New AI-powered test provides early detection for Alzheimer’s and dementia

Early detection of Alzheimer's disease and related dementias (ADRD) is crucial for slowing progression and improving outcomes. Researchers are exploring innovative ways to identify mild cognitive impairment (MCI), a potential precursor to dementia, before symptoms become severe. Recent advances in artificial intelligence and motor function assessment offer a promising new approach.

Diagnosing MCI has traditionally been challenging, especially in rural areas with limited access to neuropsychologists. Recognizing this barrier, a research team at the University of Missouri developed a low-cost, portable system designed to measure subtle motor function differences that could indicate early cognitive decline.

The system integrates a depth camera, a force plate, and an interface board to capture precise movement data. Older adults, including those with MCI, participated in a study where they completed three activities: standing still, walking, and transitioning from sitting to standing. To add complexity, participants performed these tasks while counting backward in intervals of seven.

The recorded data was analyzed using machine learning, a form of artificial intelligence, which successfully identified 83% of participants with MCI. Among various AI models tested, decision trees provided the best results, achieving a sensitivity of 0.83 and a specificity of 1.00.

“The areas of the brain involved in cognitive impairment overlap with those controlling motor function, so when one is diminished, the other is impacted as well,” said Trent Guess, an associate professor involved in the study. “Our device can detect very subtle changes in balance and walking that would otherwise go unnoticed.”

Alzheimer’s disease affects millions of people, and the Centers for Disease Control and Prevention estimates that cases in the U.S. could more than double by 2060. Despite its prevalence, only 8% of individuals with MCI receive a formal diagnosis. This gap delays early intervention, reducing the effectiveness of treatments and risk reduction strategies.

“Alzheimer’s is a significant problem in the U.S.,” said Jamie Hall, an associate professor in the study. “We know that if we can identify people early, we can intervene sooner to halt or slow disease progression.”

Current diagnostic tools, such as PET scans and cerebrospinal fluid tests, are costly and require specialized facilities, making them impractical for large-scale screening. The need for an accessible, population-wide detection method has driven researchers toward AI-powered motor function assessments.

Beyond physical motor assessments, AI is also being applied to home-based cognitive and speech analysis tools. The TAS Test, a 20-minute online platform, is a prime example of this innovation. Developed in Australia, it evaluates hand movements, speech, and cognitive performance using AI, requiring only a standard keyboard and webcam.

Studies have shown that the earliest stages of Alzheimer’s can be detected through subtle changes in motor skills and speech patterns. AI can analyze these fine details with remarkable accuracy, even in non-ideal conditions like low lighting or cluttered backgrounds. Algorithms used in TAS Test have been validated against wearable sensors and motion-tracking technology, proving their reliability.

The ability to combine multiple data sources—motor function, cognitive assessments, and speech patterns—enhances accuracy and helps researchers develop more effective predictive models. Multimodal AI screening offers a scalable, cost-effective alternative to traditional tests, allowing for widespread early detection.

The University of Missouri team aims to make their portable motor function system available in public health settings, such as senior centers, physical therapy clinics, and assisted living facilities. Their device could also help diagnose conditions beyond dementia, including fall risk, frailty, concussions, and neurodegenerative diseases like Parkinson’s.

“There are new drugs coming out to treat those with MCI, but you need a diagnosis to qualify for the medications,” Hall said. “Our system can detect if a person walks slower, takes smaller steps, or shows balance issues. These are subtle signs of cognitive strain that might otherwise go unnoticed.”

Future research will refine the system’s capabilities, expanding its applications to broader medical and rehabilitative fields.

“This technology can be beneficial in many ways, from sports rehabilitation to knee and hip replacements,” Guess said. “Movement is fundamental to who we are, and our goal is to create tools that improve people’s quality of life.”

The impact of this research extends beyond the lab. Many study participants have personal connections to Alzheimer’s, either as patients or caregivers. Their involvement underscores the urgency of developing accessible, early detection tools.

“Many of those who volunteered for our study have loved ones with Alzheimer’s and want to help push this research forward,” Hall said. “It highlights why this work is so important.”

As AI-powered assessments continue to evolve, they have the potential to transform dementia screening, making early diagnosis more accessible and improving the lives of millions.

Note: Materials provided above by The Brighter Side of News. Content may be edited for style and length.

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