Automatic classification of AD pathology in FTD phenotypes using natural speech.
Alzheimer's disease
automated speech analysis
frontotemporal lobar degeneration
machine learning classification
natural speech
pathology
Journal
Alzheimer's & dementia : the journal of the Alzheimer's Association
ISSN: 1552-5279
Titre abrégé: Alzheimers Dement
Pays: United States
ID NLM: 101231978
Informations de publication
Date de publication:
04 Apr 2024
04 Apr 2024
Historique:
revised:
23
01
2024
received:
27
11
2023
accepted:
24
01
2024
medline:
4
4
2024
pubmed:
4
4
2024
entrez:
4
4
2024
Statut:
aheadofprint
Résumé
Screening 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). We 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. Our classifier showed 0.88 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. 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.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : NIH HHS
ID : P01-AG066597
Pays : United States
Organisme : NIH HHS
ID : K99-AG073510-01
Pays : United States
Organisme : NIH HHS
ID : P30-AG072979
Pays : United States
Organisme : Alzheimer's Association
ID : AARF-21-851126
Pays : United States
Informations de copyright
© 2024 The Authors. Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.
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