Artificial Intelligence-Enabled End-To-End Detection and Assessment of Alzheimer’s Disease Using Voice

There is currently no simple, widely available screening method for Alzheimer’s disease (AD), partly because the diagnosis of AD is complex and typically involves expensive and sometimes invasive tests not commonly available outside highly specialized clinical settings. Here, we developed an artific...

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Bibliographic Details
Published in:Brain sciences Vol. 13; no. 1; p. 28
Main Authors: Agbavor, Felix, Liang, Hualou
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 23.12.2022
MDPI
Subjects:
Acoustics
Age
Alzheimer's disease
Artificial intelligence
Cognitive ability
data2vec
Datasets
Dementia
Dementia disorders
Diagnosis
end-to-end
Feasibility studies
Gender
Health care
large language models
Machine learning
Neural networks
Neurodegenerative diseases
Speech
speech and language
end-to-end
speech and language
dementia
large language models
Alzheimer’s disease
data2vec
ISSN:2076-3425, 2076-3425
Online Access:Get full text
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Summary:There is currently no simple, widely available screening method for Alzheimer’s disease (AD), partly because the diagnosis of AD is complex and typically involves expensive and sometimes invasive tests not commonly available outside highly specialized clinical settings. Here, we developed an artificial intelligence (AI)-powered end-to-end system to detect AD and predict its severity directly from voice recordings. At the core of our system is the pre-trained data2vec model, the first high-performance self-supervised algorithm that works for speech, vision, and text. Our model was internally evaluated on the ADReSSo (Alzheimer’s Dementia Recognition through Spontaneous Speech only) dataset containing voice recordings of subjects describing the Cookie Theft picture, and externally validated on a test dataset from DementiaBank. The AI model can detect AD with average area under the curve (AUC) of 0.846 and 0.835 on held-out and external test set, respectively. The model was well-calibrated (Hosmer-Lemeshow goodness-of-fit p-value = 0.9616). Moreover, the model can reliably predict the subject’s cognitive testing score solely based on raw voice recordings. Our study demonstrates the feasibility of using the AI-powered end-to-end model for early AD diagnosis and severity prediction directly based on voice, showing its potential for screening Alzheimer’s disease in a community setting.
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ISSN:2076-3425
2076-3425
DOI:10.3390/brainsci13010028