Study Explores Artificial Intelligence as a Tool to Identify Cognitive Decline - Video

Mild cognitive impairment (MCI) can be an early indicator of Alzheimer's disease or dementia, so identifying those with cognitive issues early could lead to interventions and better outcomes. But diagnosing MCI can be a long and difficult process, especially in rural areas where access to licensed neuropsychologists is limited.

To increase accessibility to cognitive assessments, a team of researchers at the University of Missouri created a portable system to efficiently measure multiple aspects of motor function. The device is simple and affordable, combining a depth camera, a force plate and an interface board.

For the study, the team examined older adults, some of whom had MCI, and asked them to complete three activities: standing still, walking and standing up from a bench. Participants had to complete these activities while counting backward in intervals of seven at the same time. Based off their performance, which was captured by the new portable system, the data was fed into a machine learning model — a type of artificial intelligence — that accurately identified 83% of those in the study with MCI.

“The areas of the brain involved in cognitive impairment overlap with areas of the brain involved in motor function, so when one is diminished, the other is impacted as well,” said Trent Guess, an associate professor in the College of Health Sciences. “These can be very subtle differences in motor function related to balance and walking that our new device can detect but would go unnoticed through observation.”

Alzheimer’s disease is a significant problem here in the U.S. We know that if we can identify people early, we can provide early intervention to halt or slow the progression of the disease,” said Jamie Hall, an associate teaching professor in the College of Health Sciences.

New drugs are coming out to treat those with MCI, but you need a diagnosis of MCI to qualify for the medications,” Hall said. “Our portable system can detect if a person walks slower or doesn’t take as big of a step because they are thinking very hard. Some people have more sway and are less balanced or are slower to stand up when they are sitting. Our technology can measure these subtle differences in a way that you could not with a stopwatch.”

Ref: Hall, Jamie B; Akter, Sonia; Rao, Praveen et al. Feasibility of Using a Novel, Multimodal Motor Function Assessment Platform With Machine Learning to Identify Individuals With Mild Cognitive Impairment. Alzheimer Disease & Associated Disorders 38(4):p 344-350, October–December 2024. | DOI: 10.1097/WAD.0000000000000646