Automated machine learning for predicting perioperative ischemia stroke in endovascularly treated ruptured intracranial aneurysm patients
We successfully developed and validated an interpretable machine learning framework, integrated with a clinical decision-support system, for predicting postprocedural PIS in endovascularly treated RIAs patients. This tool effectively predicted the likelihood of PIS, enabling high-risk patients to promptly take specific preventive and therapeutic measures.
Authors
- Ning Huang (ORCID: https://orcid.org/0000-0001-8874-3439)
- Weifu Chen
- Ying Ma
- Yuhang Peng
- Jianhe Yue
- Yongxiang Jiang (ORCID: https://orcid.org/0000-0001-9770-5733)
- Xiaolin Zhang (ORCID: https://orcid.org/0000-0003-3099-3974)
- Xiang Ji
- Yuan Cheng (ORCID: https://orcid.org/0000-0002-6434-8175)
- Ke Bi
Institutions
- Dalian Medical University (CN)
- Second Affiliated Hospital of Chongqing Medical University (CN)
- Chongqing Medical University (CN)
Publication Details
- Journal
- Frontiers in Neurology
- Published
- 2025-06-19
- DOI
- https://doi.org/10.3389/fneur.2025.1599856
- Primary Topic
- Intracranial Aneurysms: Treatment and Complications
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Chongqing Medical University