Separation of stroke from vestibular neuritis using the video head impulse test: machine learning models versus expert clinicians
Machine learning models can effectively differentiate PCS from VN using only VHIT data, with comparable accuracy to expert clinicians. They hold promise as a tool to assist Emergency Room clinicians evaluating patients with acute vestibular syndrome.
Authors
- Mukesh Prasad (ORCID: https://orcid.org/0000-0002-7745-9667)
- Gülden Akdal (ORCID: https://orcid.org/0000-0002-2486-4490)
- Deborah Black (ORCID: https://orcid.org/0000-0001-9174-1565)
- Zeljka Calic (ORCID: https://orcid.org/0000-0003-0635-016X)
- Benjamin Nham
- Gnana Bharathy (ORCID: https://orcid.org/0000-0001-8384-9509)
- Miriam S. Welgampola (ORCID: https://orcid.org/0000-0002-4573-4740)
- Cecilia Cappelen‐Smith (ORCID: https://orcid.org/0000-0001-9511-4227)
- Nese Ozalp
- G. Michael Hálmagyi (ORCID: https://orcid.org/0000-0002-9743-466X)
- James Thomas (ORCID: https://orcid.org/0000-0001-8933-9948)
- Sally M. Rosengren (ORCID: https://orcid.org/0000-0002-3830-7214)
- David Burke (ORCID: https://orcid.org/0000-0002-4622-5008)
- Chao Wang (ORCID: https://orcid.org/0000-0001-6500-5118)
- Andrew P. Bradshaw
- Nicole Reid
- Jeevan Sreerama
Institutions
- University of Technology Sydney (AU)
- The University of Sydney (AU)
- Neurosciences Institute (US)
- Dokuz Eylül University (TR)
- Sutherland Hospital (AU)
- Royal Prince Alfred Hospital (AU)
- Liverpool Hospital (AU)
- UNSW Sydney (AU)
- South Western Sydney Local Health District (AU)
Publication Details
- Journal
- Journal of Neurology
- Published
- 2025-03-01
- DOI
- https://doi.org/10.1007/s00415-025-12918-3
- Citations
- 1
- Primary Topic
- Vestibular and auditory disorders
- Type
- article
- Field-Weighted Citation Impact
- 0.84
Funders
- University of Sydney
- Garnett Passe and Rodney Williams Memorial Foundation