ArticleInternational journal of cardiology. Heart & vasculature2024
Evaluating machine learning accuracy in detecting significant coronary stenosis using CCTA-derived fractional flow reserve: Meta-analysis and systematic review.
Article in International journal of cardiology. Heart & vasculature, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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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
7 citing papers in PubMed.
- Advanced quantitative CT for coronary artery disease: integrating stenosis quantification, plaque quantification, and fractional flow reserve-CT.The British journal of radiology · 2026Review
- Systematic reviews of medical machine learning: limitations of pooling AUCs.European heart journal. Digital health · 2026Article
- Region guided mask R-CNN with Haralick ResNet fusion for accurate coronary artery disease detection in computed tomography angiography images.Scientific reports · 2026Article
- Clinical Applications of Artificial Intelligence in Cardiovascular Imaging: Where Do We Stand?Life (Basel, Switzerland) · 2026Review
- Article
- The diagnostic performance of machine learning-based FFRCT for coronary artery disease: A meta-analysis.Open medicine (Warsaw, Poland) · 2025Article
- Beyond plaque segmentation: a combined radiomics-deep learning approach for automated CAD-RADS classification.Frontiers in medicine · 2025Article
Corrections and comments
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Authors and funding
14 authors.
Funding
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Abstract
Background: The use of machine learning (ML) based coronary computed tomography angiography (CCTA) derived fractional flow reserve (ML-FFR Methods: To identify suitable studies, comprehensive literature search was performed in PubMed, the Cochrane Library, Embase, up to August 2023. The index test was ML derived FFR and studies with diagnostic test accuracy data of ML-FFR Results: After full text review of 230 identified studies, 17 were included for analysis, which encompassed 3255 participants (age 62.0 ± 3.7). 8 studies reported patient-level data; and 12, vessel-level data. With iFFR as the reference standard, the pooled patient-level sensitivity, specificity, and area-under-curve (AUC) of ML-FFR Conclusions: This systemic review demonstrated the favourable diagnostic performance of ML-FFR
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Registered trials
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