Data Management And Sharing System of China National Clinical Research Center for Neurological Diseases
A Deep Learning System to Predict Recurrence and Disability Outcomes in Patients with Transient Ischemic Attack or Ischemic Stroke
First author：Jing Jing,Liu Ziyang,Guan Hao ,Zhu Wanlin,Zhang Zhe
Corresponding author：Liu Tao,Li Zixiao,Tao Dacheng,Wang Yongjun
essential_information
- Person in charge：Jing Jing,Liu Ziyang,Guan Hao ,Zhu Wanlin,Zhang Zhe
- Data Sources： CNSR-Ⅲ
- Number of papers：CNSR-Ⅲ20230041
- Published time：2023-02-21
2023-02-21
Published articles
Journal：Advanced Intelligent Systems
Publication type: Original Contributions
IF(IF): 7.298
PMID: https://doi.org/10.1002/aisy.202200240 Link To Pubmed Full text Download
Background And Purpose
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.
Method
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.
Conclusion
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.
Main results
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.