Using Machine Learning to Identify Intravenous Contrast Phases on Computed Tomography.

Journal: Computer methods and programs in biomedicine
Published Date: Dec 25, 2021

Abstract

PURPOSE: The purpose of the present work is to demonstrate the application of machine learning (ML) techniques to automatically identify the presence and physiologic phase of intravenous (IV) contrast in Computed Tomography (CT) scans of the Chest, Abdomen and Pelvis.

Authors

  • Raouf Muhamedrahimov
    Zebra Medical Vision LTD, Shfayim, Israel.
  • Amir Bar
    Zebra Medical Vision, Shfayim, Israel.
  • Jonathan Laserson
    Zebra Medical Vision LTD, Shfayim, Israel.
  • Ayelet Akselrod-Ballin
    From the Department of Healthcare Informatics, IBM Research, IBM R&D Labs, University of Haifa Campus, Mount Carmel, Haifa 3498825, Israel (A.A.B., M.C., Y.S., A.S., A.H., R.M., E.B., S.N., E.K., Y.G., M.R.Z.); MaccabiTech, MKM, Maccabi Healthcare Services, Tel Aviv, Israel (E.H., G.K., V.S.); and Department of Imaging, Assuta Medical Centers, Tel Aviv, Israel (M.G.).
  • Eldad Elnekave
    Zebra Medical Vision LTD, Shfayim, Israel. Eldad@zebra-med.com.

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