Researchers have developed an artificial intelligence platform to detect a selection of neurodegenerative diseases in human brain tissue samples, such as Alzheimer’s disease and chronic traumatic encephalopathy, based on a research conducted at the Icahn Medical school in Mount Sinai and published in the Nature clinical journal Laboratory Investigation. Their discovery will assist scientists develop targeted biomarkers and therapeutics, leading to a diagnosis of complicated brain diseases that improve patient outcomes. The accumulation of tau proteins in the brain in neurofibrillary tangles are a characteristic of Alzheimer’s disease, however it also collects in other neurodegenerative diseases, like chronic traumatic encephalopathy and extra age conditions.
Accurate diagnosis of diseases demands a specialist and is challenging. Researchers at the middle for Computational and Systems Pathology at Mount Sinai developed and utilized the Exact Informatics Platform to employ strong machine learning methods to microscopic slides prepared using tissue samples from patients with a range of diseases. Implementing profound learning, these pictures have been used to create a convolutional neural network capable of identifying tangles with a high level of accuracy straight from pictures. Utilizing artificial intelligence has excellent potential to improve our capacity to detect and quantify neurodegenerative diseases, representing a major advance over existing labour intensive and poorly reproducible approaches, “‘said lead researcher John Crary, MD, PhD, Professor of Pathology and Neuroscience in the Icahn Medical school at Mount Sinai.
In the end, this project will lead to more effective and precise identification of neurodegenerative diseases. This is the initial framework available for evaluating learning algorithms utilizing large scale picture data. The Exact Informatics Platform enables for information managements, visual exploration, object outlininguser inspection, and evaluation of how learning algorithm results. Researchers at the middle on computational and Systems Pathology at Mount Sinai have utilized use advanced computer engineering and mathematical techniques combined with cutting edge microscope technology, computer vision, and artificial intelligence to accurately classify a wide range of diseases. Mount Sinai is the biggest academic pathology department within the country and procedures over 80 million tests per year, that provides researchers access to a wide set of information that may be utilised to improve testing and diagnostics, finally resulting in better diagnosis and patient results, said author Carlos Cordon Cardo, MD, PhD, Chair of the Department of Pathology at the Mount Sinai Health System and Professor of pathology, genetics and Genomic Sciences, and Oncological Sciences in the Icahn Medical school.