Researchers at UC Davis and UC San Francisco have found a way to teach a computer to precisely detect one of the hallmarks of Alzheimer’s disease in human brain tissue, delivering a proof of concept for a machine-learning approach to distinguishing critical markers of the disease.
Amyloid plaques are clumps of protein fragments in the brains of people with Alzheimer’s disease that destroy nerve cell connections. Much like the way Facebook recognizes faces based on captured images, the machine learning tool developed by a team of University of California scientists can “see” if a sample of brain tissue has one type of amyloid plaque or another, and do it very quickly.
