Alzheimer’s research takes a giant leap forward, thanks to a new tool that allows pathologists to analyze and classify amyloid plaques and abnormalities in blood vessels through technology similar to face recognition software – leading to the ability to process a vast amount of data in a markedly reduced amount of time.
The study, conducted by UC Davis and UC San Francisco, tested the ability to automate the existing time-intensive process of reviewing, identifying, and analyzing microscopic amyloid plaques in brain tissue. Dubbed “blob or not,” this digital pathology tool was found to be highly efficient – correctly annotating plaque samples at the unimaginable rate of 2,000 per hour – limited only by the number of processors used in the study. The program was even able to distinguish between different types of plaques and abnormalities with 98.7% accuracy.
The program, known as a “convolutional neural network” (CNN), has the ability to analyze thousands of slices of autopsied human brain tissue – both with Alzheimer’s and without – and then classify according to the appropriate patterns.
Brittany Dugger, PhD, lead researcher in the study and assistant professor in UCSF’s Institute for Neurodegenerative Diseases and Department of Pharmaceutical Chemistry, clarifies, “We still need the pathologist. This is a tool, like a keyboard for writing. As keyboards have aided in writing workflows, digital pathology paired with machine learning can aid with neuropathology workflows.”
The eventual goal is for this technology to become a routine part of neuropathologic research, and of course to draw us ever nearer to a cure for Alzheimer’s – which currently impacts as many as 5.8 million Americans, according to the most recent stats from the Alzheimer’s Association, along with the family members who care for them.
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