Automated Endotracheal Tube Placement Check Using Semantically Embedded Deep Neural Networks.

Journal: Academic radiology
PMID:

Abstract

RATIONALE AND OBJECTIVES: To develop artificial intelligence (AI) system that assists in checking endotracheal tube (ETT) placement on chest X-rays (CXRs) and evaluate whether it can move into clinical validation as a quality improvement tool.

Authors

  • Matthew S Brown
    University of California Los Angeles David Geffen School of Medicine, Los Angeles, California, USA.
  • Koon-Pong Wong
    Department of Radiological Sciences, Center for Computer Vision and Imaging Biomarkers, David Geffen School of Medicine at UCLA, 924 Westwood Blvd., Suite 615, Los Angeles, CA 90024.
  • Liza Shrestha
    Department of Radiological Sciences, Center for Computer Vision and Imaging Biomarkers, David Geffen School of Medicine at UCLA, 924 Westwood Blvd., Suite 615, Los Angeles, CA 90024.
  • Muhammad Wahi-Anwar
    Department of Radiological Sciences, Center for Computer Vision and Imaging Biomarkers, David Geffen School of Medicine at UCLA, 924 Westwood Blvd., Suite 615, Los Angeles, CA 90024.
  • Morgan Daly
    Department of Radiological Sciences, Center for Computer Vision and Imaging Biomarkers, David Geffen School of Medicine at UCLA, 924 Westwood Blvd., Suite 615, Los Angeles, CA 90024.
  • George Foster
    Department of Radiological Sciences, Center for Computer Vision and Imaging Biomarkers, David Geffen School of Medicine at UCLA, 924 Westwood Blvd., Suite 615, Los Angeles, CA 90024.
  • Fereidoun Abtin
  • Kathleen L Ruchalski
    Department of Radiological Sciences, Center for Computer Vision and Imaging Biomarkers, David Geffen School of Medicine at UCLA, 924 Westwood Blvd., Suite 615, Los Angeles, CA 90024.
  • Jonathan G Goldin
    Department of Radiological Sciences, Center for Computer Vision and Imaging Biomarkers, David Geffen School of Medicine at UCLA, 924 Westwood Blvd., Suite 615, Los Angeles, CA 90024.
  • Dieter Enzmann