ArticleNPJ Parkinson's disease2025
Digital phenotyping of Parkinson's disease via natural language processing.
Article in NPJ Parkinson's disease, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Extending standardized speech analysis across languages: An Italian toolkit for transcription and coding of neurological speech.Behavior research methods · 2026Article
- Speech and language outcome measures in clinical trials of Alzheimer's and Parkinson's diseases.Expert review of neurotherapeutics · 2026Review
- Artificial intelligence techniques for classification of Alzheimer's disease using neuroimaging data: a review.Frontiers in artificial intelligence · 2026Review
- Pauses as a Quantitative Measure of Linguistic Planning Challenges in Parkinson's Disease.Brain sciences · 2025Article
- Dynamics of language grounding: on the time course, durability, adaptability, and vulnerability of embodied effects.Frontiers in psychology · 2025Review
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Authors and funding
12 authors.
Funding
Abstract
Frontostriatal degeneration in Parkinson's disease (PD) is associated with language deficits, which can be identified using natural language processing, a remarkable tool for digital-phenotyping. Current evidence is mostly blind to the disorder's cognitive phenotypes. We validated an AI-driven approach to capture digital language markers of PD with and without mild cognitive impairment (PD-MCI, PD-nMCI) relative to healthy controls (HCs). Analyzing the connected speech of participants, we extracted linguistic features with CLAN software. Classification was performed using SVM and RFE. Discrimination between PD and HCs reached an AUC of 77%, with even better results for subgroup analyses (AUC: 85% PD-nMCI vs. HCs; 83% PD-MCI vs. HCs; 75% PD-nMCI vs. PD-MCI). Key linguistic features included retracing, action verb, utterance error, and verbless-utterance ratios. Despite the small sample size, which may limit statistical power and generalizability, this study highlights the foundational potential of linguistic digital markers for early diagnosis and phenotyping of PD.
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