ArticlePloS one2022
Detection and differentiation of ataxic and hypokinetic dysarthria in cerebellar ataxia and parkinsonian disorders via wave splitting and integrating neural networks.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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9 citing papers in PubMed, 1 synthesis or guideline pooled it, 19 citations in OpenAlex.
- Applied Machine Learning Techniques to Diagnose Voice-Affecting Conditions and Disorders: Systematic Literature Review.Journal of medical Internet research · 2023Pooled it
- Multimodal Digital Phenotyping of Behavior in a Neurology Clinic: Development of the Neurobooth Platform and the First Two Years of Data Collection.medRxiv : the preprint server for health sciences · 2025Article
- Clinical Decision Support Using Speech Signal Analysis: Systematic Scoping Review of Neurological Disorders.Journal of medical Internet research · 2025Article
- Sensitive Quantification of Cerebellar Speech Abnormalities Using Deep Learning Models.IEEE access : practical innovations, open solutions · 2024Article
- Atypical vocal quality in women with the FMR1 premutation: an indicator of impaired sensorimotor control.Experimental brain research · 2023Article
- A New Neurological Screening Approach for Diagnosing Brainstem Infarction Using the Calling Method and Familiar Voices.Medicina (Kaunas, Lithuania) · 2023Article
- Automatic stridor detection using small training set via patch-wise few-shot learning for diagnosis of multiple system atrophy.Scientific reports · 2023Article
- Oromotor Nonverbal Performance and Speech Motor Control: Theory and Review of Empirical Evidence.Brain sciences · 2023Review
- Ataxic speech disorders and Parkinson's disease diagnostics via stochastic embedding of empirical mode decomposition.PloS one · 2023Article
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14 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Dysarthria may present during the natural course of many degenerative neurological conditions. Hypokinetic and ataxic dysarthria are common in movement disorders and represent the underlying neuropathology. We developed an artificial intelligence (AI) model to distinguish ataxic dysarthria and hypokinetic dysarthria from normal speech and differentiate ataxic and hypokinetic speech in parkinsonian diseases and cerebellar ataxia. We screened 804 perceptual speech analyses performed in the Samsung Medical Center Neurology Department between January 2017 and December 2020. The data of patients diagnosed with parkinsonian disorders or cerebellar ataxia were included. Two speech tasks (numbering from 1 to 50 and reading nine sentences) were analyzed. We adopted convolutional neural networks and developed a patch-wise wave splitting and integrating AI system for audio classification (PWSI-AI-AC) to differentiate between ataxic and hypokinetic speech. Of the 395 speech recordings for the reading task, 76, 112, and 207 were from normal, ataxic dysarthria, and hypokinetic dysarthria subjects, respectively. Of the 409 recordings of the numbering task, 82, 111, and 216 were from normal, ataxic dysarthria, and hypokinetic dysarthria subjects, respectively. The reading and numbering task recordings were classified with 5-fold cross-validation using PWSI-AI-AC as follows: hypokinetic dysarthria vs. others (area under the curve: 0.92 ± 0.01 and 0.92 ± 0.02), ataxia vs. others (0.93 ± 0.04 and 0.89 ± 0.02), hypokinetic dysarthria vs. ataxia (0.96 ± 0.02 and 0.95 ± 0.01), hypokinetic dysarthria vs. none (0.86 ± 0.03 and 0.87 ± 0.05), and ataxia vs. none (0.87 ± 0.07 and 0.87 ± 0.09), respectively. PWSI-AI-AC showed reliable performance in differentiating ataxic and hypokinetic dysarthria and effectively augmented data to classify the types even with limited training samples. The proposed fully automatic AI system outperforms neurology residents. Our model can provide effective guidelines for screening related diseases and differential diagnosis of neurodegenerative diseases.
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