Deep learning-based prediction of future myocardial infarction using invasive coronary angiography: a feasibility study.

Journal: Open heart
Published Date: Jan 1, 2023

Abstract

BACKGROUND: Angiographic parameters can facilitate the risk stratification of coronary lesions but remain insufficient in the prediction of future myocardial infarction (MI).

Authors

  • Thabo Mahendiran
    Cardiology Department, Lausanne University Center Hospital, Lausanne, Switzerland.
  • Dorina Thanou
    Chair of Mathematical Data Science and LTS4 laboratory, EPFL, Lausanne, Switzerland.
  • Ortal Senouf
    Chair of Mathematical Data Science and LTS4 laboratory, EPFL, Lausanne, Switzerland.
  • David Meier
    Cardiology Department, Lausanne University Center Hospital, Lausanne, Switzerland.
  • Nicolas Dayer
    Cardiology Department, Lausanne University Center Hospital, Lausanne, Switzerland.
  • Fahrang Aminfar
    Cardiology Department, Lausanne University Center Hospital, Lausanne, Switzerland.
  • Denise Auberson
    Cardiology Department, Lausanne University Center Hospital, Lausanne, Switzerland.
  • Omar Raita
    Chair of Mathematical Data Science and LTS4 laboratory, EPFL, Lausanne, Switzerland.
  • Pascal Frossard
    LTS4 laboratory, School of Engineering, EPFL, Lausanne, Switzerland.
  • Mattia Pagnoni
    Cardiology Department, Lausanne University Center Hospital, Lausanne, Switzerland.
  • Stéphane Cook
    Cardiology Department, University and hospital Fribourg, Fribourg, Switzerland.
  • Bernard De Bruyne
    Cardiovascular Center OLV Aalst, Belgium.
  • Olivier Muller
    Cardiology Department, Lausanne University Center Hospital, Lausanne, Switzerland.
  • Emmanuel Abbe
    PACM and Department of EE, Princeton University, Princeton, NJ 08544, USA.
  • Stephane Fournier
    Cardiology Department, Lausanne University Center Hospital, Lausanne, Switzerland stephane.fournier@chuv.ch emmanuel.abbe@epfl.ch.

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