ArticleJACC. Cardiovascular imaging2020
Fully Automated, Quality-Controlled Cardiac Analysis From CMR: Validation and Large-Scale Application to Characterize Cardiac Function.
Article in JACC. Cardiovascular imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 87 papers, 3 of them syntheses that pooled it.
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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
87 citing papers in PubMed, 3 syntheses or guidelines pooled it, 180 citations in OpenAlex.
- Society for Cardiovascular Magnetic Resonance reference values ("normal values") in cardiovascular magnetic resonance: 2025 update.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2025Guideline
- Systematic review and meta-analysis for the value of cardiac magnetic resonance strain to predict cardiac outcomes.Scientific reports · 2024Pooled it
- Development and application of artificial intelligence in cardiac imaging.The British journal of radiology · 2020Pooled it
- Artificial intelligence fully automated myocardial strain quantification for risk stratification following acute myocardial infarction.Scientific reports · 2022Trial
- Assessment of Bi-Ventricular and Bi-Atrial Areas Using Four-Chamber Cine Cardiovascular Magnetic Resonance Imaging: Fully Automated Segmentation with a U-Net Convolutional Neural Network.International journal of environmental research and public health · 2022Trial
- Coronary CT Angiography in PCI Planning: Advances, Clinical Applications, and Challenges.Journal of cardiovascular development and disease · 2026Review
- Artificial intelligence in cardiology in the current era: a narrative review.Cardiovascular diagnosis and therapy · 2026Review
- Artificial Intelligence in Cardiovascular Imaging: From Automated Acquisition to Precision Diagnostics and Clinical Decision Support.Medical sciences (Basel, Switzerland) · 2026Review
- A streamlined CMR-derived machine-learning model for estimating cardiovascular biological age: development and validation in the UK-biobank and multi-ethnic study of atherosclerosis.European heart journal. Cardiovascular Imaging · 2026Article
- Computer-aided characterization of the arrhythmogenic substrate after myocardial infarction.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2026Review
- A pipeline for developing deep learning prognostic prediction models in cardiac magnetic resonance image analysis.European heart journal. Digital health · 2026Article
- AI-powered SPOT imaging for enhanced myocardial scar detection and quantification.Nature communications · 2025Article
- 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
- Predicting prognosis of light-chain cardiac amyloidosis by magnetic resonance imaging and deep learning.European heart journal. Cardiovascular Imaging · 2025Article
- Cardiac magnetic resonance imaging in the assessment of cardiac injury and toxicity from cancer-related chemotherapy.Pediatric radiology · 2025Review
- Deep representation learning of electrocardiogram reveals biological insights in cardiac phenotypes and cardiovascular diseases.iScience · 2025Article
- Shaping Quality in Cardiovascular Magnetic Resonance: A Comparative Study of Segmentation Approaches by Trainees and Experts.CJC open · 2025Article
- Cardiac magnetic resonance markers of pre-clinical hypertrophic and dilated cardiomyopathy in genetic variant carriers.BMC medicine · 2025Article
- Standardized pancreatic MRI-T1 measurement methods: comparison between manual measurement and a semi-automated pipeline with automatic quality control.The British journal of radiology · 2025Article
- An investigation into the causes of race bias in artificial intelligence-based cine cardiac magnetic resonance segmentation.European heart journal. Digital health · 2025Article
27 more citing papers are in PubMed but not listed here.
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
Abstract
objectivesThis study sought to develop a fully automated framework for cardiac function analysis from cardiac magnetic resonance (CMR), including comprehensive quality control (QC) algorithms to detect erroneous output.
backgroundAnalysis of cine CMR imaging using deep learning (DL) algorithms could automate ventricular function assessment. However, variable image quality, variability in phenotypes of disease, and unavoidable weaknesses in training of DL algorithms currently prevent their use in clinical practice.
methodsThe framework consists of a pre-analysis DL image QC, followed by a DL algorithm for biventricular segmentation in long-axis and short-axis views, myocardial feature-tracking (FT), and a post-analysis QC to detect erroneous results. The study validated the framework in healthy subjects and cardiac patients by comparison against manual analysis (n = 100) and evaluation of the QC steps' ability to detect erroneous results (n = 700). Next, this method was used to obtain reference values for cardiac function metrics from the UK Biobank.
resultsAutomated analysis correlated highly with manual analysis for left and right ventricular volumes (all r > 0.95), strain (circumferential r = 0.89, longitudinal r > 0.89), and filling and ejection rates (all r ≥ 0.93). There was no significant bias for cardiac volumes and filling and ejection rates, except for right ventricular end-systolic volume (bias +1.80 ml; p = 0.01). The bias for FT strain was <1.3%. The sensitivity of detection of erroneous output was 95% for volume-derived parameters and 93% for FT strain. Finally, reference values were automatically derived from 2,029 CMR exams in healthy subjects.
conclusionsThe study demonstrates a DL-based framework for automated, quality-controlled characterization of cardiac function from cine CMR, without the need for direct clinician oversight.
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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.