Fully Automated Artificial Intelligence Assessment of Aortic Stenosis by Echocardiography.

Journal: Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography
PMID:

Abstract

BACKGROUND: Aortic stenosis (AS) is a common form of valvular heart disease, present in over 12% of the population age 75 years and above. Transthoracic echocardiography (TTE) is the first line of imaging in the adjudication of AS severity but is time-consuming and requires expert sonographic and interpretation capabilities to yield accurate results. Artificial intelligence (AI) technology has emerged as a useful tool to address these limitations but has not yet been applied in a fully hands-off manner to evaluate AS. Here, we correlate artificial neural network measurements of key hemodynamic AS parameters to experienced human reader assessment.

Authors

  • Hema Krishna
    Division of Cardiology, University of Illinois at Chicago, Chicago, Illinois; Jesse Brown VA Medical Center, Chicago, Illinois.
  • Kevin Desai
    Department of Medicine, University of Illinois at Chicago, Chicago, Illinois.
  • Brody Slostad
    Bluhm Cardiovascular Institute, Northwestern University, Chicago, Illinois.
  • Siddharth Bhayani
    Department of Medicine, University of Illinois at Chicago, Chicago, Illinois.
  • Joshua H Arnold
    Department of Medicine, University of Illinois at Chicago, Chicago, Illinois.
  • Wouter Ouwerkerk
    National Heart Centre Singapore, Singapore, Singapore.
  • Yoran Hummel
    Us2.ai, 2 College Rd, #02-00, Singapore 169850, Singapore.
  • Carolyn S P Lam
    Duke-NUS Medical School, 8 College Rd, Singapore 169857, Singapore.
  • Justin Ezekowitz
    Canadian VIGOUR Centre, University of Alberta, Edmonton, Alberta, Canada.
  • Matthew Frost
    Us2.ai, Singapore, Singapore.
  • Zhubo Jiang
    Us2.ai, Singapore, Singapore.
  • Cyril Equilbec
    Us2.ai, Singapore.
  • Aamir Twing
    Division of Cardiology, University of Illinois at Chicago, Chicago, Illinois.
  • Patricia A Pellikka
    Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA.
  • Leon Frazin
    Division of Cardiology, University of Illinois at Chicago, Chicago, Illinois; Jesse Brown VA Medical Center, Chicago, Illinois.
  • Mayank Kansal
    Division of Cardiology, University of Illinois at Chicago, Chicago, Illinois; Jesse Brown VA Medical Center, Chicago, Illinois. Electronic address: mmkansal@uic.edu.