دورية أكاديمية
Navigating the AI frontiers in cardiovascular research: a bibliometric exploration and topic modeling.
| العنوان: | Navigating the AI frontiers in cardiovascular research: a bibliometric exploration and topic modeling. |
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| المؤلفون: | Shiferaw KB; Department of Medical Informatics, Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany., Wali P; Department of Medical Informatics, Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany., Waltemath D; Department of Medical Informatics, Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany., Zeleke AA; Department of Medical Informatics, Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany. |
| المصدر: | Frontiers in cardiovascular medicine [Front Cardiovasc Med] 2024 Jan 03; Vol. 10, pp. 1308668. Date of Electronic Publication: 2024 Jan 03 (Print Publication: 2023). |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Frontiers Media S.A Country of Publication: Switzerland NLM ID: 101653388 Publication Model: eCollection Cited Medium: Print ISSN: 2297-055X (Print) Linking ISSN: 2297055X NLM ISO Abbreviation: Front Cardiovasc Med Subsets: PubMed not MEDLINE |
| أسماء مطبوعة: | Original Publication: Lausanne : Frontiers Media S.A., [2014]- |
| مستخلص: | Artificial intelligence (AI) has emerged as a promising field in cardiovascular disease (CVD) research, offering innovative approaches to enhance diagnosis, treatment, and patient outcomes. In this study, we conducted bibliometric analysis combined with topic modeling to provide a comprehensive overview of the AI research landscape in CVD. Our analysis included 23,846 studies from Web of Science and PubMed, capturing the latest advancements and trends in this rapidly evolving field. By employing LDA (Latent Dirichlet Allocation) we identified key research themes, trends, and collaborations within the AI-CVD domain. The findings revealed the exponential growth of AI-related research in CVD, underscoring its immense potential to revolutionize cardiovascular healthcare. The annual scientific publication of machine learning papers in CVD increases continuously and significantly since 2016, with an overall annual growth rate of 22.8%. Almost half (46.2%) of the growth happened in the last 5 years. USA, China, India, UK and Korea were the top five productive countries in number of publications. UK, Germany and Australia were the most collaborative countries with a multiple country publication (MCP) value of 42.8%, 40.3% and 40.0% respectively. We observed the emergence of twenty-two distinct research topics, including "stroke and robotic rehabilitation therapy," "robotic-assisted cardiac surgery," and "cardiac image analysis," which persisted as major topics throughout the years. Other topics, such as "retinal image analysis and CVD" and "biomarker and wearable signal analyses," have recently emerged as dominant areas of research in cardiovascular medicine. Convolutional neural network appears to be the most mentioned algorithm followed by LSTM (Long Short-Term Memory) and KNN (K-Nearest Neighbours). This indicates that the future direction of AI cardiovascular research is predominantly directing toward neural networks and image analysis. As AI continues to shape the landscape of CVD research, our study serves as a comprehensive guide for researchers, practitioners, and policymakers, providing valuable insights into the current state of AI in CVD research. This study offers a deep understanding of research trends and paves the way for future directions to maximiz the potential of AI to effectively combat cardiovascular diseases. Competing Interests: 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. (© 2024 Shiferaw, Wali, Waltemath and Zeleke.) |
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| فهرسة مساهمة: | Keywords: artificial intelligence; bibliometric; cardiovascular; machine learning; scientometric; topic modeling |
| تواريخ الأحداث: | Date Created: 20240118 Latest Revision: 20240119 |
| رمز التحديث: | 20240119 |
| مُعرف محوري في PubMed: | PMC10793658 |
| DOI: | 10.3389/fcvm.2023.1308668 |
| PMID: | 38235288 |
| قاعدة البيانات: | MEDLINE |
Full Text Finder | تدمد: | 2297-055X |
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| DOI: | 10.3389/fcvm.2023.1308668 |