New AI tool may help detect early signs of dementia
A novel speech analysis tool that uses artificial intelligence successfully detected mild cognitive impairment and dementia in a Spanish-speaking population, according to research led by UT Southwestern Medical Center.
The study, published in Frontiers in Neurology, provides preliminary support for the algorithm as an early screening tool that may help identify patients at risk of developing dementia.
Data for the study was collected from 195 Spanish speakers recruited as part of a multicenter clinical trial in Spain. All participants completed an initial evaluation and were categorized as either having normal cognition, mild cognitive impairment (MCI), or dementia. Data from 21 participants was excluded due to incomplete cognitive or demographic data, or poor audio transcription quality.
The final cohort of 174 participants had a mean age of 74; there were slightly more females (56%) than males. Participants were divided into a training group of 122 participants (70%) and a test group of 52 participants (30%).
Researchers used four language tasks to train independent machine learning (ML) models using data from the training group participants. Neuropsychological performance and audio recording variables were collected from each participant using the AcceXible platform – a proprietary web-based instrument developed for disease detection through speech analysis.
The final model of the speech analysis algorithm was then used for the test group and was able to differentiate cognitively normal participants from those with dementia or MCI with an overall accuracy of 88.4% and 87.5%, respectively. The final model outperformed one of the current standard-of-care screening measures known as the Mini-Mental State Examination (MMSE).
Findings from this study and similar work with English speakers by UTSW researcher Ihab Hajjar, M.D., Professor of Neurology and Internal Medicine and in the O’Donnell Brain Institute, suggest that these tools may improve quality of life for patients at risk for dementia through early detection – an issue that most significantly affects marginalized racial and ethnic groups who often experience delayed diagnosis. Further research is needed to validate the accuracy of the model before the technology can be deployed for clinical use.
