Deep learning–based clinical decision support system for intracerebral hemorrhage: an imaging-based AI-driven framework for automated hematoma segmentation and trajectory planning
This AI-driven system demonstrated robust efficacy for supratentorial ICH, addressing 60% of prevalent hemorrhage subtypes. While limitations remain in infratentorial hematomas, this novel automated hematoma segmentation and surgical planning system could be helpful in assisting less-experienced neurosurgeons with limited resources in primary healthcare settings.
Authors
- Fangye Li (ORCID: https://orcid.org/0000-0001-6880-8311)
- Xiaolei Chen (ORCID: https://orcid.org/0000-0001-8573-1475)
- Jiashu Zhang (ORCID: https://orcid.org/0000-0003-0997-121X)
- Zhichao Gan (ORCID: https://orcid.org/0009-0005-6342-5393)
- Xinghua Xu (ORCID: https://orcid.org/0000-0002-3146-8832)
- Ron Kikinis
Institutions
- Harvard University (US)
- Chinese PLA General Hospital (CN)
Publication Details
- Journal
- Neurosurgical FOCUS
- Published
- 2025-07-01
- DOI
- https://doi.org/10.3171/2025.5.focus25246
- Citations
- 2
- Primary Topic
- Intracerebral and Subarachnoid Hemorrhage Research
- Type
- article
- Field-Weighted Citation Impact
- 2.78