国家神经系统疾病临床医学研究中心数据管理与共享系统

Data Management And Sharing System of China National Clinical Research Center for Neurological Diseases

《国家中心数据管理与共享系统操作说明书 —— 研究者部分》

A Deep Learning System to Predict Recurrence and Disability Outcomes in Patients with Transient Ischemic Attack or Ischemic Stroke

第一作者：Jing Jing,Liu Ziyang,Guan Hao ,Zhu Wanlin,Zhang Zhe

通讯作者：Liu Tao,Li Zixiao,Tao Dacheng,Wang Yongjun

AIS TIS AI

基本信息

2023-02-21

已发表文章

发表杂志：Advanced Intelligent Systems

发表类型: Original Contributions

影响因子(IF): 7.298

PMID: https://doi.org/10.1002/aisy.202200240 Pubmed原文链接 全文下载

主要研究背景及目的

Ischemic strokes (IS) and transient ischemic attacks (TIA) account for approximately 80% of all strokes and are leading causes of death worldwide. Assessing the risk of recurrence or functional impairment in IS and TIA patients is essential to both acute phase treatment and secondary prevention.

方法

Current risk prediction systems that rely on clinical parameters alone without leveraging imaging data have only modest performance. Herein, a deep learning-based risk prediction system (RPS) is developed to predict the probability of stroke recurrence or disability (i.e., deep-learning stroke recurrence risk score, SRR score). Then, Kaplan–Meier analysis to evaluate the ability of SRR score to stratify patients at stroke recurrence risk is discussed.

结论

Current risk prediction systems that rely on clinical parameters alone without leveraging imaging data have only modest performance. Herein, a deep learning-based risk prediction system (RPS) is developed to predict the probability of stroke recurrence or disability (i.e., deep-learning stroke recurrence risk score, SRR score). Then, Kaplan–Meier analysis to evaluate the ability of SRR score to stratify patients at stroke recurrence risk is discussed.

主要结果

Current risk prediction systems that rely on clinical parameters alone without leveraging imaging data have only modest performance. Herein, a deep learning-based risk prediction system (RPS) is developed to predict the probability of stroke recurrence or disability (i.e., deep-learning stroke recurrence risk score, SRR score). Then, Kaplan–Meier analysis to evaluate the ability of SRR score to stratify patients at stroke recurrence risk is discussed.