To conduct a scientometric analysis to delineate the research landscape, identify hotspots, and trace evolutionary trends of AI technology applications in aortic valve disease over the past decade.
Approach:
Data Sources: Literature published between January 2016 and January 2026 was retrieved from the Web of Science Core Collection and Scopus databases, resulting in 270 eligible articles after screening.
Analysis Tools: CiteSpace and VOSviewer were employed for visualization analyses of authors, institutions, countries, journals, keywords, and co-citation networks.
Key Findings:
Annual publications increased steadily, with the United States leading in output and influence.
The Mayo Clinic was identified as the most prolific institution.
Research hotspots included AI-assisted diagnosis, risk stratification, and prognosis prediction for aortic stenosis, primarily utilizing deep learning and machine learning techniques.
Keyword clustering revealed themes related to disease diagnosis, therapeutic technologies, AI-enabled applications, and clinical outcomes.
Co-citation analysis highlighted key studies on AI-enhanced electrocardiography and echocardiography for valve disease detection.
Interpretation:
Limitations:
The study is limited to literature published in specific databases, which may not encompass all relevant research.
The analysis is based on available publications and may not reflect unpublished or ongoing research.
