Enhancing Outcome Prediction in Intracerebral Hemorrhage Through Deep Learning: A Retrospective Multicenter Study.

Journal: Academic radiology
PMID:

Abstract

RATIONALE AND OBJECTIVES: This study aimed to employ deep learning techniques to analyze and validate an automatic prognostic biomarker for predicting outcomes following intracerebral hemorrhage (ICH).

Authors

  • Dan Wang
    Guangdong Pharmaceutical University Guangzhou Guangdong China.
  • Jing Zhang
    MOEMIL Laboratory, School of Optoelectronic Information, University of Electronic Science and Technology of China, Chengdu, China.
  • Hao Dong
  • Chencui Huang
    Department of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co., Ltd., Beijing, China.
  • Qiaoying Zhang
    Department of Radiology, Xi'an Central Hospital, Xi An 710000, China.
  • Yaqiong Ma
    Department of Radiology, Gansu Provincial Hospital, Lanzhou 730030, China.
  • Hui Zhao
    School of Mathematics and Computer Science, Shaanxi University of Technology, Hanzhong, 723000, Shaanxi, China.
  • Shenglin Li
    College of Artificial Intelligence, Southwest University, Chongqing 400715, China.
  • Juan Deng
    Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, China; Second clinical school, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China. Electronic address: 2377052591@qq.com.
  • Qiang Dong
    Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China.
  • Jinhong Xiao
    Department of Neurosurgery, The Fifth Affiliated Hospital of Zunyi Medical University, Zhuhai 519100, China.
  • Junlin Zhou
    Department of Radiology, Lanzhou University Second Hospital, 730030 Lanzhou, Gansu, China.
  • Xiaoyu Huang
    Key Laboratory of Organofluorine Chemistry and Laboratory of Polymer Materials, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 200032, China. Electronic address: xyhuang@sioc.ac.cn.