Researchers from UC Davis and UC San Francisco have developed a new artificial intelligence tool to scale up Alzheimer’s research. They have created a deep learning system to identify amyloid plaques in brain slices of patients, spotting specific subtypes of Alzheimer’s disease, in the process enabling precision medicine and faster research.
The team used a database of example images to train their machine learning algorithm to identify different types of brain changes seen in Alzheimer’s diseases. This includes discriminating between so-called cored and diffuse plaques, and identifying abnormalities in blood vessels. The researchers found the algorithm can process an entire whole-brain slice slide with 98.7% accuracy. The researchers confirmed the computer’s identification skills were biologically accurate.
“If we can better characterize what we are seeing, this could provide further insights into the diversity of dementia,” Dugger said. “It opens the door to precision medicine for dementias.” She added, “These projects are phenomenal examples of cross-disciplinary translational science; neuropathologists, a statistician, a clinician, and engineers coming together, forming a dialogue and working together to solve a problem.”
Check out this UC Davis video about the research:
Image: The brain of a person with Alzheimer’s (left) compared with the brain of a person without the disease.
The publication in Nature Communications: Interpretable classification of Alzheimer’s disease pathologies with a convolutional neural network pipeline…
Via UC Davis…
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