| Title: | Evaluation of deep learning estimation of whole heart anatomy from automated cardiovascular magnetic resonance short- and long-axis analyses in UK Biobank |
| Journal: | European Heart Journal - Cardiovascular Imaging |
| Published: | 9 May 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/38723059/ |
| DOI: | https://doi.org/10.1093/ehjci/jeae123 |
| URL: | https://academic.oup.com/ehjcimaging/advance-article-pdf/doi/10.1093/ehjci/jeae123/57468631/jeae123.pdf |
| Citations: | 1 (1 in last 2 years) as of 8 Aug 2024 |
Publication 12118
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Abstract
AIMS: Standard methods of heart chamber volume estimation in cardiovascular magnetic resonance (CMR) typically utilize simple geometric formulae based on a limited number of slices. We aimed to evaluate whether an automated deep learning neural network prediction of 3D anatomy of all four chambers would show stronger associations with cardiovascular risk factors and disease than standard volume estimation methods in the UK Biobank.</p>
METHODS: A deep learning network was adapted to predict 3D segmentations of left and right ventricles (LV, RV) and atria (LA, RA) at ∼1mm isotropic resolution from CMR short and long axis 2D segmentations obtained from a fully automated machine learning pipeline in 4723 individuals with cardiovascular disease (CVD) and 5733 without in the UK Biobank. Relationships between volumes at end-diastole (ED) and end-systole (ES) and risk/disease factors were quantified using univariate, multivariate and logistic regression analyses. Strength of association between deep learning volumes and standard volumes was compared using the area under the receiving operator characteristic curve (AUC).</p>
RESULTS: Univariate and multivariate associations between deep learning volumes and most risk and disease factors were stronger than for standard volumes (higher R2 and more significant P values), particularly for sex, age, and body mass index. AUC for all logistic regressions were higher for deep learning volumes than standard volumes (p<0.001 for all four chambers at ED and ES).</p>
CONCLUSIONS: Neural network reconstructions of whole heart volumes had significantly stronger associations with cardiovascular disease and risk factors than standard volume estimation methods in an automatic processing pipeline.</p>
11 Authors
- Marica Muffoletto
- Hao Xu
- Richard Burns
- Avan Suinesiaputra
- Anastasia Nasopoulou
- Karl P Kunze
- Radhouene Neji
- Steffen E Petersen
- Steven A Niederer
- Daniel Rueckert
- Alistair A Young
1 Application
| Application ID | Title |
|---|---|
| 2964 | Description of cardiovascular phenotype in the UK Biobank population based on cardiovascular magnetic resonance and carotid ultrasound |
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