An interpretable artificial intelligence model based on CT for prognosis of intracerebral hemorrhage: a multicenter study.

Journal: BMC medical imaging
Published Date: Jul 9, 2024

Abstract

OBJECTIVES: To develop and validate a novel interpretable artificial intelligence (AI) model that integrates radiomic features, deep learning features, and imaging features at multiple semantic levels to predict the prognosis of intracerebral hemorrhage (ICH) patients at 6 months post-onset.

Authors

  • Hao Zhang
    College of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou, 450002, China.
  • Yun-Feng Yang
    Laboratory for Medical Imaging Informatics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, 200083, China.
  • Xue-Lin Song
    Department of Radiology, The Second Affiliated Hospital of Dalian Medical University, Dalian, 116027, Liaoning, China.
  • Hai-Jian Hu
    Department of Hemato-oncology, The First Hospital of Changsha, Changsha, 410005, Hunan, China.
  • Yuan-Yuan Yang
    Department of Electrocardiography, Anhui Maternal and Child Health Hospital, Hefei, China. Electronic address: ahmuyyy@163.com.
  • Xia Zhu
    Institute of Clinical Pharmacology, School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510060, China.
  • Chao Yang
    Translational Institute for Cancer Pain, Chongming Hospital Affiliated to Shanghai University of Health & Medicine Sciences (Xinhua Hospital Chongming Branch), Shanghai 202155, P. R. China.

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