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January 24, 2026Digital HealthOpen Access

Artificial intelligence techniques for cardiovascular disease diagnosis via X-ray sensor-based coronary angiography: A bibliometric and systematic review

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Key result

Convolutional neural networks achieved diagnostic accuracies of 90%-95% and AUCs up to 0.99 for acute myocardial infarction detection via X-ray coronary angiography.

Authors

HRHao RenFJF S JingYFYan Fang

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Overview

A systematic review assesses AI's role in diagnosing cardiovascular diseases using X-ray angiography, highlighting trends and methodologies.

Key Points

  • This research evaluates how artificial intelligence is applied in coronary angiography for cardiovascular disease diagnosis, focusing on trends and methodologies.
  • Conducted a bibliometric analysis of publications from 2010 to 2025 using databases like Web of Science and PubMed.
  • Utilized PRISMA flowchart for screening and CiteSpace for analyzing publication trends and thematic clusters.
  • Reviewed AI methodologies in literature, categorizing findings based on clinical categories like myocardial infarction and ischemic cardiomyopathy.
  • Publication growth observed, with 28 papers in 2022 showing a surge in interest.
  • Convolutional neural networks achieved high diagnostic performance metrics, with AUCs ranging from 0.724 to 0.997.
  • Classical machine-learning models also demonstrated strong performance, but challenges include dataset variation and lack of extensive validation.

Cite This Study

Ren et al. (2026) studied this question. Convolutional neural networks achieved diagnostic accuracies of 90%-95% and AUCs up to 0.99 for acute myocardial infarction detection via X-ray coronary angiography.

synapsesocial.com/papers/697565e7d5e0d64addde8121https://doi.org/10.1177/20552076261417142
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