Artificial intelligence/machine learning for neuroimaging to predict hemorrhagic transformation: Systematic review/meta-analysis.

Journal: Journal of neuroimaging : official journal of the American Society of Neuroimaging
Published Date: Jul 21, 2024

Abstract

BACKGROUND AND PURPOSE: Early and reliable prediction of hemorrhagic transformation (HT) in patients with acute ischemic stroke (AIS) is crucial for treatment decisions and early intervention. The purpose of this study was to conduct a systematic review and meta-analysis on the performance of artificial intelligence (AI) and machine learning (ML) models that utilize neuroimaging to predict HT.

Authors

  • Richard Dagher
    Division of Neuroradiology, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Medicine, 600 N Wolfe St, Phipps B100F, Baltimore, MD, 21287, USA.
  • Burak Berksu Ozkara
    Department of Neuroradiology, MD Anderson Cancer Center, Houston, TX, USA.
  • Mert Karabacak
    Department of Neurosurgery, Mount Sinai Health System, New York, New York, USA.
  • Samir A Dagher
    Department of Neuroradiology, MD Anderson Cancer Center, Houston, Texas, USA.
  • Elijah Isaac Rumbaut
    School of Medicine, Baylor College of Medicine, Houston, Texas, USA.
  • Licia P Luna
    Division of Neuroradiology, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Medicine, 600 N Wolfe St, Phipps B100F, Baltimore, MD, 21287, USA. lluna6@jhmi.edu.
  • Vivek S Yedavalli
    Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins Hospital, Baltimore, Maryland, USA.
  • Max Wintermark
    Department of Radiology, Stanford University, Stanford, California, USA.

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