Key result
Deep convolutional neural networks enable automated quantification of cardiac magnetic resonance scar volume in patients with hypertrophic cardiomyopathy.
Why the study?
Does automated cardiac MR scar quantification using deep convolutional neural networks improve scar volume quantification compared to manual delineation in patients with hypertrophic cardiomyopathy?
Does automated cardiac MR scar quantification using deep convolutional neural networks improve scar volume quantification compared to manual delineation in patients with hypertrophic cardiomyopathy?
Answers are grounded in this paper and cite their sources.
Automated deep convolutional neural networks are being evaluated for cardiac MR scar quantification in hypertrophic cardiomyopathy to replace manual delineation.
Scar volume quantified by cardiovascular magnetic resonance (CMR) with late gadolinium enhancement (LGE) is a novel imaging biomarker for risk stratification in patients with hypertrophic cardiomyopathy (HCM) [(1)][1]. In current practice, scar quantification often relies on manual delineation of
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Fahmy et al. (2018) studied this question. Deep convolutional neural networks enable automated quantification of cardiac magnetic resonance scar volume in patients with hypertrophic cardiomyopathy.
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