Portable System Uses AI to Identify Individuals With Mild Cognitive Impairment

 Post stroke you should probably decline to be tested this way.

Portable System Uses AI to Identify Individuals With Mild Cognitive Impairment

A machine learning model was highly accurate in identifying individuals with mild cognitive impairment (MCI) using motor function data obtained with a portable, inexpensive, multimodal device, according to a study published in the journal Alzheimer Disease & Associated Disorders.

The novel motor function assessment platform integrates a depth camera, forceplate, and interface board to assess individuals during static balance, gait, and sit-to-stand activities in both single- and dual-task conditions. The individual’s performance is captured and fed into an artificial intelligence (AI) model to identify those with MCI.

“There are new drugs coming out to treat those with MCI, but you need a diagnosis of MCI to qualify for the medications,” said Jamie B. Hall, University of Missouri-Columbia, Columbia, Missouri. “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.”

The researchers tested the portable system on 28 healthy older adults and 19 patients with MCI. The AI-based portable system accurately identified 83% of those in the study with MCI.

The researchers noted that early identification of clinical conditions associated with Alzheimer disease and related dementias (ADRD) is vital for intervention. One promising early detection method is the use of instrumented assessment to identify subtle motor declines associated with ADRD.

“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 M. Guess, PhD, University of Missouri-Columbia. “These can be very subtle differences in motor function related to balance and walking that our new device is able to detect but would go unnoticed through observation.”

The researchers will continue the research with additional participants and also look at the portable system’s ability to detect fall risk and frailty among older adults.

Reference: https://journals.lww.com/alzheimerjournal/abstract/2024/10000/feasibility_of_using_a_novel,_multimodal_motor.7.aspx

SOURCE: University of Missouri-Columbia