ReviewReviews in cardiovascular medicine2024
Progress in the Clinical Application of Artificial Intelligence for Left Ventricle Analysis in Cardiac Magnetic Resonance.
Review in Reviews in cardiovascular medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Advancements in Cardiac Magnetic Resonance Imaging: Innovations, Challenges, and Future Directions.Reviews in cardiovascular medicine · 2026Review
- Investigation of Concordance between Artificial Intelligence and Manual Measurements of the Cardiac Parameters in Cardiac Magnetic Resonance Imaging.Journal of imaging informatics in medicine · 2026Article
- Myocardial Fibrosis in Cardiovascular Disease: An Integrative Biomarker-Imaging Framework Linking Molecular Mechanisms to Structural Phenotypes.Journal of clinical medicine · 2026Review
- Application and prospect of artificial intelligence in diagnostic imaging of prostate cancer.NPJ digital medicine · 2026Review
- Factors Affecting the Quality of Non-contrast Coronary Magnetic Resonance Angiography Images: Challenges and Change.Reviews in cardiovascular medicine · 2025Review
- Artificial Intelligence Performance in Cardiac Magnetic Resonance Strain Analysis for Aortic Stenosis: Validation with Echocardiography and Healthy Controls.Medicina (Kaunas, Lithuania) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cardiac magnetic resonance (CMR) imaging enables a one-stop assessment of heart structure and function. Artificial intelligence (AI) can simplify and automate work flows and improve image post-processing speed and diagnostic accuracy; thus, it greatly affects many aspects of CMR. This review highlights the application of AI for left heart analysis in CMR, including quality control, image segmentation, and global and regional functional assessment. Most recent research has focused on segmentation of the left ventricular myocardium and blood pool. Although many algorithms have shown a level comparable to that of human experts, some problems, such as poor performance of basal and apical segmentation and false identification of myocardial structure, remain. Segmentation of myocardial fibrosis is another research hotspot, and most patient cohorts of such studies have hypertrophic cardiomyopathy. Whether the above methods are applicable to other patient groups requires further study. The use of automated CMR interpretation for the diagnosis and prognosis assessment of cardiovascular diseases demonstrates great clinical potential. However, prospective large-scale clinical trials are needed to investigate the real-word application of AI technology in clinical practice.
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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.