Machine Learning-Based Critical Congenital Heart Disease Screening Using Dual-Site Pulse Oximetry Measurements.

Journal: Journal of the American Heart Association
PMID: 38879455

Abstract

BACKGROUND: Oxygen saturation (Spo) screening has not led to earlier detection of critical congenital heart disease (CCHD). Adding pulse oximetry features (ie, perfusion data and radiofemoral pulse delay) may improve CCHD detection, especially coarctation of the aorta (CoA). We developed and tested a machine learning (ML) pulse oximetry algorithm to enhance CCHD detection.

Authors

  • Heather Siefkes
    Pediatrics, University of California, Davis, Sacramento, CA, USA.
  • Luca Cerny Oliveira
    Electrical and Computer Engineering, University of California, Davis, Davis, CA, USA.
  • Robert Koppel
    Department of Pediatrics, Cohen Children's Medical Center Zucker School of Medicine at Hofstra/Northwell New Hyde Park NY.
  • Whitnee Hogan
    University of Utah, Primary Children's Hospital Salt Lake City UT.
  • Meena Garg
    Department of Pediatrics University of California Los Angeles CA.
  • Erlinda Manalo
    Department of Pediatrics Sutter Sacramento Medical Center Sacramento CA.
  • Nicole Cresalia
    Department of Pediatrics University of California San Francisco CA.
  • Zhengfeng Lai
    Electrical and Computer Engineering, University of California, Davis, Davis, CA, USA.
  • Daniel Tancredi
    Department of Pediatrics University of California Davis CA.
  • Satyan Lakshminrusimha
    Department of Pediatrics University of California Davis CA.
  • Chen-Nee Chuah
    Department of Electrical and Computer Engineering University of California Davis California USA.

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