Deep Learning Using One-stop-shop CT Scan to Predict Hemorrhagic Transformation in Stroke Patients Undergoing Reperfusion Therapy: A Multicenter Study.

Journal: Academic radiology
PMID: 39462736

Abstract

RATIONALE AND OBJECTIVES: Hemorrhagic transformation (HT) is one of the most serious complications in patients with acute ischemic stroke (AIS) following reperfusion therapy. The purpose of this study is to develop and validate deep learning (DL) models utilizing multiphase computed tomography angiography (CTA) and computed tomography perfusion (CTP) images for the fully automated prediction of HT.

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.
  • Jiayang Liu
    Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China.
  • Shaoguo Cui
    College of Computer Science and Engineering, Chongqing University of Technology, Chongqing 400054, China.
  • Meilin Gong
    Department of Radiology, Chongqing General Hospital, Chongqing 400013, China (M.G.).
  • Yongmei Li
    Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.

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