Predicting infarct outcomes after extended time window thrombectomy in large vessel occlusion using knowledge guided deep learning
In this study, integrating medical knowledge into deep learning models enhanced the accuracy of infarct predictions in AIS patients undergoing extended time window MT.
Authors
- Yunfei Zha (ORCID: https://orcid.org/0000-0002-8714-7472)
- Houwang Zhang (ORCID: https://orcid.org/0000-0002-2358-0613)
- Lei Yuan (ORCID: https://orcid.org/0000-0001-9069-9481)
- Y S Li (ORCID: https://orcid.org/0000-0003-2417-0867)
- Lisong Dai (ORCID: https://orcid.org/0000-0003-3608-9732)
- Zhaohui Li (ORCID: https://orcid.org/0000-0002-4132-658X)
- Zheng Sun (ORCID: https://orcid.org/0000-0003-0028-9441)
- Jingxuan Jiang
Institutions
- City University of Hong Kong (HK)
- Shanghai Jiao Tong University (CN)
- Nantong University (CN)
- Nanjing University of Information Science and Technology (CN)
- Wuhan University (CN)
- Affiliated Hospital of Nantong University (CN)
- Renmin Hospital of Wuhan University (CN)
- Shanghai Sixth People's Hospital (CN)
- Wuhan Hankou Hospital (CN)
Publication Details
- Journal
- Journal of NeuroInterventional Surgery
- Published
- 2025-06-06
- DOI
- https://doi.org/10.1136/jnis-2025-023355
- Primary Topic
- Acute Ischemic Stroke Management
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Key Research and Development Program of China