Deep learning of noncontrast CT for fast prediction of hemorrhagic transformation of acute ischemic stroke: a multicenter study.

Journal: European radiology experimental
PMID: 39812734

Abstract

BACKGROUND: Hemorrhagic transformation (HT) is a complication of reperfusion therapy following acute ischemic stroke (AIS). We aimed to develop and validate a model for predicting HT and its subtypes with poor prognosis-parenchymal hemorrhage (PH), including PH-1 (hematoma within infarcted tissue, occupying < 30%) and PH-2 (hematoma occupying ≥ 30% of the infarcted tissue)-in AIS patients following intravenous thrombolysis (IVT) based on noncontrast computed tomography (NCCT) and clinical data.

Authors

  • Huanhuan Ren
    Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
  • Haojie Song
    College of Computer and Information Science, Chongqing Normal University, Chongqing, China.
  • Shaoguo Cui
    College of Computer Science and Engineering, Chongqing University of Technology, Chongqing 400054, China.
  • Hua Xiong
    Department of Radiology, Shapingba Hospital affiliated to Chongqing University (Shapingba District People's Hospital of Chongqing), Chongqing, China.
  • Bangyuan Long
    Department of Radiology, Chongqing General Hospital, Chongqing, China.
  • Yongmei Li
    Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.

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