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New AI System Spots Brain Decline On-The-Go

Jason Phillippe by Jason Phillippe
March 23, 2025
in Disease
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New AI System Spots Brain Decline On-The-Go

Photo by National Cancer Institute on Unsplash

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This is a game changer.

Researchers have developed a portable AI-powered system that can detect early signs of brain decline, offering a potential tool for identifying individuals at risk of dementia or Alzheimer’s disease. The test analyzes various aspects of motor function, observing how seniors perform tasks such as standing still, walking, and getting up from a bench.

By processing data from these movements, the AI system was able to accurately identify 83% of participants diagnosed with mild cognitive impairment (MCI), a condition that involves memory or thinking problems greater than typical aging but not yet full dementia. MCI can be an early warning sign of Alzheimer’s or other forms of dementia.

The system works by assessing subtle differences in motor skills, particularly balance and walking, which are closely linked to cognitive function. Trent Guess, senior researcher and associate professor at the University of Missouri, explained that impairments in motor function often reflect cognitive decline, but these issues can be too subtle for visual observation to detect.

For the test, researchers used a camera and a force board that measures pressure and balance. A small group of seniors with MCI performed tasks like standing, walking, and standing up while counting backwards. The results from this group were compared to those of healthy seniors, and the AI system was able to distinguish most participants with MCI. The technology can detect even minor issues, like slower walking or difficulty standing up, which could indicate cognitive strain.

The researchers aim to expand the use of this portable system in various environments, such as clinics, senior centers, and assisted living facilities, to screen for brain decline. It could also be applied in other areas, including concussion assessments, sports rehabilitation, and monitoring conditions like Parkinson’s disease, ALS, and recovery from knee or hip replacements. The technology’s versatility could make it a valuable tool in improving early diagnosis and intervention across multiple fields.

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