Trial reportScientific reports2022
Artificial intelligence fully automated myocardial strain quantification for risk stratification following acute myocardial infarction.
Trial report in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 21 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Prospective Randomized Controlled Clinical Study to Compare Abciximab-bolus i.v. Versus i.c. in Primary PCI in Patients With Acute ST-elevation Myocardial Infarction
Thrombus Aspiration in ThrOmbus Containing culpRiT Lesions in Non-ST-Elevation Myocardial Infarction (TATORT-NSTEMI)
Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it, 34 citations in OpenAlex.
- Systematic review and meta-analysis for the value of cardiac magnetic resonance strain to predict cardiac outcomes.Scientific reports · 2024Pooled it
- The Diagnostic and Prognostic Impact of Cardiovascular Magnetic Resonance in Ischemic Heart Disease: A Systematic Review.Journal of clinical medicine · 2026Review
- Review
- 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
- Reference Values for Myocardial Strain by Cardiac Magnetic Resonance Feature Tracking: Insights From Healthy Volunteers and Heart Failure Patients Using Caas MR.Reviews in cardiovascular medicine · 2026Article
- AI Applied to Cardiac Magnetic Resonance for Precision Medicine in Coronary Artery Disease: A Systematic Review.Journal of cardiovascular development and disease · 2025Review
- Reperfusion injury in STEMI: a double-edged sword.The Egyptian heart journal : (EHJ) : official bulletin of the Egyptian Society of Cardiology · 2025Review
- Artificial intelligence in interventional cardiology: a review of its role in diagnosis, decision-making, and procedural precision.Annals of medicine and surgery (2012) · 2025Review
- Machine Learning Applications in Acute Coronary Syndrome: Diagnosis, Outcomes and Management.Advances in therapy · 2025Review
- Charting the Unseen: How Non-Invasive Imaging Could Redefine Cardiovascular Prevention.Journal of cardiovascular development and disease · 2024Review
- Review
- The Role of Artificial Intelligence in Improving Patient Outcomes and Future of Healthcare Delivery in Cardiology: A Narrative Review of the Literature.Healthcare (Basel, Switzerland) · 2024Review
- Early signs of myocardial systolic dysfunction in patients with type 2 diabetes are strongly associated with myocardial microvascular dysfunction independent of myocardial fibrosis: a prospective cohort study.Diabetology & metabolic syndrome · 2024Article
- Comparison of manual and artificial intelligence based quantification of myocardial strain by feature tracking-a cardiovascular MR study in health and disease.European radiology · 2024Article
- Enhancing Arrhythmogenic Right Ventricular Cardiomyopathy Detection and Risk Stratification: Insights from Advanced Echocardiographic Techniques.Diagnostics (Basel, Switzerland) · 2024Article
- Inter-study reproducibility of cardiovascular magnetic resonance-derived hemodynamic force assessments.Scientific reports · 2024Article
- Personalized Approaches to Cardiovascular Disease: Insights into FDA-Approved Interventions and Clinical Pharmacogenetics.Current pharmaceutical design · 2024Review
- Artificial intelligence in cardiovascular diseases: diagnostic and therapeutic perspectives.European journal of medical research · 2023Review
- Artificial intelligence in modern clinical practice (Review).Medicine internationalReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors at 6 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Feasibility of automated volume-derived cardiac functional evaluation has successfully been demonstrated using cardiovascular magnetic resonance (CMR) imaging. Notwithstanding, strain assessment has proven incremental value for cardiovascular risk stratification. Since introduction of deformation imaging to clinical practice has been complicated by time-consuming post-processing, we sought to investigate automation respectively. CMR data (n = 1095 patients) from two prospectively recruited acute myocardial infarction (AMI) populations with ST-elevation (STEMI) (AIDA STEMI n = 759) and non-STEMI (TATORT-NSTEMI n = 336) were analysed fully automated and manually on conventional cine sequences. LV function assessment included global longitudinal, circumferential, and radial strains (GLS/GCS/GRS). Agreements were assessed between automated and manual strain assessments. The former were assessed for major adverse cardiac event (MACE) prediction within 12 months following AMI. Manually and automated derived GLS showed the best and excellent agreement with an intraclass correlation coefficient (ICC) of 0.81. Agreement was good for GCS and poor for GRS. Amongst automated analyses, GLS (HR 1.12, 95% CI 1.08-1.16, p < 0.001) and GCS (HR 1.07, 95% CI 1.05-1.10, p < 0.001) best predicted MACE with similar diagnostic accuracy compared to manual analyses; area under the curve (AUC) for GLS (auto 0.691 vs. manual 0.693, p = 0.801) and GCS (auto 0.668 vs. manual 0.686, p = 0.425). Amongst automated functional analyses, GLS was the only independent predictor of MACE in multivariate analyses (HR 1.10, 95% CI 1.04-1.15, p < 0.001). Considering high agreement of automated GLS and equally high accuracy for risk prediction compared to the reference standard of manual analyses, automation may improve efficiency and aid in clinical routine implementation.Trial registration: ClinicalTrials.gov, NCT00712101 and NCT01612312.
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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.