 to December 2022
Year: 2025
Type: article
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: 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.
DOI: 10.21037/qims-24-878
Authors: Pei Yang, Xinwen Wu, Yaya Zhao, Qingyou Ye, Mengyao Hu, Yinping Leng, Xuan Xiao, Jiahui Zhang, Haibo Ren, Lianggeng Gong
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4411901782