Vestibular Neuritis (VN) and Posterior Circulation Stroke (PCS) are the two common causes of the Acute Vestibular Syndrome, which is characterised by sudden, severe and persistent vertigo and/or imbalance. Expert clinicians separate the two diagnoses using information from the history, examination, and laboratory tests. Machine learning models capable of performing expert-level classification can expand the availability of diagnostic expertise.

To develop and evaluate machine learning models for differentiation of vestibular neuritis and posterior circulation stroke using history, examination and vestibular function tests.

We recruited patients who presented to the Emergency Room (ER) with acute vestibular syndrome and received a final diagnosis of VN or PCS. Data from the clinical history, bedside examination and four laboratory tests (videonystagmography, video head impulse test (VHIT), vestibular-evoked myogenic potentials (VEMP) and subjective visual horizontal) were used for model development. Different subsets of data simulated three scenarios: Tier 1 represented an ER with access to neuro-otology expertise (history, neuro-otological examination, videonystagmography, VHIT, ocular VEMP), Tier 2 an ER with VHIT (history, bedside examination, VHIT) and Tier 3 an ER reliant on history and bedside examination only. Model performance was also compared against the HINTS test (head impulse, nystagmus, test-of-skew).

Our dataset consisted of 163 VN and 131 PCS patients. Our best performing models used the CatBoost or XGBoost algorithms and identified PCS with accuracies of 96.6% (95% CI: 93.3-99.9%), 94.6% (95% CI: 90.5-98.6%) and 88.8% (95% CI: 86.0-91.6%) for Tiers 1, 2 and 3. HINTS by experts achieved 94.6% accuracy.

Machine learning models can distinguish between posterior circulation stroke and vestibular neuritis with high accuracy, demonstrating their potential as a diagnostic aid to improve the differential diagnosis of acute vestibular syndrome in the Emergency Room by non-expert physicians.