The tool was trained by a team of University of California scientists using thousands of slide images of tissue taken from autopsies of both healthy and diseased brains. The researchers built a convolutional neural network out of the images to teach the program to recognize the differences between different types of amyloid plaques and to do so in biologically valid methods, rather than being based simply on programmed patterns.
As a result, the tool was able to process a slide depicting a whole-brain slice with 98.7 percent accuracy, which the study’s authors note does not supersede the abilities of a human neuropathologist.
“It’s a copilot, a force multiplier that extends the scope of what we can accomplish and lets us ask questions we never would have attempted manually,” said Michael J. Keiser, PhD, an assistant professor at UC San Francisco. “For example, we can look for rare plaques in unexpected places that could give us important clues about the course of the disease.”
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