Machine learning method enables quick analysis of amyloid plaques

In a recent study, NIA-supported researchers at the University of California (UC) demonstrated how a computer system with the ability to learn and improve, known as machine learning, could help analyze amyloid plaques in the human brain. Amyloid plaques are abnormal clumps of protein that accumulate in the brains of people with Alzheimer's disease, but not all plaques look alike. As published in Nature Communications, the researchers from both UC San Francisco and UC Davis describe a technique that automates the process of measuring plaques and their different characteristics. This approach could enable larger-scale analysis of brain tissue to help accelerate research on the possible causes of Alzheimer's and how the disease progresses. Using 43 healthy and diseased human brain samples donated to the UC Davis Alzheimer's Disease Center Brain Bank, the researchers taught a computer to detect different types of amyloid plaques within each sample.