A Multimodal Video-Based AI Biomarker for Aortic Stenosis Development and Progression.

Journal: JAMA cardiology
Published Date:

Abstract

IMPORTANCE: Aortic stenosis (AS) is a major public health challenge with a growing therapeutic landscape, but current biomarkers do not inform personalized screening and follow-up. A video-based artificial intelligence (AI) biomarker (Digital AS Severity index [DASSi]) can detect severe AS using single-view long-axis echocardiography without Doppler characterization.

Authors

  • Evangelos K Oikonomou
    Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford, UK.
  • Gregory Holste
    Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USA.
  • Neal Yuan
    Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
  • Andreas Coppi
  • Robert L McNamara
  • Norrisa A Haynes
    Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut.
  • Amit N Vora
    Heart and Vascular Institute, University of Pittsburgh Medical Center, Harrisburg, Pennsylvania; Department of Cardiovascular Medicine, Yale University School of Medicine, New Haven, Connecticut.
  • Eric J Velazquez
    Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut.
  • Fan Li
    Department of Instrument Science and Engineering, School of SEIEE, Shanghai Jiao Tong University, Shanghai 200240, China.
  • Venu Menon
    Department of Cardiovascular Medicine, Cleveland Clinic, Cleveland, OH.
  • Samir R Kapadia
    Department of Cardiovascular Medicine, Cleveland Clinic, Cleveland, Ohio.
  • Thomas M Gill
    Department of Internal Medicine (T.M.G.), Yale School of Medicine, New Haven, CT.
  • Girish N Nadkarni
    Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Harlan M Krumholz
    Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.
  • Zhangyang Wang
    Departments of Electrical and Computer Engineering & Computer Science and Engineering Texas A&M University, College Station, TX 77840.
  • David Ouyang
    Division of Artificial Intelligence, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA.
  • Rohan Khera
    Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.