Comparison of machine learning and deep learning for view identification from cardiac magnetic resonance images.

Journal: Clinical imaging
Published Date: Nov 19, 2021

Abstract

BACKGROUND: Artificial intelligence is increasingly utilized to aid in the interpretation of cardiac magnetic resonance (CMR) studies. One of the first steps is the identification of the imaging plane depicted, which can be achieved by both deep learning (DL) and classical machine learning (ML) techniques without user input. We aimed to compare the accuracy of ML and DL for CMR view classification and to identify potential pitfalls during training and testing of the algorithms.

Authors

  • Daksh Chauhan
    University of Chicago, Chicago, IL, United States of America.
  • Emeka Anyanwu
    Department of Medicine, University of Chicago, Chicago, IL, United States of America.
  • Jacob Goes
    Illinois Institute of Technology, Chicago, IL, United States of America.
  • Stephanie A Besser
    Department of Medicine, University of Chicago, Chicago, IL, United States of America.
  • Simran Anand
    University Pompeu Fabra, Barcelona, Spain.
  • Ravi Madduri
    Data Science and Learning Department, Argonne National Laboratory, Lemont, IL, United States of America.
  • Neil Getty
    Illinois Institute of Technology, Chicago, IL, United States of America; Data Science and Learning Department, Argonne National Laboratory, Lemont, IL, United States of America.
  • Sebastian Kelle
    Department of Internal Medicine/Cardiology German Heart Center, Berlin, Germany.
  • Keigo Kawaji
    Illinois Institute of Technology, Chicago, IL, United States of America.
  • Victor Mor-Avi
    Cardiac Imaging Center, University of Chicago Medical Center, Chicago, Illinois.
  • Amit R Patel
    Cardiac Imaging Center, University of Chicago Medical Center, Chicago, Illinois.

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