Machine learning and deep learning algorithms in stroke medicine: a systematic review of hemorrhagic transformation prediction models.

Journal: Journal of neurology
PMID: 39666168

Abstract

BACKGROUND: Acute ischemic stroke (AIS) is a major cause of morbidity and mortality, with hemorrhagic transformation (HT) further worsening outcomes. Traditional scoring systems have limited predictive accuracy for HT in AIS. Recent research has explored machine learning (ML) and deep learning (DL) algorithms for stroke management. This study evaluates and compares the effectiveness of ML and DL algorithms in predicting HT post-AIS, benchmarking them against conventional models.

Authors

  • Mahbod Issaiy
    Advanced Diagnostic and Interventional Radiology Research Center (ADHR), Tehran University of Medical Sciences, Tehran, Iran.
  • Diana Zarei
    School of Medicine, Iran University of Medical Sciences, Tehran, Iran.
  • Shahriar Kolahi
    Department of Radiology, School of Medicine, Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, Iran.
  • David S Liebeskind
    From the Biocomplexity Institute (V.A., R.Z.), Department of Industrial and Systems Engineering (G.T.), and Nutritional Immunology and Molecular Medicine Laboratory, Biocomplexity Institute (R.H., J.B.-R.), Virginia Tech, Blacksburg; Biomedical and Translational Informatics Institute (V.A.) and Department of Neurology (R.Z.), Geisinger Health System, Danville, PA; Department of Neurology, University of Tennessee Health Science Center, Memphis (N.G., G.T., L.E., J.E.M., A.W.A., A.V.A., R.Z.); Second Department of Neurology, "Attikon University Hospital," School of Medicine, University of Athens, Greece (N.H.); and Neurovascular Imaging Research Core and UCLA Stroke Center, University of California, Los Angeles (D.S.L.).

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