New technologies are emerging that can capture slight changes in a patient’s voice, potentially enabling physicians to diagnose cognitive impairment and Alzheimer’s disease before symptoms begin to show.
A study led by a researcher from UT Southwestern Medical Center focused on identifying subtle language and audio changes present in the early stages of Alzheimer’s disease.
The researchers used machine learning and natural language processing (NLP) tools to examine speech patterns in 206 participants, 114 of whom met the criteria for mild cognitive impairment.
The team then cross-referenced these findings with commonly used biomarkers to determine their efficacy in measuring cognitive impairment.
Participants were given cognitive assessments before being asked to record a 1- to 2-minute description of a piece of artwork.
The researchers analyzed these recordings using artificial intelligence to examine elements of speech such as motor control, idea density, and grammatical complexity.
They compared the speech analytics with cerebral spinal fluid samples and MRI scans from the participants to determine how accurately the voice biomarkers detected both mild cognitive impairment and Alzheimer’s disease status and progression.
Significance of Findings
The study found that the novel method of analyzing speech patterns was effective in detecting mild cognitive impairment and identifying patients with signs of Alzheimer’s disease, even when standard cognitive assessments could not easily detect the disease.
Capturing a patient’s voice recording took less than 10 minutes, a significant reduction compared to traditional neuropsychological tests which usually take several hours.
If further studies confirm these findings, the use of artificial intelligence and machine learning to study vocal recordings could provide a straightforward screening tool for primary care providers to use with at-risk individuals.
Earlier diagnoses could provide patients and families with more time to plan for the future and give clinicians greater flexibility in recommending promising lifestyle interventions.
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The study was published in Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring.
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