Artificial Intelligence of Arterial Doppler Waveforms to Predict Major Adverse Outcomes Among Patients Evaluated for Peripheral Artery Disease.

Journal: Journal of the American Heart Association
Published Date:

Abstract

BACKGROUND: Patients with peripheral artery disease are at increased risk for major adverse cardiac events, major adverse limb events, and all-cause death. Developing tools capable of identifying those patients with peripheral artery disease at greatest risk for major adverse events is the first step for outcome prevention. This study aimed to determine whether computer-assisted analysis of a resting Doppler waveform using deep neural networks can accurately identify patients with peripheral artery disease at greatest risk for adverse outcome events.

Authors

  • Robert D McBane
    Gonda Vascular Center Mayo Clinic Rochester MN.
  • Dennis H Murphree
    Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minnesota, USA.
  • David Liedl
    Gonda Vascular Center Mayo Clinic Rochester MN.
  • Francisco Lopez-Jimenez
    Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA.
  • Itzhak Zachi Attia
    Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA.
  • Adelaide M Arruda-Olson
    Mayo Clinic Rochester, MN.
  • Christopher G Scott
    Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA.
  • Naresh Prodduturi
    Quantitative Health Sciences Mayo Clinic Rochester MN.
  • Steve E Nowakowski
    Division of Engineering Mayo Clinic Rochester MN.
  • Thom W Rooke
    Gonda Vascular Center Mayo Clinic Rochester MN.
  • Ana I Casanegra
    Gonda Vascular Center Mayo Clinic Rochester MN.
  • Waldemar E Wysokinski
    Gonda Vascular Center Mayo Clinic Rochester MN.
  • Damon E Houghton
    Gonda Vascular Center Mayo Clinic Rochester MN.
  • Haraldur Bjarnason
    Gonda Vascular Center Mayo Clinic Rochester MN.
  • Paul W Wennberg
    Gonda Vascular Center Mayo Clinic Rochester MN.