Development and External Validation of a Deep Learning Algorithm to Identify and Localize Subarachnoid Hemorrhage on CT Scans.

Journal: Neurology
Published Date: Jan 13, 2023

Abstract

BACKGROUND AND OBJECTIVES: In medical imaging, a limited number of trained deep learning algorithms have been externally validated and released publicly. We hypothesized that a deep learning algorithm can be trained to identify and localize subarachnoid hemorrhage (SAH) on head computed tomography (CT) scans and that the trained model performs satisfactorily when tested using external and real-world data.

Authors

  • Antonios Thanellas
    From the Department of Information Management (A.T.), Helsinki University Hospital, Helsinki, Finland; Department of Neurosurgery, University of Helsinki and Helsinki University Hospital (H.P., M.K.), Helsinki, Finland; CGI (M.L., T.R.), Helsinki, Finland; Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery (M.V., V.E.S., S.W., L.R.), Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland; Department of Neuroradiology (C.S.), Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
  • Heikki Peura
    From the Department of Information Management (A.T.), Helsinki University Hospital, Helsinki, Finland; Department of Neurosurgery, University of Helsinki and Helsinki University Hospital (H.P., M.K.), Helsinki, Finland; CGI (M.L., T.R.), Helsinki, Finland; Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery (M.V., V.E.S., S.W., L.R.), Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland; Department of Neuroradiology (C.S.), Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
  • Mikko Lavinto
    From the Department of Information Management (A.T.), Helsinki University Hospital, Helsinki, Finland; Department of Neurosurgery, University of Helsinki and Helsinki University Hospital (H.P., M.K.), Helsinki, Finland; CGI (M.L., T.R.), Helsinki, Finland; Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery (M.V., V.E.S., S.W., L.R.), Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland; Department of Neuroradiology (C.S.), Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
  • Tomi Ruokola
    From the Department of Information Management (A.T.), Helsinki University Hospital, Helsinki, Finland; Department of Neurosurgery, University of Helsinki and Helsinki University Hospital (H.P., M.K.), Helsinki, Finland; CGI (M.L., T.R.), Helsinki, Finland; Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery (M.V., V.E.S., S.W., L.R.), Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland; Department of Neuroradiology (C.S.), Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
  • Moira Vieli
    Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Frauenklinikstrasse 10, 8091, Zurich, Switzerland.
  • Victor E Staartjes
    Department of Neurosurgery, Bergman Clinics, Naarden, The Netherlands; and.
  • Sebastian Winklhofer
    Department of Neuroradiology, University Hospital Zurich, Zurich, Switzerland.
  • Carlo Serra
    1Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Switzerland.
  • Luca Regli
    Department of Neurosurgery, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
  • Miikka Korja
    Department of Neurosurgery, Helsinki University Hospital and University of Helsinki, Topeliuksenkatu 5, PB 266, 00029 HUS, Helsinki, Finland.

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