Automated detection of large vessel occlusion using deep learning: a pivotal multicenter study and reader performance study
The AI software demonstrated a high detection rate for LVO. In addition, the software improved diagnostic accuracy of early-career physicians in detecting LVO, streamlining stroke workflow in the emergency room.
Authors
- Wi‐Sun Ryu (ORCID: https://orcid.org/0000-0002-2823-5253)
- Myung-Jae Lee (ORCID: https://orcid.org/0000-0003-0475-1437)
- Jae Guk Kim (ORCID: https://orcid.org/0000-0002-2446-4497)
- Hotak Hong
- Leonard Sunwoo (ORCID: https://orcid.org/0000-0003-0374-8658)
- You‐Ri Kang
- Sue Young Ha (ORCID: https://orcid.org/0000-0003-2214-1106)
- Dongmin Kim (ORCID: https://orcid.org/0000-0001-6931-7653)
- Joon‐Tae Kim (ORCID: https://orcid.org/0000-0003-4028-8339)
Institutions
- Chonnam National University (KR)
- Seoul National University Bundang Hospital (KR)
- Chonnam National University Hospital (KR)
- Daejeon Eulji Medical Center, Eulji University (KR)
- Artificial Intelligence in Medicine (Canada) (CA)
Publication Details
- Journal
- Journal of NeuroInterventional Surgery
- Published
- 2024-09-20
- DOI
- https://doi.org/10.1136/jnis-2024-022254
- Citations
- 7
- Primary Topic
- Acute Ischemic Stroke Management
- Type
- article
- Field-Weighted Citation Impact
- 2.71
Funders
- Korea Medical Device Development Fund