ReviewJournal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance2019
Machine learning in cardiovascular magnetic resonance: basic concepts and applications.
Review in Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 129 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
129 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The Role of Artificial Intelligence in Predicting Outcomes by Cardiovascular Magnetic Resonance: A Comprehensive Systematic Review.Medicina (Kaunas, Lithuania) · 2022Pooled it
- A machine learning-based classification model for interstitial lung disease in rheumatoid arthritis.Frontiers in medicine · 2026Trial
- Artificial Intelligence in Pulmonary Hypertension: Current State and Future Prospects.Reviews in cardiovascular medicine · 2026Review
- Understanding HFpEF Through Imaging: Strengths, Limitations, and Current State of the Art.JACC. Cardiovascular imaging · 2026Review
- Imaging-anchored multiomics in cardiovascular disease: integrating cardiac imaging, bulk, single-cell, and spatial transcriptomics.Briefings in bioinformatics · 2026Review
- A comparison of vendor artificial intelligence solutions for automated post-processing of short-axis cine images in cardiovascular magnetic resonance imaging.Scientific reports · 2026Article
- Deep learning-based segmentation of T1 and T2 cardiac MRI maps for automated disease detection.European radiology · 2026Article
- Clinical Applications of Artificial Intelligence in Cardiovascular Imaging: Where Do We Stand?Life (Basel, Switzerland) · 2026Review
- Assessing the robustness of an artificial intelligence segmentation model for quantitative cardiovascular magnetic resonance imaging across cardiac phenotypes.The international journal of cardiovascular imaging · 2026Article
- The Emerging Role of Artificial Intelligence in the Assessment of Valvular Heart Disease with Cardiac Imaging.Current cardiology reports · 2026Review
- Myocardial radiomics of non-ischemic cardiomyopathy using cardiovascular magnetic resonance: current perspectives and future directions.Frontiers in cardiovascular medicine · 2026Review
- Deep learning for cardiac MRI: performance evidence and barriers to clinical integration. A Systematic Review and Meta-Analysis.European heart journal. Imaging methods and practice · 2026Article
- Artificial Intelligence in Tetralogy of Fallot: From Prenatal Diagnosis to Lifelong Management: A Narrative Review.Bioengineering (Basel, Switzerland) · 2025Review
- A semi-automated quality assurance tool for cardiovascular magnetic resonance imaging: application to outlier detection, artificial intelligence evaluation and trainee feedback.BMC medical informatics and decision making · 2025Article
- Robust myocardium detection and scar severity classification in LGE-CMR using ScarYOLO and contrastive learning.European journal of medical research · 2025Article
- Cardiac Magnetic Resonance Imaging in Heart Failure With Preserved Ejection Fraction.Circulation. Cardiovascular imaging · 2025Review
- Consequences of Right Heart Disease for Cardiac Electrophysiology and Arrhythmias: Cellular and Structural Mechanisms.CJC open · 2025Review
- Open-source cardiac magnetic resonance fingerprinting.Magma (New York, N.Y.) · 2025Article
- Machine Learning for Coronary Plaque Characterization: A Multimodal Review of OCT, IVUS, and CCTA.Diagnostics (Basel, Switzerland) · 2025Review
- Review
69 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Machine learning (ML) is making a dramatic impact on cardiovascular magnetic resonance (CMR) in many ways. This review seeks to highlight the major areas in CMR where ML, and deep learning in particular, can assist clinicians and engineers in improving imaging efficiency, quality, image analysis and interpretation, as well as patient evaluation. We discuss recent developments in the field of ML relevant to CMR in the areas of image acquisition & reconstruction, image analysis, diagnostic evaluation and derivation of prognostic information. To date, the main impact of ML in CMR has been to significantly reduce the time required for image segmentation and analysis. Accurate and reproducible fully automated quantification of left and right ventricular mass and volume is now available in commercial products. Active research areas include reduction of image acquisition and reconstruction time, improving spatial and temporal resolution, and analysis of perfusion and myocardial mapping. Although large cohort studies are providing valuable data sets for ML training, care must be taken in extending applications to specific patient groups. Since ML algorithms can fail in unpredictable ways, it is important to mitigate this by open source publication of computational processes and datasets. Furthermore, controlled trials are needed to evaluate methods across multiple centers and patient groups.
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.