Machine learning models based on location-radiomics enable the accurate prediction of early neurological function deterioration for acute stroke in elderly patients
We constructed comprehensive maps of strategic lesion network localizations for predicting END in elderly stroke patients and developed a predictive ML model that incorporates both location and radiomics features. This model could facilitate the rapid and robust prediction of the risk of END, enabling timely interventions and personalized treatment strategies to improve patient outcomes.
Authors
- Xiao‐Ming Fu (ORCID: https://orcid.org/0000-0001-8479-0107)
- Chuanyang Zhang
- Mingyang Peng
- Wenli Zhu (ORCID: https://orcid.org/0000-0003-2535-0464)
- Hui Xu (ORCID: https://orcid.org/0000-0003-2970-3984)
- Hongjie Huang (ORCID: https://orcid.org/0000-0001-5300-4652)
- Xueping Li (ORCID: https://orcid.org/0000-0002-1985-9291)
- Miaomiao Li (ORCID: https://orcid.org/0000-0001-7678-687X)
- Zhijun Gao (ORCID: https://orcid.org/0000-0002-6251-7326)
- Changcheng Li (ORCID: https://orcid.org/0000-0001-8861-6771)
Institutions
- Gaochun People's Hospital (CN)
- Nanjing Medical University (CN)
Publication Details
- Journal
- Frontiers in Aging Neuroscience
- Published
- 2025-04-23
- DOI
- https://doi.org/10.3389/fnagi.2025.1582687
- Citations
- 1
- Primary Topic
- Acute Ischemic Stroke Management
- Type
- article
- Field-Weighted Citation Impact
- 1.35
Funders
- Jiangsu University