Speech Sound Waves Concept Illustration
Speech Sound Waves Concept Illustration

UT Southwestern Medical Heart researchers discovered that AI-powered voice evaluation may help diagnose Alzheimer’s and cognitive impairment in early phases, doubtlessly offering an environment friendly screening software for major care suppliers if confirmed by bigger research.

O’Donnell Mind Institute researcher says findings could result in a easy screening take a look at for early detection of cognitive impairment.

New applied sciences that may seize refined adjustments in a affected person’s voice could assist physicians diagnose cognitive impairment and

“Our focus was on identifying subtle language and audio changes that are present in the very early stages of Alzheimer’s disease but not easily recognizable by family members or an individual’s primary care physician,” said Ihab Hajjar, M.D., Professor of Neurology at UT Southwestern’s Peter O’Donnell Jr. Brain Institute.

Researchers used advanced

“The recorded descriptions of the picture provided us with an approximation of conversational abilities that we could study via artificial intelligence to determine speech motor control, idea density, grammatical complexity, and other speech features,” Dr. Hajjar said.

The research team compared the participants’ speech analytics to their cerebral spinal fluid samples and MRI scans to determine how accurately the digital voice biomarkers detected both mild cognitive impairment and Alzheimer’s disease status and progression.

“Prior to the development of machine learning and NLP, the detailed study of speech patterns in patients was extremely labor intensive and often not successful because the changes in the early stages are frequently undetectable to the human ear,” Dr. Hajjar said. “This novel method of testing performed well in detecting those with mild cognitive impairment and more specifically in identifying patients with evidence of Alzheimer’s disease – even when it cannot be easily detected using standard cognitive assessments.”

During the study, researchers spent fewer than 10 minutes capturing a patient’s voice recording. Traditional neuropsychological tests typically take several hours to administer.

“If confirmed with larger studies, the use of artificial intelligence and machine learning to study vocal recordings could provide primary care providers with an easy-to-perform screening tool for at-risk individuals,” Dr. Hajjar said. “Earlier diagnoses would give patients and families more time to plan for the future and give clinicians greater flexibility in recommending promising lifestyle interventions.”

Reference: “Development of digital voice biomarkers and associations with cognition, cerebrospinal biomarkers, and neural representation in early Alzheimer’s disease” by Ihab Hajjar MD, MS, Maureen Okafor MD, MPH, Jinho D. Choi PhD, Elliot Moore II PhD, Anees Abrol PhD, Vince D. Calhoun PhD and Felicia C. Goldstein PhD, 5 February 2023, Diagnosis, Assessment & Disease Monitoring.
DOI: 10.1002/dad2.12393

Dr. Hajjar collaborated on this study with a team of researchers at Emory, where he previously served as Director of the Clinical Trial Unit of the Goizueta Alzheimer’s Disease Research Center before joining UTSW in 2022. He is continuing to collect voice recordings in Dallas as part of a follow-up study at UTSW being funded with a

Dr. Hajjar holds the Pogue Family Distinguished University Chair in Alzheimer’s Disease Clinical Research and Care, in Memory of Maurine and David Weigers McMullan.

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