Researchers have found a way to teach a computer to precisely detect one of the hallmarks of Alzheimer's disease in human brain tissue using artificial intelligence (AI).
The study, published in the journal Nature Communications, is a proof of concept for a machine-learning approach to distinguishing critical markers of the neurodegenerative disease.
Amyloid plaques are clumps of protein fragments in the brains of people with Alzheimer's disease that destroy nerve cell connections, said researchers at University of California, Davis (UC Davis) in the US.
Much like the way Facebook recognises faces based on captured 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 suggest that machine learning can augment the expertise and analysis of an expert neuropathologist.
The tool allows them to analyse thousands of times more data and ask new questions that would not be possible with the limited data processing capabilities of even the most highly trained human experts.
"We still need the pathologist," said Brittany N Dugger, an assistant professor at the UC Davis, and lead author of the study.
"This is a tool, like a keyboard is for writing. As keyboards have aided in writing workflows, digital pathology paired with machine learning can aid with neuropathology workflows," Dugger said.
She partnered with Michael J Keiser, an assistant professor at University of California, San Francisco (UCSF), to determine if they could teach a computer to automate the laborious process of identifying and analysing tiny amyloid plaques of various types in large slices of autopsied human brain tissue.
Keiser and his team designed a "convolutional neural network" (CNN), a computer programme designed to recognise patterns based on thousands of human-labelled examples.
The team devised a method that allowed it to rapidly annotate or label tens of thousands of images from a collection half a million close-up images of tissue from 43 healthy and diseased brain samples.
Like a computer dating service that allows users to swipe left or right to label someone's photo "hot" or "not," they developed a web platform that allowed Dugger to look one-at-a-time at highly zoomed-in regions of potential plaques and quickly label what she saw there.
This digital pathology tool -- which researchers called "blob or not" -- allowed Dugger to annotate more than 70,000 "blobs," or plaque candidates, at a rate of about 2,000 images per hour.
The UCSF team used this database of tens of thousands of labelled example images to train their CNN machine-learning algorithm to identify different types of brain changes seen in Alzheimer's disease.
That includes discriminating between so-called cored and diffuse plaques and identifying abnormalities in blood vessels.
The researchers showed that their algorithm could process an entire whole-brain slice slide with 98.7 per cent accuracy, with speed only limited by the number of computer processors they used.