Artificial intelligence techniques for cardiovascular disease diagnosis via X-ray sensor-based coronary angiography: A bibliometric and systematic review
Abstract
Purpose: To systematically evaluate the application of artificial intelligence (AI) techniques in X-ray sensor-based coronary angiography for cardiovascular disease (CVD) diagnosis, mapping publication trends, geographic and topical hotspots via bibliometric analysis, and critically reviewing disease-specific AI methodologies and performance to inform future research and clinical integration. Non-angiographic inputs were considered only when angiography served as the reference standard or when the algorithm was explicitly integrated into an angiography-based workflow.
Methods: A two-part approach was undertaken. In Part I, we performed a bibliometric analysis of English-language original research and reviews published between 1 June 2010 and 1 June 2025, retrieved from Web of Science, Scopus, and PubMed. Records (n = 123) were screened using a PRISMA flowchart and analyzed with CiteSpace v6.3.R1 to identify annual publication trends, country contributions, co-authorship networks, and keyword clusters. In Part II, we conducted a structured literature review of the AI methods reported in these studies, organizing findings by three major clinical categories-acute myocardial infarction, ischemic cardiomyopathy, and unstable angina-and extracting model architectures, data sources, and diagnostic performance metrics (accuracy, sensitivity, specificity, and AUC).
Results: Bibliometric analysis revealed three publication phases: a formative period (2010-2017) with n = 39) and China…
How strong is the evidence?
Evidence Hub reads this as a systematic review. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid. Reported size: 123 participants or records.
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