Development of a Deep Learning Network to Classify Inferior Vena Cava Collapse to Predict Fluid Responsiveness.

Journal: Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine
PMID: 33038035

Abstract

OBJECTIVES: To create a deep learning algorithm capable of video classification, using a long short-term memory (LSTM) network, to analyze collapsibility of the inferior vena cava (IVC) to predict fluid responsiveness in critically ill patients.

Authors

  • Michael Blaivas
    Department of Emergency Medicine, University of South Carolina School of Medicine, Columbia, South Carolina, USA.
  • Laura Blaivas
    Michigan State University, East Lansing, Michigan, USA.
  • Gary Philips
    Center for Biostatistics, Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio, USA.
  • Roland Merchant
    Department of Emergency Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
  • Mitchell Levy
    Department of Medicine, Division of Pulmonary Critical Care and Sleep, Warren Alert Medical School of Brown University, Providence, Rhode Island, USA.
  • Adeel Abbasi
    Department of Medicine, Division of Pulmonary Critical Care and Sleep, Warren Alert Medical School of Brown University, Providence, Rhode Island, USA.
  • Carsten Eickhoff
    Department of Computer Science, ETH Zurich, Zurich, Switzerland; Center for Biomedical Informatics, Brown University, Providence, RI, USA.
  • Nathan Shapiro
    Department of Emergency Medicine, Beth Israel Deaconess Hospital, Harvard Medical School, Boston, Massachusetts, USA.
  • Keith Corl
    Department of Medicine, Division of Pulmonary Critical Care and Sleep, Warren Alert Medical School of Brown University, Providence, Rhode Island, USA.

Keywords

External Resources

Popular Topics
Recent Journals