ArticleThe international journal of cardiovascular imaging2024
Invasive fractional-flow-reserve prediction by coronary CT angiography using artificial intelligence vs. computational fluid dynamics software in intermediate-grade stenosis.
Article in The international journal of cardiovascular imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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Who cites it
8 citing papers in PubMed.
- Diagnostic performance of a coronary CT angiography-based deep learning model for the prediction of vessel-specific ischemia.European radiology · 2026Article
- The role of artificial intelligence in early detection and risk prediction of ischemic heart disease.Annals of medicine and surgery (2012) · 2026Review
- Comparative diagnostic performance and stability of deep learning- and CFD-based CT-FFR across vessels, cardiac phases, and centers.Frontiers in cardiovascular medicine · 2026Article
- Emerging quantitative CCTA imaging biomarkers for cardiovascular risk stratification: a narrative review.Frontiers in cardiovascular medicine · 2026Review
- Fractional Flow Reserve in the Left Anterior Descending Artery.Journal of clinical medicine · 2025Review
- Efficacy of artificial intelligence-based FFR technology for coronary CTA stenosis detection in clinical management of coronary artery disease: a systematic review.Frontiers in physiology · 2025Review
- Advancements in Cardiac CT Imaging: The Era of Artificial Intelligence.Echocardiography (Mount Kisco, N.Y.) · 2024Review
- Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Coronary computed angiography (CCTA) with non-invasive fractional flow reserve (FFR) calculates lesion-specific ischemia when compared with invasive FFR and can be considered for patients with stable chest pain and intermediate-grade stenoses according to recent guidelines. The objective of this study was to compare a new CCTA-based artificial-intelligence deep-learning model for FFR prediction (FFR
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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.