ArticleJournal of the American Heart Association2020
Fully Automated Cardiac Assessment for Diagnostic and Prognostic Stratification Following Myocardial Infarction.
Article in Journal of the American Heart Association, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 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
24 citing papers in PubMed, 1 synthesis or guideline pooled it, 40 citations in OpenAlex.
- The Role of Artificial Intelligence in Predicting Outcomes by Cardiovascular Magnetic Resonance: A Comprehensive Systematic Review.Medicina (Kaunas, Lithuania) · 2022Pooled it
- Cardiovascular magnetic resonance-derived left atrioventricular coupling index and major adverse cardiac events in patients following acute myocardial infarction.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2023Trial
- Artificial intelligence fully automated myocardial strain quantification for risk stratification following acute myocardial infarction.Scientific reports · 2022Trial
- From Promise to Practice: Real-World Readiness of Artificial Intelligence for Cardiac Magnetic Resonance Imaging.Korean journal of radiology · 2026Article
- 3D Spatiotemporal cardiac reconstruction for predicting MACE in acute myocardial infarction.NPJ digital medicine · 2026Article
- Unsupervised phenotypic clustering of cardiac MRI data reveals distinct subgroups associated with outcomes in ischemic cardiomyopathy.The international journal of cardiovascular imaging · 2026Article
- AI Applied to Cardiac Magnetic Resonance for Precision Medicine in Coronary Artery Disease: A Systematic Review.Journal of cardiovascular development and disease · 2025Review
- Review
- Cardiac function in a large animal model of myocardial infarction at 7 T: deep learning based automatic segmentation increases reproducibility.Scientific reports · 2024Article
- Review
- Comprehensive characterization of cardiac contraction for improved post-infarction risk assessment.Scientific reports · 2024Article
- Inter-study reproducibility of cardiovascular magnetic resonance-derived hemodynamic force assessments.Scientific reports · 2024Article
- Artificial Intelligence as a Diagnostic Tool in Non-Invasive Imaging in the Assessment of Coronary Artery Disease.Medical sciences (Basel, Switzerland) · 2023Review
- Impact of myocardial deformation on risk prediction in patients following acute myocardial infarction.Frontiers in cardiovascular medicine · 2023Article
- Understanding and Improving Risk Assessment After Myocardial Infarction Using Automated Left Ventricular Shape Analysis.JACC. Cardiovascular imaging · 2022Article
- Role of cardiac magnetic resonance imaging in troponinemia syndromes.World journal of cardiology · 2022Review
- Assessment of right ventricular size and function from cardiovascular magnetic resonance images using artificial intelligence.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2022Article
- Artificial Intelligence Enabled Fully Automated CMR Function Quantification for Optimized Risk Stratification in Patients Undergoing Transcatheter Aortic Valve Replacement.Journal of interventional cardiology · 2022Article
- Semi-Automated 3D Volumetric Renal Measurements in Polycystic Kidney Disease Using b0-Images-A Feasibility Study.Tomography (Ann Arbor, Mich.) · 2021Article
- Quantification of Myocardial Deformation Applying CMR-Feature-Tracking-All About the Left Ventricle?Current heart failure reports · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
18 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background Cardiovascular magnetic resonance imaging is considered the reference methodology for cardiac morphology and function but requires manual postprocessing. Whether novel artificial intelligence-based automated analyses deliver similar information for risk stratification is unknown. Therefore, this study aimed to investigate feasibility and prognostic implications of artificial intelligence-based, commercially available software analyses. Methods and Results Cardiovascular magnetic resonance data (n=1017 patients) from 2 myocardial infarction multicenter trials were included. Analyses of biventricular parameters including ejection fraction (EF) were manually and automatically assessed using conventional and artificial intelligence-based software. Obtained parameters entered regression analyses for prediction of major adverse cardiac events, defined as death, reinfarction, or congestive heart failure, within 1 year after the acute event. Both manual and uncorrected automated volumetric assessments showed similar impact on outcome in univariate analyses (left ventricular EF, manual: hazard ratio [HR], 0.93 [95% CI 0.91-0.95];
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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.