Evidence map›Paper›PMID 41521318›Full record

ArticleNeurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology2026

Automated vertigo diagnosis using video-oculography and artificial intelligence for clinic-based triage: Preliminary results.

Yi-Jing Lai, Ching-Fu Wang, Chun-Chen Yang, Chin-Hsun Huang, Chih-Chung Chen, Ting-Yi Lee, Hsun-Hua Lee, I-Hsien Chen, Tzu-Pu Chang

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In one paragraph

Article in Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology, 2026. 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

9 authors.

Yi-Jing Lai *Neurobit Technologies Co., Ltd., 2F., No. 320, Sec. 4, Zhongxiao E. Rd., Da'an Dist., Taipei, 106, Taiwan.
Ching-Fu Wang *School of Biomedical Engineering, College of Biomedical Engineering, Taipei Medical University, Taipei, Taiwan.
Chun-Chen YangNeurobit Technologies Co., Ltd., 2F., No. 320, Sec. 4, Zhongxiao E. Rd., Da'an Dist., Taipei, 106, Taiwan. travisyang@neurobittech.com.
Chin-Hsun HuangNeurobit Technologies Co., Ltd., 2F., No. 320, Sec. 4, Zhongxiao E. Rd., Da'an Dist., Taipei, 106, Taiwan.
Chih-Chung ChenDepartment of Neurology, Taipei Medical University-Shuang Ho Hospital, New Taipei City, Taiwan.
Ting-Yi LeeDizziness and Balance Disorder Center, Taipei Medical University-Shuang Ho Hospital, New Taipei City, Taiwan.
Hsun-Hua LeeDepartment of Neurology, Taipei Medical University-Shuang Ho Hospital, New Taipei City, Taiwan.
I-Hsien ChenNeurobit Technologies Co., Ltd., 2F., No. 320, Sec. 4, Zhongxiao E. Rd., Da'an Dist., Taipei, 106, Taiwan.
Tzu-Pu ChangDepartment of Neurology/Neuro-Medical Scientific Center, Taichung Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, No. 88, Sec. 1, Fengxing Rd., Tanzi Dist., Taichung, 427, Taiwan. neurochang0617@gmail.com.ORCID http://orcid.org/0000-0001-6143-9023

Funding

Buddhist Tzu Chi Medical Foundation TCMF-A 111-07(112)Neurobit Technologies Co., Ltd A-109-042
6 · The paper itself

Abstract

backgroundDistinguishing dangerous from benign vertigo remains a diagnostic challenge. Our study aimed to develop and evaluate a machine learning model to differentiate between dangerous and benign vertigo in the outpatient setting across two medical institutions that used different equipment, testing methods, and protocols.

methodsPatients in the dizziness clinics of Shuang Ho Hospital (SHH) and Taichung Tzu Chi Hospital (TTCH) were classified into "benign vertigo" and "dangerous vertigo" groups. The data of video-oculography (VOG) and video head impulse test were combined and preprocessed by applying the min-max scaler and the synthetic minority oversampling technique. The optimized Random Forest (RF)-Adaptive Boosting (AdaBoost) model was employed for classification. The SHapley Additive exPlanations method provided feature importance. Model performance was evaluated using a random 70/30 stratified split across sites, and 95% confidence intervals (CIs) were computed using stratified bootstrapping.

resultsA total of 294 patients were enrolled, including 184 (171 with benign vertigo and 13 with dangerous vertigo) in SHH and 110 (63 with benign vertigo and 47 with dangerous vertigo) in TTCH. The optimized RF-AdaBoost model was used to identify dangerous vertigo. The accuracy was 97% (95% CI: 92 - 100%), the sensitivity was 92% (95% CI: 75 - 100%), the specificity was 97% (95% CI: 93 - 100%), and the AUC was 0.95 (95% CI: 0.86 - 1.00).

conclusionOur VOG-based machine learning model for differentiating dangerous from benign vertigo demonstrated good accuracy and showed potential for use across different equipment, testing methods, and institutional protocols.

Indexed as

Artificial IntelligenceDiagnosis, Computer-AssistedTriageVertigoAdultAgedFemaleHead Impulse TestHumansMachine LearningMaleMiddle AgedVideo RecordingMachine learningOptimized random forest with adaptive boostingSynthetic minority oversampling techniqueVertigoVideo-oculography

Identifiers

PMID41521318

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Registered trials

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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.