Performance of Automated Algorithm in Large and Medium Vessel Occlusion Detection: A Real-World Experience
The automated algorithm demonstrated high accuracy in identifying anterior LVO with lower performance for pLVO and MeVO. It is crucial for acute stroke teams to be aware of the discordance between automated algorithm results and true rates of LVO and MeVO for timely diagnosis and triage.
Authors
- Sriharsha Voleti (ORCID: https://orcid.org/0000-0002-7030-2907)
- Achala Vagal (ORCID: https://orcid.org/0000-0001-6428-6499)
- Stacie L Demel (ORCID: https://orcid.org/0000-0002-3000-8420)
- Yasmin Tarek Ali Abdel Aziz (ORCID: https://orcid.org/0000-0002-3515-4005)
- Arafat Ali (ORCID: https://orcid.org/0000-0001-6541-8991)
- Aakanksha Sriwastwa
- Bin Zhang (ORCID: https://orcid.org/0000-0003-0607-1806)
- Lily Wang (ORCID: https://orcid.org/0000-0002-5107-438X)
- R. Buse
- Kara Weiss
Institutions
- Cincinnati Children's Hospital Medical Center (US)
- Ann Arbor VA Medical Center (US)
- University of Cincinnati (US)
- University of Pennsylvania (US)
- University of Cincinnati Medical Center (US)
Publication Details
- Journal
- American Journal of Neuroradiology
- Published
- 2024-09-26
- DOI
- https://doi.org/10.3174/ajnr.a8509
- Citations
- 10
- Primary Topic
- Cardiac Valve Diseases and Treatments
- Type
- article
- Field-Weighted Citation Impact
- 4.02