Neurologists-level interpretable CT-based deep neural network for prediction of hemorrhagic transformation after ischemic stroke
By combining plain CT scans with deep learning methodologies, we developed a clinically applicable model with demonstrable interpretability. Primarily designed to predict HT after acute ischemic stroke, this model demonstrated significant performance advantages in testing compared to both clinical physicians and similar existing models.
Authors
- Yihui Tu (ORCID: https://orcid.org/0000-0003-4838-5117)
- Yanrui Jin (ORCID: https://orcid.org/0000-0002-2691-9643)
- Xinyu Zhao (ORCID: https://orcid.org/0000-0002-5128-4424)
- Z. Li (ORCID: https://orcid.org/0000-0002-5620-1229)
- Xu Han
- Mengxing Wang
- Guanyi Zhang
- Qian Zhang
Institutions
- Shanghai University (CN)
- Academy of Medical Sciences (GB)
- Shanghai Jiao Tong University (CN)
- Capital Medical University (CN)
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Beijing Tian Tan Hospital (CN)
- National Clinical Research Center for Digestive Diseases (CN)
- The People's Hospital of Guangxi Zhuang Autonomous Region (CN)
- Academia Sinica (TW)
Publication Details
- Journal
- Frontiers in Neuroscience
- Published
- 2026-01-14
- DOI
- https://doi.org/10.3389/fnins.2025.1753071
- Primary Topic
- Acute Ischemic Stroke Management
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Science and Technology Major Project
- National Health Commission of the People's Republic of China