ArticleThe international journal of cardiovascular imaging2018
Fully-automated left ventricular mass and volume MRI analysis in the UK Biobank population cohort: evaluation of initial results.
Article in The international journal of cardiovascular imaging, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed, 66 citations in OpenAlex.
- Towards a Compositional Framework for Describing Human Phenotypes.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Shape and Scale in Quantifying Aortic Morphology Evolution and Chronicity.Cardiovascular engineering and technology · 2026Article
- Sex disparities in deep learning estimation of ejection fraction from cardiac magnetic resonance imaging.NPJ digital medicine · 2026Article
- Automated Deep Learning Phenotyping of Tricuspid Regurgitation in Echocardiography.JAMA cardiology · 2025Article
- Automated Deep Learning Based Cardiac Quantification in Hypertrophic Cardiomyopathy: A Comparative Study with Manual Segmentation.Acta medica Lituanica · 2025Article
- A novel deep learning method for large-scale analysis of bone marrow adiposity using UK Biobank Dixon MRI data.Computational and structural biotechnology journal · 2024Article
- Development and performance evaluation of fully automated deep learning-based models for myocardial segmentation on T1 mapping MRI data.Scientific reports · 2024Article
- A Multimodality Video-Based AI Biomarker For Aortic Stenosis Development And Progression.medRxiv : the preprint server for health sciences · 2024Article
- Genetically predicted androgenic profiles and adverse cardiac markers: a sex-specific Mendelian randomization study.ESC heart failure · 2023Article
- Correcting bias in cardiac geometries derived from multimodal images using spatiotemporal mapping.Scientific reports · 2023Article
- Clinical and genetic associations of deep learning-derived cardiac magnetic resonance-based left ventricular mass.Nature communications · 2023Article
- Artificial intelligence in cardiovascular imaging: enhancing image analysis and risk stratification.BJR open · 2023Review
- Frequency, Penetrance, and Variable Expressivity of Dilated Cardiomyopathy-Associated Putative Pathogenic Gene Variants in UK Biobank Participants.Circulation · 2022Article
- Training and clinical testing of artificial intelligence derived right atrial cardiovascular magnetic resonance measurements.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2022Article
- AI Based CMR Assessment of Biventricular Function: Clinical Significance of Intervendor Variability and Measurement Errors.JACC. Cardiovascular imaging · 2022Article
- Effect of age and sex on fully automated deep learning assessment of left ventricular function, volumes, and contours in cardiac magnetic resonance imaging.The international journal of cardiovascular imaging · 2021Article
- Balancing Speed and Accuracy in Cardiac Magnetic Resonance Function Post-Processing: Comparing 2 Levels of Automation in 3 Vendors to Manual Assessment.Diagnostics (Basel, Switzerland) · 2021Article
- Deep learning to estimate cardiac magnetic resonance-derived left ventricular mass.Cardiovascular digital health journal · 2021Article
- Retraining Convolutional Neural Networks for Specialized Cardiovascular Imaging Tasks: Lessons from Tetralogy of Fallot.Pediatric cardiology · 2021Article
- Cardiovascular magnetic resonance imaging in the UK Biobank: a major international health research resource.European heart journal. Cardiovascular Imaging · 2021Article
Corrections and comments
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Authors and funding
18 authors at 5 institutions in 4 countries.
Funding
Abstract
UK Biobank, a large cohort study, plans to acquire 100,000 cardiac MRI studies by 2020. Although fully-automated left ventricular (LV) analysis was performed in the original acquisition, this was not designed for unsupervised incorporation into epidemiological studies. We sought to evaluate automated LV mass and volume (Siemens syngo InlineVF versions D13A and E11C), against manual analysis in a substantial sub-cohort of UK Biobank participants. Eight readers from two centers, trained to give consistent results, manually analyzed 4874 UK Biobank cases for LV end-diastolic volume (EDV), end-systolic volume (ESV), stroke volume (SV), ejection fraction (EF) and LV mass (LVM). Agreement between manual and InlineVF automated analyses were evaluated using Bland-Altman analysis and the intra-class correlation coefficient (ICC). Tenfold cross-validation was used to establish a linear regression calibration between manual and InlineVF results. InlineVF D13A returned results in 4423 cases, whereas InlineVF E11C returned results in 4775 cases and also reported LVM. Rapid visual assessment of the E11C results found 178 cases (3.7%) with grossly misplaced contours or landmarks. In the remaining 4597 cases, LV function showed good agreement: ESV -6.4 ± 9.0 ml, 0.853 (mean ± SD of the differences, ICC) EDV -3.0 ± 11.6 ml, 0.937; SV 3.4 ± 9.8 ml, 0.855; and EF 3.5 ± 5.1%, 0.586. Although LV mass was consistently overestimated (29.9 ± 17.0 g, 0.534) due to larger epicardial contours on all slices, linear regression could be used to correct the bias and improve accuracy. Automated InlineVF results can be used for case-control studies in UK Biobank, provided visual quality control and linear bias correction are performed. Improvements between InlineVF D13A and InlineVF E11C show the field is rapidly advancing, with further improvements expected in the near future.
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Registered trials
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