Interpretable machine learning for automated left ventricular scar quantification in hypertrophic ca...
Interpretable machine learning for automated left ventricular scar quantification in hypertrophic cardiomyopathy patients
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United States: Public Library of Science
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Language
English
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Publisher
United States: Public Library of Science
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Contents
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...
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Full title
Interpretable machine learning for automated left ventricular scar quantification in hypertrophic cardiomyopathy patients
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TN_cdi_doaj_primary_oai_doaj_org_article_7bdeae2d472f4e7484de1f803c5d25a7
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_7bdeae2d472f4e7484de1f803c5d25a7
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ISSN
2767-3170
E-ISSN
2767-3170
DOI
10.1371/journal.pdig.0000159
