ArticleFrontiers in cardiovascular medicine2021
Deep Learning Analysis of Cardiac MRI in Legacy Datasets: Multi-Ethnic Study of Atherosclerosis.
Article in Frontiers in cardiovascular medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 16 citations in OpenAlex.
- Advancing Brain and Body Vascular Imaging in Bipolar Disorder: A Report From the International Society for Bipolar Disorders Vascular Task Force.Bipolar disorders · 2026Review
- Relationship between left ventricular shape and cardiovascular risk factors: comparison between the Multi-Ethnic Study of Atherosclerosis and UK Biobank.Heart (British Cardiac Society) · 2025Article
- Towards a cardiovascular magnetic resonance foundation model for multi-task cardiac image analysis.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2025Article
- Application of artificial intelligence model in pathological staging and prognosis of clear cell renal cell carcinoma.Discover oncology · 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
- Artificial Intelligence in Cardiology and Atherosclerosis in the Context of Precision Medicine: A Scoping Review.Applied bionics and biomechanics · 2024Article
- An artificial intelligence tool for automated analysis of large-scale unstructured clinical cine cardiac magnetic resonance databases.European heart journal. Digital health · 2023Article
- A deep learning approach for fully automated cardiac shape modeling in tetralogy of Fallot.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2023Article
- Multi-Ethnic Study of Atherosclerosis: Relationship between Left Ventricular Shape at Cardiac MRI and 10-year Outcomes.Radiology · 2023Article
- Multilevel comparison of deep learning models for function quantification in cardiovascular magnetic resonance: On the redundancy of architectural variations.Frontiers in cardiovascular medicine · 2023Article
- Le Cœur en Sabot: shape associations with adverse events in repaired tetralogy of Fallot.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2022Article
- Longitudinal trajectories of left ventricular myocardial remodeling: associations with cardiovascular risk factors in the multi-ethnic study of atherosclerosis.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic ResonanceArticle
Corrections and comments
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Authors and funding
12 authors at 7 institutions in 5 countries.
Funding
Abstract
The Multi-Ethnic Study of Atherosclerosis (MESA), begun in 2000, was the first large cohort study to incorporate cardiovascular magnetic resonance (CMR) to study the mechanisms of cardiovascular disease in over 5,000 initially asymptomatic participants, and there is now a wealth of follow-up data over 20 years. However, the imaging technology used to generate the CMR images is no longer in routine use, and methods trained on modern data fail when applied to such legacy datasets. This study aimed to develop a fully automated CMR analysis pipeline that leverages the ability of machine learning algorithms to enable extraction of additional information from such a large-scale legacy dataset, expanding on the original manual analyses. We combined the original study analyses with new annotations to develop a set of automated methods for customizing 3D left ventricular (LV) shape models to each CMR exam and build a statistical shape atlas. We trained VGGNet convolutional neural networks using a transfer learning sequence between two-chamber, four-chamber, and short-axis MRI views to detect landmarks. A U-Net architecture was used to detect the endocardial and epicardial boundaries in short-axis images. The landmark detection network accurately predicted mitral valve and right ventricular insertion points with average error distance <2.5 mm. The agreement of the network with two observers was excellent (intraclass correlation coefficient >0.9). The segmentation network produced average Dice score of 0.9 for both myocardium and LV cavity. Differences between the manual and automated analyses were small, i.e., <1.0 ± 2.6 mL/m
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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.