Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2019 Jul 29;16(15):2699.
doi: 10.3390/ijerph16152699.

The Current Research Landscape of the Application of Artificial Intelligence in Managing Cerebrovascular and Heart Diseases: A Bibliometric and Content Analysis

Bach Xuan Tran  1   2 , Carl A Latkin  3 , Giang Thu Vu  4 , Huong Lan Thi Nguyen  5 , Son Nghiem  6 , Ming-Xuan Tan  7 , Zhi-Kai Lim  7 , Cyrus S H Ho  8 , Roger C M Ho  7   9   10
Affiliations

The Current Research Landscape of the Application of Artificial Intelligence in Managing Cerebrovascular and Heart Diseases: A Bibliometric and Content Analysis

Bach Xuan Tran et al. Int J Environ Res Public Health. .

Abstract

The applications of artificial intelligence (AI) in aiding clinical decision-making and management of stroke and heart diseases have become increasingly common in recent years, thanks in part to technological advancements and the heightened interest of the research and medical community. This study aims to provide a comprehensive picture of global trends and developments of AI applications relating to stroke and heart diseases, identifying research gaps and suggesting future directions for research and policy-making. A novel analysis approach that combined bibliometrics analysis with a more complex analysis of abstract content using exploratory factor analysis and Latent Dirichlet allocation, which uncovered emerging research domains and topics, was adopted. Data were extracted from the Web of Science database. Results showed topics with the most compelling growth to be AI for big data analysis, robotic prosthesis, robotics-assisted stroke rehabilitation, and minimally invasive surgery. The study also found an emerging landscape of research that was centered on population-specific and early detection of stroke and heart disease. Application of AI in health behavior tracking and improvement as well as the use of robotics in medical diagnostics and prognostication have also been found to attract significant research attention. In light of these findings, it is suggested that the currently under-researched issues of data management, AI model reliability, as well as validation of its clinical utility, need to be further explored in future research and policy decisions to maximize the benefits of AI applications in stroke and heart diseases.

Keywords: artificial intelligence; bibliometrics; cerebrovascular; heart diseases; scientometrics.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Paper selection process.
Figure 2
Figure 2
Co-occurrence of the most frequent author’s keywords. Note: the colors of the nodes indicate principle components of the data structure; node size was scaled to keyword occurrences; the thickness of the lines was drawn based on the strength of the association between two keywords. (ANN: artificial neural network; EEG: electroencephalogram; HRV: heart rate variability; MRI: magnetic resonance imaging; SVM: support vector machine).
Figure 3
Figure 3
Co-occurrence of the most frequent topics that emerged from the exploratory factor analysis of abstracts contents. (ANN: Artificial Neural Network; AUC: area under the curve; CHD: coronary heart disease; CT: computed tomography; ECG: electrocardiogram; HF: heart failure; HR: heart rate; HRV: heart rate variability; SVM: support vector machine).
Figure 4
Figure 4
Changes in applications of AI to stroke and heart disease research during 1991–2018.
Figure 5
Figure 5
Dendrogram of research areas using the WoS classifications.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. World Health Organization Cardiovascular Diseases (CVDs) [(accessed on 26 July 2019)]; Available online: www.who.int/cardiovascular_diseases/en/
    1. GBD 2017 DALYs. HALE Collaborators Global, regional, and national disability-adjusted life-years (DALYs) for 359 diseases and injuries and healthy life expectancy (HALE) for 195 countries and territories, 1990–2017: A systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2018;392:1859–1922. doi: 10.1016/S0140-6736(18)32335-3. - DOI - PMC - PubMed
    1. Tran B.X., Vu G.T., Ha G.H., Vuong Q.H., Ho M.T., Vuong T.T., La V.P., Ho M.T., Nghiem K.P., Nguyen H.L.T., et al. Global Evolution of Research in Artificial Intelligence in Health and Medicine: A Bibliometric Study. J. Clin. Med. 2019;8:360. doi: 10.3390/jcm8030360. - DOI - PMC - PubMed
    1. Bom M.J., Levin E., Driessen R.S., Danad I., Van Kuijk C.C., van Rossum A.C., Narula J., Min J.K., Leipsic J.A., Belo Pereira J.P., et al. Predictive value of targeted proteomics for coronary plaque morphology in patients with suspected coronary artery disease. EBioMedicine. 2019;39:109–117. doi: 10.1016/j.ebiom.2018.12.033. - DOI - PMC - PubMed
    1. Alawieh A., Zaraket F., Alawieh M.B., Chatterjee A.R., Spiotta A. Using machine learning to optimize selection of elderly patients for endovascular thrombectomy. J. NeuroInterv. Surg. 2019;11:847–851. doi: 10.1136/neurintsurg-2018-014381. - DOI - PubMed