New computational model can detect cognitive impairment from audio recordings of neuropsychological tests
It takes a lot of time and money to diagnose Alzheimer’s disease. After running a lengthy face-to-face neuropsychological exam, the doctor must copy, review, and analyze each response in detail. But researchers at Boston University have developed a new tool that could automate the process and eventually allow it to move online. Their machine learning-powered computational model can detect cognitive impairment from audio recordings of neuropsychological-tests; no in-person appointment required. Their findings were published in Alzheimer’s & Dementia: Journal of the Alzheimer’s Association . “This approach brings us one step closer to early intervention,” said Ioannis Paschalidis, co-author of the paper and BU College of Engineering Distinguished Professor of Engineering. He said faster early detection of Alzheimer’s could prompt larger clinical trials that focus on individuals in the early stages of the disease and potentially enable clinical interventions that slow cognitive