An artificial intelligence platform has been developed which researchers claim can detect a range of neurodegenerative disease in human brain tissues samples – such as Alzheimer’s.
The study was conducted by researchers at the Icahn School of Medicine at Mount Sinai and published in Laboratory Investigation.
The discovery will help scientists develop targeted biomarkers and therapeutics, resulting in a more accurate diagnosis of complex brain diseases that improve patient outcomes.
The build-up of abnormal tau proteins in the brain in neurofibrillary tangles is a feature of Alzheimer’s disease, but it also accumulates in other neurodegenerative diseases, such as chronic traumatic encephalopathy and additional age-related conditions.
Accurate diagnosis of neurodegenerative diseases is challenging and requires a highly-trained specialist.
Researchers at the Centre for Computational and Systems Pathology at Mount Sinai developed and used the Precise Informatics Platform to apply powerful machine learning approaches to digitized microscopic slides prepared using tissue samples from patients with a spectrum of neurodegenerative diseases.
Applying deep learning, these images were used to create a convolutional neural network capable of identifying neurofibrillary tangles with a high degree of accuracy directly from digitised images.
“Utilising AI has great potential to improve our ability to detect and quantify neurodegenerative diseases, representing a major advance over existing labour-intensive and poorly reproducible approaches,” said lead investigator John Crary, Professor of Pathology and Neuroscience at the Icahn School of Medicine at Mount Sinai.
“Ultimately, this project will lead to more efficient and accurate diagnosis of neurodegenerative diseases.”
This is the first framework available for evaluating deep learning algorithms using large-scale image data in neuropathology.