SynthesisFrontiers in cardiovascular medicine2024
Diagnostic value of artificial intelligence-assisted CTA for the assessment of atherosclerosis plaque: a systematic review and meta-analysis.
Synthesis in Frontiers in cardiovascular medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
What it found
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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
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence diagnostic performance in image-based vulnerable carotid plaque detection: a systematic review and meta-analysis.BMC medical informatics and decision making · 2025Pooled it
- Coronary CT angiography evaluation with artificial intelligence for individualized medical treatment of atherosclerosis: a Consensus Statement from the QCI Study Group.Nature reviews. Cardiology · 2026Review
- Exploring the Potential of AI and Augmented Reality in Cardiovascular Disease Management: A Narrative Review.Current cardiology reviews · 2026Review
- Applications of Artificial Intelligence in Chronic Total Occlusion Revascularization: From Present to Future-A Narrative Review.Medicina (Kaunas, Lithuania) · 2025Review
- Increased high-risk plaque burden in type 2 diabetes: a 10-year follow-up study.Cardiovascular diabetology · 2025Article
- Review
- Artificial intelligence in carotid computed tomography angiography plaque detection: Decade of progress and future perspectives.World journal of radiology · 2025Review
- Computed Tomography and Coronary Plaque Analysis.Tomography (Ann Arbor, Mich.) · 2025Review
- Beyond plaque segmentation: a combined radiomics-deep learning approach for automated CAD-RADS classification.Frontiers in medicine · 2025Article
- Innovations in MRI and AI Integration for Vascular Plaque Evaluation and Overview of Deep Learning Techniques in Peripheral Vascular Disease.Methodist DeBakey cardiovascular journal · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Artificial intelligence (AI) has increasingly been applied to computed tomography angiography (CTA) images to aid in the assessment of atherosclerotic plaque. Our aim was to explore the diagnostic accuracy of AI-assisted CTA for plaque diagnosis and classification through a systematic review and meta-analysis. Methods: A systematic literature review was performed by searching PubMed, EMBASE, and the Cochrane Library according to PRISMA guidelines. Original studies evaluating the diagnostic accuracy of radiomics, machine-learning, or deep-learning techniques applied to CTA images for detecting stenosis, calcification, or plaque vulnerability were included. The quality and risk of bias of the included studies were evaluated using the QUADAS-2 tool. The meta-analysis was conducted using STATA software (version 17.0) to pool sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC) to determine the overall diagnostic performance. Results: A total of 11 studies comprising 1,484 patients were included. There was low risk of bias and substantial heterogeneity. The overall pooled AUROC for atherosclerotic plaque assessment was 0.96 [95% confidence interval (CI) 0.94-0.97] across 21 trials. Of these, for ≥50% stenosis detection, the AUROC was 0.95 (95% CI 0.93-0.96) in five studies. For identifying ≥70% stenosis, the AUROC was 0.96 (95% CI 0.94-0.97) in six studies. For calcium detection, the AUROC was 0.92 (95% CI 0.90-0.94) in six studies. Conclusion: Our meta-analysis demonstrates that AI-assisted CTA has high diagnostic accuracy for detecting stenosis and characterizing plaque composition, with optimal performance in detecting ≥70% stenosis. Systematic Review Registration: https://www.crd.york.ac.uk/, PROSPERO, identifier (CRD42023431410).
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