An interpretable machine learning model for stroke recurrence in patients with symptomatic intracranial atherosclerotic arterial stenosis.

Journal: Frontiers in neuroscience
Published Date: Jan 8, 2024

Abstract

BACKGROUND AND OBJECTIVE: Symptomatic intracranial atherosclerotic stenosis (SICAS) is the most common etiology of ischemic stroke and one of the main causes of high stroke recurrence. The recurrence of stroke is closely related to the prognosis of ischemic stroke. This study aims to develop a machine learning model based on high-resolution vessel wall imaging (HR-VWI) to predict the risk of stroke recurrence in SICAS.

Authors

  • Yu Gao
    Department of Radiology Center, The First Affiliated Hospital of Xinxiang Medical University, Xin Xiang, China.
  • Zi-Ang Li
    Department of Radiology Center, The First Affiliated Hospital of Xinxiang Medical University, Xin Xiang, China.
  • Xiao-Yang Zhai
    Department of Radiology Center, The First Affiliated Hospital of Xinxiang Medical University, Xin Xiang, China.
  • Lin Han
    Department of Radiology Center, The First Affiliated Hospital of Xinxiang Medical University, Xin Xiang, China.
  • Ping Zhang
    Department of Computer Science and Engineering, The Ohio State University, USA.
  • Si-Jia Cheng
    Department of Radiology Center, The First Affiliated Hospital of Xinxiang Medical University, Xin Xiang, China.
  • Jun-Yan Yue
    Department of Radiology Center, The First Affiliated Hospital of Xinxiang Medical University, Xin Xiang, China.
  • Hong-Kai Cui
    Department of Neurointerventional Center, The First Affiliated Hospital of Xinxiang Medical University, Xin Xiang, China.

Keywords

No keywords available for this article.

External Resources

Popular Topics
Recent Journals