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

Journal: Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
PMID: 35761339

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.

Authors

  • Ahmed S Fahmy
  • Ethan J Rowin
    From the Departments of Medicine (Cardiovascular Division) (A.S.F., U.N., W.J.M., R.N.) and Radiology (W.J.M.), Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Ave, Boston, MA 02215; Toronto General Hospital, University Health Network, Toronto, Ontario, Canada (R.H.C.); and Hypertrophic Cardiomyopathy Center, Division of Cardiology, Tufts Medical Center, Boston, Mass (E.J.R., M.S.M.).
  • Arghavan Arafati
    The Edwards Lifesciences Center for Advanced Cardiovascular Technology, University of California, 2410 Engineering Hall, Irvine, CA 92697-2730, USA.
  • Talal Al-Otaibi
    Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Ave, Boston, MA, 02215, USA.
  • Martin S Maron
  • Reza Nezafat

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