Evidence map›Paper›PMID 41238832›Full record

ArticleJournal of neurology2025

The need for speed: using nystagmus velocity profiles and machine learning models to separate canalithiasis BPV from its mimics.

Nicholas Yang, Kunal Chaturvedi, Nicole Reid, Alyssa C Dyball, Emma C Argaet, Andrew P Bradshaw, Chao Wang, Anousha Rafi, Sally M Rosengren, Gabor M Halmagyi and 5 more

Abstract read
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In one paragraph

Article in Journal of neurology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. Review
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

15 authors.

Nicholas YangCentral Clinical School, School of Medicine, University of Sydney, Sydney, Australia.ORCID http://orcid.org/0000-0002-9390-7407
Kunal ChaturvediSchool of Computer Science, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, Australia.ORCID http://orcid.org/0000-0001-9940-1565
Nicole ReidInstitute of Clinical Neurosciences, Royal Prince Alfred Hospital, Sydney, Australia.
Alyssa C DyballCentral Clinical School, School of Medicine, University of Sydney, Sydney, Australia.ORCID http://orcid.org/0009-0005-4651-9187
Emma C ArgaetCentral Clinical School, School of Medicine, University of Sydney, Sydney, Australia.ORCID http://orcid.org/0000-0001-6313-9155
Andrew P BradshawInstitute of Clinical Neurosciences, Royal Prince Alfred Hospital, Sydney, Australia.
Chao WangCentral Clinical School, School of Medicine, University of Sydney, Sydney, Australia.ORCID http://orcid.org/0000-0001-6500-5118
Anousha RafiCentral Clinical School, School of Medicine, University of Sydney, Sydney, Australia.ORCID http://orcid.org/0009-0003-2400-2967
Sally M RosengrenCentral Clinical School, School of Medicine, University of Sydney, Sydney, Australia.ORCID http://orcid.org/0000-0002-3830-7214
Gabor M HalmagyiCentral Clinical School, School of Medicine, University of Sydney, Sydney, Australia.ORCID http://orcid.org/0000-0002-9743-466X
Deborah A BlackFaculty of Medicine and Health, University of Sydney, Sydney, Australia.ORCID http://orcid.org/0000-0001-9174-1565
Gnana BharathySchool of Computer Science, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, Australia.ORCID http://orcid.org/0000-0001-8384-9509
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
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, School of Medicine, 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 2023_RS_Yang
6 · The paper itself

Abstract

purposeSeparating BPV from other positional nystagmus types can be challenging. We examined the utility of nystagmus slow-phase velocity (SPV) profiles when seeking to separate canalithiasis benign positional vertigo (BPV) from its mimics.

methods231 eye-videos canalithiasis BPV were compared against 245 non-BPV positional nystagmus recordings whose diagnoses included: vestibular migraine, posterior fossa tumors, and cerebellar ataxia. A custom-written pupil-tracker generated 2D eye-position traces and plotted SPV as a function of time. Nystagmus onset latency, 50% rise time, peak velocity, peak latency, time to 50%, and 95% decay of SPV (T50 and T95) were recorded. Machine learning (ML) models were developed to separate BPV from non-BPV positional nystagmus.

resultsMedian nystagmus onset, 50% rise time, peak velocity, and peak latency were 0.789 s, 0.488 s, 36.530°/s, and 2.487 s for BPV and 0.321 s, 0 s, 9.306°/s, and 4.418 s for non-BPV groups. BPV demonstrated a paroxysmal profile with T50 and T95 of 3.062 s and 10.616 s; the non-BPV group had a T50 of 19.02 s and did not decay to 95% by 1000 s. All six metrics demonstrated significant differences in the dominant plane. Among the ML models tested, CatBoost achieved the highest performance with an accuracy, sensitivity, and specificity of 93.3, 92.7, and 93.9%. Statistical methods separated these two groups with an accuracy, a sensitivity, and a specificity of 87.4, 94.8, and 80.4%.

conclusionMachine learning models, when applied to SPV profiles, exceeded statistical methods and may prove useful when developing video-nystagmography-based diagnostic aids for non-experts.

Indexed as

Benign Paroxysmal Positional VertigoMachine LearningNystagmus, PathologicNystagmus, PhysiologicAdultAgedCerebellar AtaxiaDiagnosis, DifferentialFemaleHumansMaleMiddle AgedMigraine DisordersYoung AdultBenign paroxysmal positional vertigoCentral positional vertigoMachine learningMechanical rotation chairVertigoVideo nystagmography

Identifiers

PMID41238832

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

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.