Evidence map›Paper›PMID 42709223›Full record

ArticleJournal of neurology2026

Separating stroke and acute unilateral vestibulopathy using history, examination and vestibular tests: a machine learning approach.

Chao Wang, Kunal Chaturvedi, Benjamin Nham, Nicole Reid, Andrew P Bradshaw, Sally M Rosengren, Deborah A Black, Kendall J Bein, G Michael Halmagyi, Ali Braytee and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of neurology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Chao WangCentral Clinical School, University of Sydney, Sydney, Australia.ORCID http://orcid.org/0000-0001-6500-5118
Kunal ChaturvediSchool of Computer Science, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, Australia.
Benjamin NhamSt George and Sutherland Clinical School, University of New South Wales, Sydney, Australia.
Nicole ReidInstitute of Clinical Neurosciences, Royal Prince Alfred Hospital, Sydney, Australia.
Andrew P BradshawInstitute of Clinical Neurosciences, Royal Prince Alfred Hospital, Sydney, Australia.
Sally M RosengrenCentral Clinical School, University of Sydney, Sydney, Australia.ORCID http://orcid.org/0000-0002-3830-7214
Deborah A BlackFaculty of Medicine and Health, University of Sydney, Sydney, Australia.ORCID http://orcid.org/0000-0001-9174-1565
Kendall J BeinDepartment of Emergency Medicine, Royal Prince Alfred Hospital, Sydney, Australia.
G Michael HalmagyiCentral Clinical School, University of Sydney, Sydney, Australia.ORCID http://orcid.org/0000-0002-9743-466X
Ali BrayteeSchool of Computer Science, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, Australia.ORCID http://orcid.org/0000-0003-2561-6496
Gnana K BharathySchool of Computer Science, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, Australia.ORCID http://orcid.org/0000-0001-8384-9509
Mukesh PrasadSchool of Computer Science, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, Australia.ORCID http://orcid.org/0000-0002-7745-9667
Miriam S WelgampolaCentral Clinical School, University of Sydney, Sydney, Australia. miriam@icn.usyd.edu.au.ORCID http://orcid.org/0000-0002-4573-4740

Funding

Garnett Passe and Rodney Williams Memorial Foundation 2021_RS_Wang
6 · The paper itself

Abstract

backgroundAcute unilateral vestibulopathy (AUVP) and posterior circulation stroke (PCS) are the two common causes of the acute vestibular syndrome. We developed and evaluated machine learning models to differentiate AUVP and PCS using combinations of patient history, examination, and vestibular tests.

methodsWe recruited 294 patients presenting to the Emergency Room (ER) with acute vestibular syndrome (AUVP: n = 163, PCS: n = 131). Data from history, bedside examination and vestibular tests (video-nystagmography (VNG), video head-impulse test (VHIT), vestibular-evoked myogenic potentials (VEMP) and subjective visual horizontal) were used for machine learning model development. Different subsets of data simulated three scenarios hierarchically reflecting different levels of clinical expertise and resources: Tier 1 represented an ER with neuro-otology support (history, neuro-otological examination, VNG, VHIT, ocular VEMP), Tier 2 an ER with VHIT (history, basic examination, VHIT) and Tier 3 an ER reliant on history and basic examination only. Model performance was also compared against the HINTS test (head impulse, nystagmus, test-of-skew).

resultsOur 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. The most important variables were the bedside head-impulse test, presence of focal neurological symptoms and spontaneous nystagmus slow phase velocity in Tier 1; focal neurological symptoms in Tier 2; and age in Tier 3.

conclusionMachine learning models can accurately separate PCS and AUVP and hold promise as diagnostic aids for frontline clinicians.

Indexed as

Machine LearningStrokeVestibular DiseasesVestibular Function TestsVestibular NeuronitisAdultAgedDiagnosis, DifferentialFemaleHumansMaleMiddle AgedPredictive Learning ModelsAcute unilateral vestibulopathyArtificial intelligenceMachine learningStrokeVestibular neuritis

Identifiers

PMID42709223
PMCPMC13553692

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.