Automatic classification of AD pathology in FTD phenotypes using natural speech

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.PMID:38572850 | DOI:10.1002/alz.13748

Source: The Journal of Alzheimers Association - April 4, 2024 Category: Psychiatry Authors: Sunghye Cho Christopher A Olm Sharon Ash Sanjana Shellikeri Galit Agmon Katheryn A Q Cousins David J Irwin Murray Grossman Mark Liberman Naomi Nevler Source Type: research