ArticleAlzheimer's & dementia : the journal of the Alzheimer's Association2024
Automatic classification of AD pathology in FTD phenotypes using natural speech.
Article in Alzheimer's & dementia : the journal of the Alzheimer's Association, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Multilevel white matter degeneration associated cognitive dysfunction in mild cognitive impairment.Molecular psychiatry · 2026Article
- A Machine Learning Framework for Cognitive Impairment Screening from Speech with Multimodal Large Models.Bioengineering (Basel, Switzerland) · 2026Article
- Multi-level language deficits in behavioural frontotemporal degeneration and related disorders.Brain communications · 2026Article
- Predicting amyloid status in Primary Progressive Aphasia using explainable artificial intelligence.NPJ dementia · 2026Article
- Written picture descriptions distinguish variants of primary progressive aphasia.Journal of Alzheimer's disease : JAD · 2025Article
- Picture Description and Functional Communication Rating Correlates in Variants of Primary Progressive Aphasia.Aphasiology · 2025Article
- Primary Progressive Aphasias: Diagnosis and Treatment.Brain sciences · 2025Review
- Automatic classification of AD pathology in FTD phenotypes using natural speech.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Article
- The collection of speech data for the assessment of cognition remotely: Balancing ethical and practical challenges.Alzheimer's & dementia (Amsterdam, Netherlands)Review
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Authors and funding
10 authors.
Funding
Abstract
introductionScreening for Alzheimer's disease neuropathologic change (ADNC) in individuals with atypical presentations is challenging but essential for clinical management. We trained automatic speech-based classifiers to distinguish frontotemporal dementia (FTD) patients with ADNC from those with frontotemporal lobar degeneration (FTLD).
methodsWe trained automatic classifiers with 99 speech features from 1 minute speech samples of 179 participants (ADNC = 36, FTLD = 60, healthy controls [HC] = 89). Patients' pathology was assigned based on autopsy or cerebrospinal fluid analytes. Structural network-based magnetic resonance imaging analyses identified anatomical correlates of distinct speech features.
resultsOur classifier showed 0.88 DISCUSSION: Brief naturalistic speech samples can be used for screening FTD patients for underlying ADNC in vivo. This work supports the future development of digital assessment tools for FTD. HIGHLIGHTS: We trained machine learning classifiers for frontotemporal dementia patients using natural speech. We grouped participants by neuropathological diagnosis (autopsy) or cerebrospinal fluid biomarkers. Classifiers well distinguished underlying pathology (Alzheimer's disease vs. frontotemporal lobar degeneration) in patients. We identified important features through an explainable artificial intelligence approach. This work lays the groundwork for a speech-based neuropathology screening tool.
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