ArticleJournal of neurology2020
Modern machine-learning can support diagnostic differentiation of central and peripheral acute vestibular disorders.
Article in Journal of neurology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
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The trial behind it
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Who cites it
28 citing papers in PubMed, 1 synthesis or guideline pooled it, 50 citations in OpenAlex.
- The Epidemiology and Clinical Presentation of the Acute Imbalance Syndrome (AIS)-A Systematic Review and Meta-Analysis.European journal of neurology · 2026Pooled it
- Separating stroke and acute unilateral vestibulopathy using history, examination and vestibular tests: a machine learning approach.Journal of neurology · 2026Article
- A digital neurotology history: Characteristics and inter-rater reliability.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026Article
- Comprehensive Review of Nystagmus and Vertigo Diagnostics: From Pathological Foundations to AI-Driven Telemedicine.Sensors (Basel, Switzerland) · 2026Review
- Automated classification of benign paroxysmal positional vertigo from video-nystagmography using a delay-aware neural network.Scientific reports · 2026Article
- Diagnostic challenges in vestibular migraine-clinical differentiation from Menière's disease and discrepancies with current classification criteria.Journal of neurology · 2025Article
- Clinical decision support for vestibular diagnosis: large-scale machine learning with lived experience coaching.NPJ digital medicine · 2025Article
- Multidimensional Feature Analysis of Meniere's Disease and Vestibular Migraine: Insights from Machine Learning and Vestibular Testing.Journal of the Association for Research in Otolaryngology : JARO · 2025Article
- Bibliometric analysis of global research trends in vestibular neuritis (1980-2024).European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2025Article
- Machine Learning Analysis of Physical Activity Data to Classify Postural Dysfunction.The Laryngoscope · 2023Article
- Neurological update: neuro-otology 2023.Journal of neurology · 2023Article
- Multifractal detrended fluctuation analysis of insole pressure sensor data to diagnose vestibular system disorders.Biomedical engineering letters · 2023Article
- Deep Learning Algorithms for Screening and Diagnosis of Systemic Diseases Based on Ophthalmic Manifestations: A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- Application of machine learning in the diagnosis of vestibular disease.Scientific reports · 2022Article
- Current concepts in acute vestibular syndrome and video-oculography.Current opinion in neurology · 2022Review
- Artificial intelligence for early stroke diagnosis in acute vestibular syndrome.Frontiers in neurology · 2022Article
- Videooculography "HINTS" in Acute Vestibular Syndrome: A Prospective Study.Frontiers in neurology · 2022Article
- Machine Learning Techniques for Differential Diagnosis of Vertigo and Dizziness: A Review.Sensors (Basel, Switzerland) · 2021Review
- A Set of Eight Key Questions Helps to Classify Common Vestibular Disorders-Results From the DizzyReg Patient Registry.Frontiers in neurology · 2021Article
- Editorial: Epidemiology and Genetics of Vestibular Disorders.Frontiers in neurology · 2021Article
Corrections and comments
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Authors and funding
11 authors at 4 institutions in 2 countries.
Funding
Abstract
backgroundDiagnostic classification of central vs. peripheral etiologies in acute vestibular disorders remains a challenge in the emergency setting. Novel machine-learning methods may help to support diagnostic decisions. In the current study, we tested the performance of standard and machine-learning approaches in the classification of consecutive patients with acute central or peripheral vestibular disorders.
methods40 Patients with vestibular stroke (19 with and 21 without acute vestibular syndrome (AVS), defined by the presence of spontaneous nystagmus) and 68 patients with peripheral AVS due to vestibular neuritis were recruited in the emergency department, in the context of the prospective EMVERT trial (EMergency VERTigo). All patients received a standardized neuro-otological examination including videooculography and posturography in the acute symptomatic stage and an MRI within 7 days after symptom onset. Diagnostic performance of state-of-the-art scores, such as HINTS (Head Impulse, gaze-evoked Nystagmus, Test of Skew) and ABCD
resultsMachine-learning methods (e.g., MultiGMC) outperform univariate scores, such as HINTS or ABCD
conclusionsEstablished clinical scores (such as HINTS) provide a valuable baseline assessment for stroke detection in acute vestibular syndromes. In addition, machine-learning methods may have the potential to increase sensitivity and selectivity in the establishment of a correct diagnosis.
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Registered trials
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.