Target-based deep learning network surveillance of non-contrast computed tomography for small infarct core of acute ischemic stroke
The deep learning model was able to detect small infarct core on CT images more accurately. It provided the infarct portion and extent, which is valuable in assessing the severity of disease and guiding treatment procedures.
Authors
- Hang Qu (ORCID: https://orcid.org/0000-0003-4263-0505)
- Lu Lu (ORCID: https://orcid.org/0000-0003-2240-9547)
- Hui Nee Tang (ORCID: https://orcid.org/0000-0001-8448-6623)
- Yongxin Li (ORCID: https://orcid.org/0000-0002-0914-6386)
- Yu‐Chen Chen (ORCID: https://orcid.org/0000-0002-8539-7224)
- Yi Zhao (ORCID: https://orcid.org/0000-0003-4593-8353)
- Qiqi Ban (ORCID: https://orcid.org/0009-0006-3775-3219)
- Dongyang Gao
- Wei J. Wang (ORCID: https://orcid.org/0000-0002-4848-7394)
Institutions
- Nanjing University of Science and Technology (CN)
- Georgia Southern University (US)
- Chinese Institute for Brain Research (CN)
- Northern Jiangsu People's Hospital (CN)
- Yangzhou University (CN)
- Nanjing Medical University (CN)
Publication Details
- Journal
- Frontiers in Neurology
- Published
- 2024-09-19
- DOI
- https://doi.org/10.3389/fneur.2024.1477811
- Citations
- 1
- Primary Topic
- Acute Ischemic Stroke Management
- Type
- article
- Field-Weighted Citation Impact
- 0.39