Separation of stroke from vestibular neuritis using the video head impulse test: machine learning models versus expert clinicians.

ABSTRACT:

Background

Acute vestibular syndrome usually represents either vestibular neuritis (VN), an innocuous viral illness, or posterior circulation stroke (PCS), a potentially life-threatening event. The video head impulse test (VHIT) is a quantitative measure of the vestibulo-ocular reflex that can distinguish between these two diagnoses. It can be rapidly performed at the bedside by any trained healthcare professional but requires interpretation by an expert clinician. We developed machine learning models to differentiate between PCS and VN using only the VHIT.

Methods

We trained machine learning classification models using unedited head- and eye-velocity data from acute VHIT performed in an Emergency Room on patients presenting with acute vestibular syndrome and whose final dia

SUBMITTER: Wang C 

PROVIDER: S-EPMC11882619 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature