Early Predictive Accuracy of Machine Learning for Hemorrhagic Transformation in Acute Ischemic Stroke: Systematic Review and Meta-Analysis.

Journal: Journal of medical Internet research
Published Date: May 23, 2025

Abstract

BACKGROUND: Hemorrhagic transformation (HT) is commonly detected in acute ischemic stroke (AIS) and often leads to poor outcomes. Currently, there is no ideal tool for early prediction of HT risk. Recently, machine learning has gained traction in stroke management, prompting the exploration of predictive models for HT. However, systematic evidence on these models is lacking.

Authors

  • Benqiao Wang
    Department of Neurology, First Hospital of China Medical University, Shenyang, China.
  • Bohao Jiang
    Department of Urology, First Hospital of China Medical University, Shenyang, China.
  • Dan Liu
    Department of Bioengineering, Temple University, Philadelphia, PA, United States.
  • Ruixia Zhu
    Department of Neurology, First Hospital of China Medical University, Shenyang, China.

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