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. 2024 Feb 16:11:1323918.
doi: 10.3389/fcvm.2024.1323918. eCollection 2024.

Artificial intelligence applied in cardiovascular disease: a bibliometric and visual analysis

Jirong Zhang #  1 , Jimei Zhang #  2 , Juan Jin  3 , Xicheng Jiang  4 , Linlin Yang  5 , Shiqi Fan  6 , Qiao Zhang  7 , Ming Chi  1
Affiliations

Artificial intelligence applied in cardiovascular disease: a bibliometric and visual analysis

Jirong Zhang et al. Front Cardiovasc Med. .

Abstract

Background: With the rapid development of technology, artificial intelligence (AI) has been widely used in the diagnosis and prognosis prediction of a variety of diseases, including cardiovascular disease. Facts have proved that AI has broad application prospects in rapid and accurate diagnosis.

Objective: This study mainly summarizes the research on the application of AI in the field of cardiovascular disease through bibliometric analysis and explores possible future research hotpots.

Methods: The articles and reviews regarding application of AI in cardiovascular disease between 2000 and 2023 were selected from Web of Science Core Collection on 30 December 2023. Microsoft Excel 2019 was applied to analyze the targeted variables. VOSviewer (version 1.6.16), Citespace (version 6.2.R2), and a widely used online bibliometric platform were used to conduct co-authorship, co-citation, and co-occurrence analysis of countries, institutions, authors, references, and keywords in this field.

Results: A total of 4,611 articles were selected in this study. AI-related research on cardiovascular disease increased exponentially in recent years, of which the USA was the most productive country with 1,360 publications, and had close cooperation with many countries. The most productive institutions and researchers were the Cedar sinai medical center and Acharya, Ur. However, the cooperation among most institutions or researchers was not close even if the high research outputs. Circulation is the journal with the largest number of publications in this field. The most important keywords are "classification", "diagnosis", and "risk". Meanwhile, the current research hotpots were "late gadolinium enhancement" and "carotid ultrasound".

Conclusions: AI has broad application prospects in cardiovascular disease, and a growing number of scholars are devoted to AI-related research on cardiovascular disease. Cardiovascular imaging techniques and the selection of appropriate algorithms represent the most extensively studied areas, and a considerable boost in these areas is predicted in the coming years.

Keywords: Left Ventricle Ejection Fraction (LVEF); artificial intelligence; bibliometric; cardiovascular disease; late gadolinium enhancement.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Flow diagram of screening progress related to AI in CVDs.
Figure 2
Figure 2
Global trend of publications on AI-based CVDs pathology research over the past 23 years.
Figure 3
Figure 3
Visual map of countries/region. (A) The countries/regions citation overlay visualization map generated by using VOS viewer. (B) Collaboration network analysis of countries/regions. (C) The distribution trend of the top 10 countriesregions by year.
Figure 4
Figure 4
The network of institution.
Figure 5
Figure 5
A visual authors co-operation map for VOSviwer network.
Figure 6
Figure 6
Co-cited references timeline view.
Figure 7
Figure 7
Top 30 references with the strongest citation bursts on AI-based CVDs research.
Figure 8
Figure 8
The network of keywords.
Figure 9
Figure 9
Top 30 keywords with the strongest citation bursts.
Figure 10
Figure 10
Timeline view to AI in CVDs.
See this image and copyright information in PMC

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