Deep Learning to Predict Traumatic Brain Injury Outcomes in the Low-Resource Setting.

Journal: World neurosurgery
PMID: 35247613

Abstract

OBJECTIVE: Traumatic brain injury (TBI) disproportionately affects low- and middle-income countries (LMICs). In these settings, accurate patient prognostication is both difficult and essential for high-quality patient care. With the ultimate goal of enhancing TBI triage in LMICs, we aim to develop the first deep learning model to predict outcomes after TBI and compare its performance with that of less complex algorithms.

Authors

  • Syed M Adil
    Division of Global Neurosurgery and Neurology, Duke University Medical Center, Durham, North Carolina, USA.
  • Cyrus Elahi
    Division of Global Neurosurgery and Neurology, Duke University Medical Center, Durham, North Carolina, USA.
  • Dev N Patel
    Division of Global Neurosurgery and Neurology, Duke University Medical Center, Durham, North Carolina, USA; Department of Neurosurgery, New York University Langone Medical Center, New York, New York, USA.
  • Andreas Seas
    Division of Global Neurosurgery and Neurology, Duke University Medical Center, Durham, North Carolina, USA; Department of Biomedical Engineering, Pratt School of Engineering, Duke University, Durham, North Carolina, USA.
  • Pranav I Warman
    Division of Global Neurosurgery and Neurology, Duke University Medical Center, Durham, North Carolina, USA.
  • Anthony T Fuller
    Division of Global Neurosurgery and Neurology, Duke University Medical Center, Durham, North Carolina, USA.
  • Michael M Haglund
    Division of Global Neurosurgery and Neurology, Duke University Medical Center, Durham, North Carolina, USA.
  • Timothy W Dunn
    Division of Global Neurosurgery and Neurology, Duke University Medical Center, Durham, North Carolina, USA.

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