Researchers at UC Davis Health and UC San Francisco have found a way to teach a computer to detect one of the hallmarks of Alzheimer’s disease in human brain tissue. The research delivers 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 images, the machine learning tool can “see” if a sample of brain tissue has one type of amyloid plaque or another, and do it very quickly.

The findings, published May 15 in Nature Communications, suggest that machine learning can add to the expertise and analysis of a neuropathologist. The tool allows them to analyze thousands of times more data and ask new questions otherwise impossible with the limited data processing capabilities of highly trained human experts.

We still need the pathologist. This is a tool, like a keyboard is for writing. As keyboards help writing workflows, digital pathology paired with machine learning helps with neuropathology workflows.
Brittany N. Dugger, an assistant professor in the UC Davis Department of Pathology and Laboratory Medicine and lead author of the study

 

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