Radiomics and deep learning for myocardial scar screening in hypertrophic cardiomyopathy.

ABSTRACT:

Background

Myocardial scar burden quantified using late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR), has important prognostic value in hypertrophic cardiomyopathy (HCM). However, nearly 50% of HCM patients have no scar but undergo repeated gadolinium-based CMR over their life span. We sought to develop an artificial intelligence (AI)-based screening model using radiomics and deep learning (DL) features extracted from balanced steady state free precession (bSSFP) cine sequences to identify HCM patients without scar.

Methods

We evaluated three AI-based screening models using bSSFP cine image features extracted by radiomics, DL, or combined DL-Radiomics. Images for 759 HCM patients (50 ± 16 years, 66% men) in a multi-center/vendor study were used to dev

SUBMITTER: Fahmy AS 

PROVIDER: S-EPMC9235098 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature