ArticleJournal of neurology2019
Towards computerized diagnosis of neurological stance disorders: data mining and machine learning of posturography and sway.
Article in Journal of neurology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.
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Who cites it
19 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine learning and wearable sensors for automated Parkinson's disease diagnosis aid: a systematic review.Journal of neurology · 2024Pooled it
- Machine Learning for the Diagnosis of Parkinson's Disease: A Review of Literature.Frontiers in aging neuroscience · 2021Pooled it
- Acute vertigo and dizziness: the diagnostic potential of static posturography in the neurological emergency department.Journal of neurology · 2026Article
- A deep learning pipeline for detecting vestibular schwannoma patients with unilateral vestibular loss based on kinematic data.Scientific reports · 2025Article
- Multifactorial predictors of falls in older adults: a decade of data from the National Health and Aging Trends Study.BMC geriatrics · 2025Article
- Differential diagnosis of orthostatic dizziness with persistent postural-perceptual dizziness and its underlying mechanisms.Frontiers in neurology · 2025Review
- Impact of proprioceptive cervical dizziness in chronic neck pain syndromes on gait and stance during active head-turn challenges.Journal of neurology · 2024Article
- Vestibular perceptual learning improves self-motion perception, posture, and gait in older adults.Communications biology · 2024Article
- Three Hertz postural leg tremor impairs posture maintenance in multiple system atrophy-cerebellar type.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2024Article
- A Computerized Analysis with Machine Learning Techniques for the Diagnosis of Parkinson's Disease: Past Studies and Future Perspectives.Diagnostics (Basel, Switzerland) · 2022Review
- Predictive value of neutrophil-to-lymphocyte ratio for the fatality of COVID-19 patients complicated with cardiovascular diseases and/or risk factors.Scientific reports · 2022Article
- Article
- Incongruity of Geometric and Spectral Markers in the Assessment of Body Sway.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
- Applications of Multivariate Statistical and Data Mining Analyses to the Search for Biomarkers of Sensorineural Hearing Loss, Tinnitus, and Vestibular Dysfunction.Frontiers in neurology · 2021Article
- Modern machine-learning can support diagnostic differentiation of central and peripheral acute vestibular disorders.Journal of neurology · 2020Article
- Quantitative assessment of postural instability in spinocerebellar ataxia type 3 patients.Annals of clinical and translational neurology · 2020Article
- 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
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Authors and funding
7 authors.
Funding
Abstract
We perform classification, ranking and mapping of body sway parameters from static posturography data of patients using recent machine-learning and data-mining techniques. Body sway is measured in 293 individuals with the clinical diagnoses of acute unilateral vestibulopathy (AVS, n = 49), distal sensory polyneuropathy (PNP, n = 12), anterior lobe cerebellar atrophy (CA, n = 48), downbeat nystagmus syndrome (DN, n = 16), primary orthostatic tremor (OT, n = 25), Parkinson's disease (PD, n = 27), phobic postural vertigo (PPV n = 59) and healthy controls (HC, n = 57). We classify disorders and rank sway features using supervised machine learning. We compute a continuous, human-interpretable 2D map of stance disorders using t-stochastic neighborhood embedding (t-SNE). Classification of eight diagnoses yielded 82.7% accuracy [95% CI (80.9%, 84.5%)]. Five (CA, PPV, AVS, HC, OT) were classified with a mean sensitivity and specificity of 88.4% and 97.1%, while three (PD, PNP, and DN) achieved a mean sensitivity of 53.7%. The most discriminative stance condition was ranked as "standing on foam-rubber, eyes closed". Mapping of sway path features into 2D space revealed clear clusters among CA, PPV, AVS, HC and OT subjects. We confirm previous claims that machine learning can aid in classification of clinical sway patterns measured with static posturography. Given a standardized, long-term acquisition of quantitative patient databases, modern machine learning and data analysis techniques help in visualizing, understanding and utilizing high-dimensional sensor data from clinical routine.
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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.