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Interpretable machine learning for automated left...
Journal article

Interpretable machine learning for automated left ventricular scar quantification in hypertrophic cardiomyopathy patients

Abstract

Scar quantification on cardiovascular magnetic resonance (CMR) late gadolinium enhancement (LGE) images is important in risk stratifying patients with hypertrophic cardiomyopathy (HCM) due to the importance of scar burden in predicting clinical outcomes. We aimed to develop a machine learning (ML) model that contours left ventricular (LV) endo- and epicardial borders and quantifies CMR LGE images from HCM patients.We retrospectively studied …

Authors

Navidi Z; Sun J; Chan RH; Hanneman K; Al-Arnawoot A; Munim A; Rakowski H; Maron MS; Woo A; Wang B

Journal

PLOS Digital Health, Vol. 2, No. 1,

Publisher

Public Library of Science (PLoS)

DOI

10.1371/journal.pdig.0000159

ISSN

2767-3170