Machine learning in action: Revolutionizing intracranial hematoma detection and patient transport decision-making.

Journal: Journal of neurosciences in rural practice
Published Date: Dec 16, 2023

Abstract

OBJECTIVES: Traumatic intracranial hematomas represent a critical clinical situation where early detection and management are of utmost importance. Machine learning has been recently used in the detection of neuroradiological findings. Hence, it can be used in the detection of intracranial hematomas and furtherly initiate a management cascade of patient transfer, diagnostics, admission, and emergency intervention. We aim, here, to develop a diagnostic tool based on artificial intelligence to detect hematomas instantaneously, and automatically start a cascade of actions that support the management protocol depending on the early diagnosis.

Authors

  • Ehab El Refaee
    Department of Neurosurgery, Faculty of Medicine, Cairo University, Cairo, Egypt.
  • Taher M Ali
    Department of Neurosurgery, Faculty of Medicine, Cairo University, Cairo, Egypt.
  • Ahmed Al Menabbawy
    Department of Neurosurgery, Faculty of Medicine, Cairo University, Cairo, Egypt.
  • Mahmoud Elfiky
    Department of Surgery, Cairo University, Kasr Al Ainy Faculty of Medicine, Cairo, Egypt.
  • Ahmed El Fiki
    Department of Neurosurgery, Faculty of Medicine, Cairo University, Cairo, Egypt.
  • Shady Mashhour
    Department of Radiology, Cairo University, Cairo, Egypt.
  • Ahmed Harouni
    Department of Medical Engineering, NVIDIA, Santa Clara, California, United States.

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