Development and Validation of a Deep Learning Strategy for Automated View Classification of Pediatric Focused Assessment With Sonography for Trauma.

Journal: Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine
Published Date: Aug 1, 2022

Abstract

OBJECTIVE: Pediatric focused assessment with sonography for trauma (FAST) is a sequence of ultrasound views rapidly performed by clinicians to diagnose hemorrhage. A technical limitation of FAST is the lack of expertise to consistently acquire all required views. We sought to develop an accurate deep learning view classifier using a large heterogeneous dataset of clinician-performed pediatric FAST.

Authors

  • Aaron E Kornblith
    Department of Emergency Medicine, University of California, San Francisco, CA, USA.
  • Newton Addo
    Department of Emergency Medicine, University of California, San Francisco, CA, USA.
  • Ruolei Dong
    Department of Bioengineering, University of California, Berkeley, CA, USA.
  • Robert Rogers
    Center for Digital Health Innovation, University of California, San Francisco, CA, USA.
  • Jacqueline Grupp-Phelan
    Division of Emergency Medicine, Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA.
  • Atul Butte
    Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, California, United States of America.
  • Pavan Gupta
    Center for Digital Health Innovation, University of California, San Francisco, CA, USA.
  • Rachael A Callcut
    Division of General Surgery, Department of Surgery, School of Medicine, University of California San Francisco, San Francisco, California, United States of America.
  • Rima Arnaout

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