A weakly supervised deep learning model integrating noncontrasted computed tomography images and clinical factors facilitates haemorrhagic transformation prediction after intravenous thrombolysis in acute ischaemic stroke patients.

Journal: Biomedical engineering online
PMID: 38115029

Abstract

BACKGROUND: Haemorrhage transformation (HT) is a serious complication of intravenous thrombolysis (IVT) in acute ischaemic stroke (AIS). Accurate and timely prediction of the risk of HT before IVT may change the treatment decision and improve clinical prognosis. We aimed to develop a deep learning method for predicting HT after IVT for AIS using noncontrast computed tomography (NCCT) images.

Authors

  • Xiaoshuang Ru
    Central Hospital, Dalian, Liaoning Province, China.
  • Shilong Zhao
    Department of Radiology, Affliated ZhongShan Hospital of Dalian University, No. 6 Jiefang Rd, Zhongshan District, Dalian, 116001, Liaoning Province, China.
  • Weidao Chen
    Beijing Infervision Technology Co. Ltd., Beijing, China.
  • Jiangfen Wu
    Department of Biomedical Engineering, College of Automation, Nanjing University of Aeronautics and Astronautics, No. 29, Yudao St., Qinhuai District, Nanjing, 210016, Jiangsu Province, China, wjfyunzhu@163.com.
  • Ruize Yu
    Infervision Medical Technology Co., Ltd., Ocean International Center, Beijing, China.
  • Dawei Wang
    Institute of Advanced Research, Infervision Medical Technology Co., Ltd, Beijing, China.
  • Mengxing Dong
    Department of Applied Clinical Medicine, Infervision, Beijing, China.
  • Qiong Wu
    Key Laboratory of Flexible Electronics (KLOFE) and Institute of Advanced Materials (IAM), Nanjing Tech University (NanjingTech), 30 South Puzhu Road, Nanjing 211816, P. R. China.
  • Daoyong Peng
    Department of Neurology, Central Hospital of Dalian University of Technology, No. 826 Xinan Rd, Shahekou District, Dalian, 116033, Liaoning Province, China.
  • Yang Song
    Biomedical and Multimedia Information Technology (BMIT) Research Group, School of IT, University of Sydney, NSW 2006, Australia. Electronic address: yson1723@uni.sydney.edu.au.

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