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

Wang, C; Sreerama, J; Nham, B; Reid, N; Ozalp, N; Thomas, JO; Cappelen-Smith, C; Calic, Z; Bradshaw, AP; Rosengren, SM; Akdal, G; Halmagyi, GM; Black, DA; Burke, D; Prasad, M; Bharathy, GK; Welgampola, MS

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

Wang, C Sreerama, J Nham, B Reid, N Ozalp, N Thomas, JO Cappelen-Smith, C Calic, Z Bradshaw, AP Rosengren, SM Akdal, G Halmagyi, GM Black, DA Burke, D Prasad, M Bharathy, GK Welgampola, MS

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Publisher:: SPRINGER HEIDELBERG
Publication Type:: Journal Article
Citation:: J Neurol, 2025, 272, (3), pp. 248
Issue Date:: 2025-03-05

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Field	Value	Language
dc.contributor.author	Wang, C
dc.contributor.author	Sreerama, J
dc.contributor.author	Nham, B
dc.contributor.author	Reid, N
dc.contributor.author	Ozalp, N
dc.contributor.author	Thomas, JO
dc.contributor.author	Cappelen-Smith, C
dc.contributor.author	Calic, Z
dc.contributor.author	Bradshaw, AP
dc.contributor.author	Rosengren, SM
dc.contributor.author	Akdal, G
dc.contributor.author	Halmagyi, GM
dc.contributor.author	Black, DA
dc.contributor.author	Burke, D
dc.contributor.author	Prasad, M
dc.contributor.author	Bharathy, GK
dc.contributor.author	Welgampola, MS
dc.date.accessioned	2025-07-15T05:50:28Z
dc.date.available	2025-01-12
dc.date.available	2025-07-15T05:50:28Z
dc.date.issued	2025-03-05
dc.identifier.citation	J Neurol, 2025, 272, (3), pp. 248
dc.identifier.issn	0340-5354
dc.identifier.issn	1432-1459
dc.identifier.uri	http://hdl.handle.net/10453/188293
dc.description.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 diagnosis was VN or PCS. The models were validated using an independent test dataset collected at a second institution. We compared the performance of the models against expert clinicians as well as a widely used VHIT metric: the gain cutoff value. RESULTS: The training and test datasets comprised 252 and 49 patients, respectively. In the test dataset, the best machine learning model identified VN with 87.8% (95% CI 77.6%-95.9%) accuracy. Model performance was not significantly different (p = 0.56) from that of blinded expert clinicians who achieved 85.7% accuracy (75.5%-93.9%) and was superior (p = 0.01) to that of the optimal gain cutoff value (75.5% accuracy (63.8%-85.7%)). CONCLUSION: 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.
dc.format	Electronic
dc.language	eng
dc.publisher	SPRINGER HEIDELBERG
dc.relation.ispartof	J Neurol
dc.relation.isbasedon	10.1007/s00415-025-12918-3
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	1103 Clinical Sciences, 1109 Neurosciences
dc.subject.classification	Neurology & Neurosurgery
dc.subject.classification	3202 Clinical sciences
dc.subject.classification	3209 Neurosciences
dc.subject.mesh	Humans
dc.subject.mesh	Vestibular Neuronitis
dc.subject.mesh	Machine Learning
dc.subject.mesh	Head Impulse Test
dc.subject.mesh	Female
dc.subject.mesh	Male
dc.subject.mesh	Stroke
dc.subject.mesh	Middle Aged
dc.subject.mesh	Aged
dc.subject.mesh	Adult
dc.subject.mesh	Diagnosis, Differential
dc.subject.mesh	Video Recording
dc.subject.mesh	Humans
dc.subject.mesh	Vestibular Neuronitis
dc.subject.mesh	Diagnosis, Differential
dc.subject.mesh	Video Recording
dc.subject.mesh	Adult
dc.subject.mesh	Aged
dc.subject.mesh	Middle Aged
dc.subject.mesh	Female
dc.subject.mesh	Male
dc.subject.mesh	Stroke
dc.subject.mesh	Head Impulse Test
dc.subject.mesh	Machine Learning
dc.subject.mesh	Humans
dc.subject.mesh	Vestibular Neuronitis
dc.subject.mesh	Machine Learning
dc.subject.mesh	Head Impulse Test
dc.subject.mesh	Female
dc.subject.mesh	Male
dc.subject.mesh	Stroke
dc.subject.mesh	Middle Aged
dc.subject.mesh	Aged
dc.subject.mesh	Adult
dc.subject.mesh	Diagnosis, Differential
dc.subject.mesh	Video Recording
dc.title	Separation of stroke from vestibular neuritis using the video head impulse test: machine learning models versus expert clinicians.
dc.type	Journal Article
utslib.citation.volume	272
utslib.location.activity	Germany
utslib.for	1103 Clinical Sciences
utslib.for	1109 Neurosciences
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/The Trustworthy Digital Society
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2025-07-15T05:50:26Z
pubs.issue	3
pubs.publication-status	Published online
pubs.volume	272
utslib.citation.issue	3

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 diagnosis was VN or PCS. The models were validated using an independent test dataset collected at a second institution. We compared the performance of the models against expert clinicians as well as a widely used VHIT metric: the gain cutoff value. RESULTS: The training and test datasets comprised 252 and 49 patients, respectively. In the test dataset, the best machine learning model identified VN with 87.8% (95% CI 77.6%-95.9%) accuracy. Model performance was not significantly different (p = 0.56) from that of blinded expert clinicians who achieved 85.7% accuracy (75.5%-93.9%) and was superior (p = 0.01) to that of the optimal gain cutoff value (75.5% accuracy (63.8%-85.7%)). CONCLUSION: 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.

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http://hdl.handle.net/10453/188293