ArticleNPJ digital medicine2024
Evaluation of stenoses using AI video models applied to coronary angiography.
Article in NPJ digital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed.
- Introduction and validation of OSCAR-optimal stent choice algorithm.CVIR endovascular · 2026Article
- The current state of artificial intelligence-based invasive coronary angiography image analysis: a systematic review.European heart journal. Digital health · 2026Review
- Artificial Intelligence Powered Real-Time Coronary Stenosis Recognition and Quantification in Angiography.Journal of imaging informatics in medicine · 2026Article
- Deep learning predicts stent implantation in borderline coronary lesions from angiography.NPJ digital medicine · 2026Article
- Comparing artificial intelligence and healthcare professional performance in surgical and interventional video analysis: a systematic review and meta-analysis.NPJ digital medicine · 2026Article
- Cardiologist user experience of artificial intelligence-based quantitative coronary angiography.Cardiovascular diagnosis and therapy · 2025Article
- Deep Learning-Based Segmentation of Coronary Arteries and Stenosis Detection in X-Ray Coronary Angiography.JACC. Advances · 2025Article
- Artificial Intelligence in Drug-Coated Cardiovascular Devices: A Narrative Review.Reviews in cardiovascular medicine · 2025Review
- Review
- Artificial Intelligence in Coronary Artery Interventions: Preprocedural Planning and Procedural Assistance.Journal of the Society for Cardiovascular Angiography & Interventions · 2025Review
- Artificial Intelligence in Cardiovascular Clinical Trials.Journal of the American College of Cardiology · 2024Review
- Revolutionising Acute Cardiac Care With Artificial Intelligence: Opportunities and Challenges.The Canadian journal of cardiology · 2024Review
- Article
Corrections and comments
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Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The coronary angiogram is the gold standard for evaluating the severity of coronary artery disease stenoses. Presently, the assessment is conducted visually by cardiologists, a method that lacks standardization. This study introduces DeepCoro, a ground-breaking AI-driven pipeline that integrates advanced vessel tracking and a video-based Swin3D model that was trained and validated on a dataset comprised of 182,418 coronary angiography videos spanning 5 years. DeepCoro achieved a notable precision of 71.89% in identifying coronary artery segments and demonstrated a mean absolute error of 20.15% (95% CI: 19.88-20.40) and a classification AUROC of 0.8294 (95% CI: 0.8215-0.8373) in stenosis percentage prediction compared to traditional cardiologist assessments. When compared to two expert interventional cardiologists, DeepCoro achieved lower variability than the clinical reports (19.09%; 95% CI: 18.55-19.58 vs 21.00%; 95% CI: 20.20-21.76, respectively). In addition, DeepCoro can be fine-tuned to a different modality type. When fine-tuned on quantitative coronary angiography assessments, DeepCoro attained an even lower mean absolute error of 7.75% (95% CI: 7.37-8.07), underscoring the reduced variability inherent to this method. This study establishes DeepCoro as an innovative video-based, adaptable tool in coronary artery disease analysis, significantly enhancing the precision and reliability of stenosis assessment.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.