Automatic classification of AD pathology in FTD phenotypes using natural speech.
Sunghye ChoChristopher A OlmSharon AshSanjana ShellikeriGalit AgmonKatheryn A Q CousinsDavid J IrwinMurray GrossmanMark LibermanNaomi NevlerPublished in: Alzheimer's & dementia : the journal of the Alzheimer's Association (2024)
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.
Keyphrases
- machine learning
- artificial intelligence
- end stage renal disease
- deep learning
- newly diagnosed
- ejection fraction
- chronic kidney disease
- cerebrospinal fluid
- big data
- peritoneal dialysis
- prognostic factors
- patient reported outcomes
- cognitive decline
- body composition
- mild cognitive impairment
- high intensity
- patient reported