Can machine learning be a reliable tool for predicting hematoma progression following traumatic brain injury? A systematic review and meta-analysis.

Journal: Neuroradiology
Published Date: May 21, 2025

Abstract

BACKGROUND: Predicting hematoma progression in traumatic brain injury (TBI) is crucial for timely interventions and effective clinical management, as unchecked hematoma growth can lead to rapid neurological deterioration, increased intracranial pressure, and poor patient outcomes. Accurate risk assessment enables proactive therapeutic strategies, minimizing secondary brain damage and improving survival rates.

Authors

  • Ibrahim Mohammadzadeh
    Skull Base Research Center, Loghman-Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran. Electronic address: Ibrahim.mdz7777@gmail.com.
  • Bardia Hajikarimloo
    Skull Base Research Center, Loghman-Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Pooya Eini
    Toxicological Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Behnaz Niroomand
    Skull Base Research Center, Loghman-Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Shahin Mohammadzadeh
    Skull Base Research Center, Loghman-Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Mohammad Amin Habibi
    Department of Neurosurgery, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran.
  • Zohre Masoumi Shahr-E Babak
    School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Islamic Republic of Iran.
  • Abbas Aliaghaei
    Department of Biology and Anatomical Sciences, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Islamic Republic of Iran.

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