ArticleEuropean radiology2020
Impact of machine learning-based coronary computed tomography angiography fractional flow reserve on treatment decisions and clinical outcomes in patients with suspected coronary artery disease.
Article in European radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it, 43 citations in OpenAlex.
- The Machine Learning Models in Major Cardiovascular Adverse Events Prediction Based on Coronary Computed Tomography Angiography: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Integration of Magnetocardiography and Coronary Computed Tomography Angiography With Machine Learning for Detection of Functionally Significant Myocardial Ischemia.Reviews in cardiovascular medicine · 2026Article
- Diagnostic performance of a coronary CT angiography-based deep learning model for the prediction of vessel-specific ischemia.European radiology · 2026Article
- Article
- Long-term prognostic value of the CT-derived fractional flow reserve combined with atherosclerotic burden in patients with non-obstructive coronary artery disease.European radiology · 2025Article
- Cardiac CT in the era of artificial intelligence: precision imaging, treatment guidance and optimised risk stratification for coronary artery disease.Open heart · 2025Review
- Assessment of coronary computed tomography angiography-derived plaque features in the diagnosis of optical coherence tomography-defined vulnerable plaques.Quantitative imaging in medicine and surgery · 2025Article
- Prognostic Significance of Computed Tomography-Derived Fractional Flow Reserve for Long-Term Outcomes in Individuals With Coronary Artery Disease.Journal of the American Heart Association · 2025Article
- Artificial Intelligence in Imaging for Personalized Management of Coronary Artery Disease.Journal of clinical medicine · 2025Review
- Artificial Intelligence based fractional flow reserve.Cardiology journal · 2025Review
- Use of AI in Cardiac CT and MRI: A Scientific Statement from the ESCR, EuSoMII, NASCI, SCCT, SCMR, SIIM, and RSNA.Radiology · 2025Review
- Cardiovascular computed tomography in cardiovascular disease: An overview of its applications from diagnosis to prediction.Journal of geriatric cardiology : JGC · 2024Article
- Investigation of the predictive value of a novel algorithm based on coronary CT angiography regarding fractional flow reserve and revascularization in patients with stable coronary artery disease.Heart and vessels · 2024Article
- Diagnostic Performance of Noninvasive Coronary Computed Tomography Angiography-Derived FFR for Coronary Lesion-Specific Ischemia Based on Deep Learning Analysis.Reviews in cardiovascular medicine · 2024Article
- Comparison of prognostic value between CAD-RADS 1.0 and CAD-RADS 2.0 evaluated by convolutional neural networks based CCTA.Heliyon · 2023Article
- Patient-level explainable machine learning to predict major adverse cardiovascular events from SPECT MPI and CCTA imaging.PloS one · 2023Article
- Current and Future Applications of Artificial Intelligence in Coronary Artery Disease.Healthcare (Basel, Switzerland) · 2022Review
- CT-based fractional flow reserve: development and expanded application.Global cardiology science & practice · 2021Review
- A 2-year investigation of the impact of the computed tomography-derived fractional flow reserve calculated using a deep learning algorithm on routine decision-making for coronary artery disease management.European radiology · 2021Article
- Feasibility and prognostic role of machine learning-based FFREuropean radiology · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors at 4 institutions in 3 countries.
Funding
Abstract
objectivesThis study investigated the impact of machine learning (ML)-based fractional flow reserve derived from computed tomography (FFR
methodsOne thousand one hundred twenty-one consecutive patients with stable chest pain who underwent coronary computed tomography angiography (CCTA) followed ICA within 90 days between January 2007 and December 2016 were included in this retrospective study. Medical records were reviewed for the endpoint of major adverse cardiac events (MACEs). FFR
resultsAfter FFR
conclusionsThis study indicated ML-based FFR KEY POINTS: • ML-based FFR
Indexed as
Identifiers
What OpenQuestion holds
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.