ReviewFrontiers in cardiovascular medicine2024
AI-powered contrast-free cardiovascular magnetic resonance imaging for myocardial infarction.
Review 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 11 papers.
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.
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Who cites it
11 citing papers in PubMed.
- From Automated ECG Interpretation to Multimodal Cardiovascular Intelligence: The Evolution of Artificial Intelligence in Cardiovascular Medicine.Medical sciences (Basel, Switzerland) · 2026Review
- AI-Driven Generation of Post-Contrast T1 and ECV Maps from Native T1 Map in Cardiac MRI.Diagnostics (Basel, Switzerland) · 2026Article
- Advancements in Cardiac Magnetic Resonance Imaging: Innovations, Challenges, and Future Directions.Reviews in cardiovascular medicine · 2026Review
- Prognostic incremental value of perivascular adipose tissue in myocardial infarction with non-obstructive coronary arteries: a multicenter cohort study.Quantitative imaging in medicine and surgery · 2026Article
- Article
- Unsupervised cluster analysis identifies risk profiles driving heterogeneity and survival patterns in aortic aneurysm patients.Scientific reports · 2026Article
- Predictive value of hemoglobin-to-red cell distribution width ratio for mortality in new-onset atrial fibrillation after cardiac surgery a machine learning study based on the MIMIC-IV database.BMC cardiovascular disorders · 2026Article
- AI Characterisation of Discordance Profiles Between Stress Electrocardiogram and Myocardial Tomoscintigraphy Using Random Forest XGBoost and SHAP.Medical devices (Auckland, N.Z.) · 2026Article
- Prognostic Associations of the Naples Score for Clinical Outcomes in Patients With Acute Myocardial Infarction: A Systematic Review and a Prognostic Meta-Analysis.Cardiology research and practice · 2026Review
- Predicting peripheral venous catheter related phlebitis using machine learning (PPML): development and prospective validation of PPML for cardiology inpatients.BMC medical informatics and decision making · 2025Article
- Comparison of machine learning and nomogram to predict 30-day in-hospital mortality in patients with acute myocardial infarction combined with cardiogenic shock: a retrospective study based on the eICU-CRD and MIMIC-IV databases.BMC cardiovascular disorders · 2025Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Cardiovascular magnetic (CMR) resonance is a versatile tool for diagnosing cardiovascular diseases. While gadolinium-based contrast agents are the gold standard for identifying myocardial infarction (MI), their use is limited in patients with allergies or impaired kidney function, affecting a significant portion of the MI population. This has led to a growing interest in developing artificial intelligence (AI)-powered CMR techniques for MI detection without contrast agents. This mini-review focuses on recent advancements in AI-powered contrast-free CMR for MI detection. We explore various AI models employed in the literature and delve into their strengths and limitations, paving the way for a comprehensive understanding of this evolving field.
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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.