ArticleJournal of neurology2020
Development and validation of a classification algorithm to diagnose and differentiate spontaneous episodic vertigo syndromes: results from the DizzyReg patient registry.
Article in Journal of neurology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- [The SCD diagnostic strategy, procedure and pathway for episodic vestibular syndrome].Lin chuang er bi yan hou tou jing wai ke za zhi = Journal of clinical otorhinolaryngology head and neck surgery · 2026Review
- 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
- [Artificial intelligence applications in Ménière's disease].Lin chuang er bi yan hou tou jing wai ke za zhi = Journal of clinical otorhinolaryngology head and neck surgery · 2025Review
- Machine learning models help differentiate between causes of recurrent spontaneous vertigo.Journal of neurology · 2024Article
- Neurological update: neuro-otology 2023.Journal of neurology · 2023Article
- Development and Validation of the Predictive Model for the Differentiation between Vestibular Migraine and Meniere's Disease.Journal of clinical medicine · 2022Article
- Artificial intelligence for early stroke diagnosis in acute vestibular syndrome.Frontiers in neurology · 2022Article
- Machine Learning Techniques for Differential Diagnosis of Vertigo and Dizziness: A Review.Sensors (Basel, Switzerland) · 2021Review
- Using Base-ml to Learn Classification of Common Vestibular Disorders on DizzyReg Registry Data.Frontiers in neurology · 2021Article
- A Set of Eight Key Questions Helps to Classify Common Vestibular Disorders-Results From the DizzyReg Patient Registry.Frontiers in neurology · 2021Article
- Machine Learning-Based Dizziness/Vertigo Disease Diagnosis by Combining Medical History and Signs.Journal of otolaryngology - head & neck surgery = Le Journal d'oto-rhino-laryngologie et de chirurgie cervico-facialeArticle
Corrections and comments
- Erratum issued
Authors and funding
4 authors.
Funding
Abstract
backgroundSpontaneous episodic vertigo syndromes, namely vestibular migraine (VM) and Menière's disease (MD), are difficult to differentiate, even for an experienced clinician. In the presence of complex diagnostic information, automated systems can support human decision making. Recent developments in machine learning might facilitate bedside diagnosis of VM and MD.
methodsData of this study originate from the prospective patient registry of the German Centre for Vertigo and Balance Disorders, a specialized tertiary treatment center at the University Hospital Munich. The classification task was to differentiate cases of VM, MD from other vestibular disease entities. Deep Neural Networks (DNN) and Boosted Decision Trees (BDT) were used for classification.
resultsA total of 1357 patients were included (mean age 52.9, SD 15.9, 54.7% female), 9.9% with MD and 15.6% with VM. DNN models yielded an accuracy of 98.4 ± 0.5%, a precision of 96.3 ± 3.9%, and a sensitivity of 85.4 ± 3.9% for VM, and an accuracy of 98.0 ± 1.0%, a precision of 90.4 ± 6.2% and a sensitivity of 89.9 ± 4.6% for MD. BDT yielded an accuracy of 84.5 ± 0.5%, precision of 51.8 ± 6.1%, sensitivity of 16.9 ± 1.7% for VM, and an accuracy of 93.3 ± 0.7%, precision 76.0 ± 6.7%, sensitivity 41.7 ± 2.9% for MD.
conclusionThe correct diagnosis of spontaneous episodic vestibular syndromes is challenging in clinical practice. Modern machine learning methods might be the basis for developing systems that assist practitioners and clinicians in their daily treatment decisions.
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