ArticleEuropean heart journal. Digital health2023
An artificial intelligence tool for automated analysis of large-scale unstructured clinical cine cardiac magnetic resonance databases.
Article in European heart journal. Digital health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 23 citations in OpenAlex.
- Advancing Patient-Centred AI With Adaptive Machine Learning.Cancer medicine · 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
- A pipeline for developing deep learning prognostic prediction models in cardiac magnetic resonance image analysis.European heart journal. Digital health · 2026Article
- Evaluation of Projection Images for Visual Quality Control of Automated Left and Right Lung Segmentations on T1-Weighted MRI in Large-Scale Clinical Cohort Studies.Tomography (Ann Arbor, Mich.) · 2025Article
- An investigation into the causes of race bias in artificial intelligence-based cine cardiac magnetic resonance segmentation.European heart journal. Digital health · 2025Article
- Artificial Intelligence in Breast Reconstruction: A Narrative Review.Medicina (Kaunas, Lithuania) · 2025Review
- Progress in the Clinical Application of Artificial Intelligence for Left Ventricle Analysis in Cardiac Magnetic Resonance.Reviews in cardiovascular medicine · 2024Review
- Segmentation-Free Estimation of Left Ventricular Ejection Fraction Using 3D CNN Is Reliable and Improves as Multiple Cardiac MRI Cine Orientations Are Combined.Biomedicines · 2024Article
- Automated Quantification of Simple and Complex Aortic Flow Using 2D Phase Contrast MRI.Medicina (Kaunas, Lithuania) · 2024Article
- Development and validation of AI-derived segmentation of four-chamber cine cardiac magnetic resonance.European radiology experimental · 2024Article
Corrections and comments
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Authors and funding
12 authors at 3 institutions in 3 countries.
Funding
Abstract
Aims: Artificial intelligence (AI) techniques have been proposed for automating analysis of short-axis (SAX) cine cardiac magnetic resonance (CMR), but no CMR analysis tool exists to automatically analyse large (unstructured) clinical CMR datasets. We develop and validate a robust AI tool for start-to-end automatic quantification of cardiac function from SAX cine CMR in large clinical databases. Methods and results: Our pipeline for processing and analysing CMR databases includes automated steps to identify the correct data, robust image pre-processing, an AI algorithm for biventricular segmentation of SAX CMR and estimation of functional biomarkers, and automated post-analysis quality control to detect and correct errors. The segmentation algorithm was trained on 2793 CMR scans from two NHS hospitals and validated on additional cases from this dataset ( Conclusion: We show that our proposed tool, which combines image pre-processing steps, a domain-generalizable AI algorithm trained on a large-scale multi-domain CMR dataset and quality control steps, allows robust analysis of (clinical or research) databases from multiple centres, vendors, and cardiac diseases. This enables translation of our tool for use in fully automated processing of large multi-centre databases.
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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.