Global trends in coronary artery disease and artificial intelligence relevant studies: a bibliometric analysis

X.-T. Qi, H. Wang, D.-G. Zhu, L. Zheng, X. Cheng, R.-J. Zhang, H.-L. Dong

Department of Vascular Surgery, The Second Hospital of Shanxi Medical University, Taiyuan, China. honglindong@sxmu.edu.cn

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• Artificial intelligence

OBJECTIVE: Coronary artery disease (CAD) is a major global cause of death, greatly affecting life expectancy and quality of life for populations. With the advent of artificial intelligence (AI), there is new hope for accurately managing CAD. While recent studies have shown remarkable progress in AI and CAD research, there is a gap in comprehensive bibliometric analysis in this field. Therefore, this study aims to provide a thorough analysis of trends and hotspots in AI and CAD-related research utilizing bibliometrics.

MATERIALS AND METHODS: Publications on AI and CAD relevant research from 2009 to 2023 were searched through the WoS core database (WoSCC). CiteSpace, VOSviewer and Excel 365 were used to conduct the bibliometric analysis.

RESULTS: The bibliometric analysis included 1,248 publications, indicating a steady increase in AI and CAD-related publications annually. The United States of America (USA), China, and Germany were identified as the most influential countries in this field. Research institutions such as Cedars Sinai Med Ctr, Med Univ South Carolina, Harvard Med Sch and Capital Med Univ were the main contributors to research production. FRONT CARDIOVASC MED is the top-ranked journal, while J AM COLL CARDIOL emerged as the most cited journal. Schoepf, U. Joseph, Slomka, Piotr J., Berman, Daniel S. and Dey, Damini were the most prolific authors, while U. Rajendra Acharya was the most frequently co-cited author. Research related to the AI calculation of coronary flow reserve fraction and coronary artery calcification, based on coronary CT to identify CAD and cardiovascular risk, was a key research topic in this field. The potential link between cardiovascular risk stratification and radiomics is currently at the forefront of the field.

CONCLUSIONS: This study is the first to use a bibliometric approach to visualize and analyze AI and CAD-related research. The findings provide insights into recent research trends and hotspots in the field and can serve as a reference for scholars to identify critical issues in this field.

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