A Deep Learning Approach to Predict Recanalization First-Pass Effect following Mechanical Thrombectomy in Patients with Acute Ischemic Stroke.

Journal: AJNR. American journal of neuroradiology
Published Date: Aug 9, 2024

Abstract

BACKGROUND AND PURPOSE: Following endovascular thrombectomy in patients with large-vessel occlusion stroke, successful recanalization from 1 attempt, known as the first-pass effect, has correlated favorably with long-term outcomes. Pretreatment imaging may contain information that can be used to predict the first-pass effect. Recently, applications of machine learning models have shown promising results in predicting recanalization outcomes, albeit requiring manual segmentation. In this study, we sought to construct completely automated methods using deep learning to predict the first-pass effect from pretreatment CT and MR imaging.

Authors

  • Haoyue Zhang
    Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, USA.
  • Jennifer S Polson
    Computational Diagnostics Lab, University of California, Los Angeles, CA 90024, USA; Department of Bioengineering, University of California, Los Angeles, CA 90024, USA.
  • Zichen Wang
    Department of Pharmacology and Systems Therapeutics, Department of Genetics and Genomic Sciences, BD2K-LINCS Data Coordination and Integration Center (DCIC), Mount Sinai's Knowledge Management Center for Illuminating the Druggable Genome (KMC-IDG), Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Kambiz Nael
    Department of Radiology, University of California, Los Angeles, Los Angeles, CA, USA.
  • Neal M Rao
    Department of Neurology (N.M.R.), University of California, Los Angeles, California.
  • William F Speier
    Computational Diagnostics Lab, University of California, Los Angeles, Los Angeles, California, USA.
  • Corey W Arnold
    Department of Bioengineering; University of California, Los Angeles, CA.

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