ArticleFrontiers in aging neuroscience2024
Screening for early Alzheimer's disease: enhancing diagnosis with linguistic features and biomarkers.
Article in Frontiers in aging neuroscience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Advancing Alzheimer Disease Prediction With Large Language Model-Based Linguistic Feature Analysis: Development and Validation Study.JMIR medical informatics · 2026Article
- Combining Linguistic, Behavioral and Visuospatial Measures to Characterize Multidomain Impairment in Dementia.Brain sciences · 2026Article
- AI-enabled digital phenotyping for Alzheimer's disease: a review of multimodal sensor integration and symptom trajectories.Alzheimer's research & therapy · 2026Review
- Assessment of Language Impairments Towards Identifying Markers for Early Diagnosis of Pathological Cognitive Decline.Behavioral sciences (Basel, Switzerland) · 2026Review
- Listening for Alzheimer's clues: machine learning analysis of multidomain speech features for cognitive impairment screening.Frontiers in aging neuroscience · 2026Article
- WATCH-SS: Developing a Trustworthy and Explainable Modular Framework for Detecting Cognitive Impairment from Spontaneous Speech.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2026Article
- Bridging Language Markers and Pathology: Correlations Between Digital Speech Measures and Surrogate CSF Biomarkers in Alzheimer's Disease.bioRxiv : the preprint server for biology · 2025Article
- WATCH-SS: Developing a Trustworthy and Explainable Modular Framework for Detecting Cognitive Impairment from Spontaneous Speech.medRxiv : the preprint server for health sciences · 2025Article
- Barriers to Using Mobile App-Based Cognitive Testing in Older Adults with Probable Alzheimer's Disease: A Qualitative Study.Brain sciences · 2025Article
- Precision neuropsychology in the area of AI.Frontiers in psychology · 2025Article
- Detecting CSF-validated Alzheimer's disease from spontaneous speech in German: an interpretable end-to-end machine-learning framework.Frontiers in neurologyArticle
- Bridging language markers and pathology: correlations between digital speech measures and surrogate CSF biomarkers in Alzheimer's disease.Alzheimer's & dementia (Amsterdam, Netherlands)Article
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9 authors.
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Abstract
Introduction: Research has shown that speech analysis demonstrates sensitivity in detecting early Alzheimer's disease (AD), but the relation between linguistic features and cognitive tests or biomarkers remains unclear. This study aimed to investigate how linguistic features help identify cognitive impairments in patients in the early stages of AD. Method: This study analyzed connected speech from 80 participants and categorized the participants into early-AD and normal control (NC) groups. The participants underwent amyloid- Results: We found notable linguistic differences between the early-AD and NC groups in lexical diversity, syntactic complexity, and language disfluency. Using machine learning classifiers (SVM, KNN, and RF), we achieved up to 88% accuracy in distinguishing early-AD patients from normal controls, with mean length of utterance (MLU) and long pauses ratio (LPR) serving as core linguistic indicators. Moreover, the integration of linguistic indicators with biomarkers significantly improved predictive accuracy for AD. Regression analysis also highlighted crucial linguistic features, such as MLU, LPR, Type-to-Token ratio (TTR), and passive construction ratio (PCR), which were sensitive to changes in cognitive function. Conclusion: Findings support the efficacy of linguistic analysis as a screening tool for the early detection of AD and the assessment of subtle cognitive decline. Integrating linguistic features with biomarkers significantly improved diagnostic accuracy.
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