Researchers at UT Southwestern Medical Center have discovered that AI-powered voice analysis could facilitate the early diagnosis of Alzheimer’s disease and cognitive impairments, presenting a potentially efficient screening tool for primary care providers, pending validation through larger studies. Dr. Ihab Hajjar, who led the research, emphasized the importance of detecting subtle language and audio changes associated with Alzheimer’s that might be overlooked by caregivers and healthcare providers.
The study involved advanced machine learning and natural language processing techniques to analyze speech patterns in 206 participants—114 with mild cognitive decline and 92 without impairment. Participants described artwork for one to two minutes, allowing the team to evaluate various speech features. The researchers then compared speech analytics with cerebrospinal fluid samples and MRI scans to assess the accuracy of these “digital voice biomarkers.”
The results indicated that this innovative approach detected mild cognitive impairment and Alzheimer’s more effectively than conventional tests, which usually take hours to complete. If confirmed with larger research cohorts, this method could streamline screenings for at-risk individuals, enabling earlier diagnoses and greater planning opportunities for patients and clinicians alike. The study was supported by several grants, including from the National Institutes of Health.
