Artificial Intelligence Proves its Excellent Serviceability in Identifying Neurodegenerative Diseases

According to a study carried out at the Icahn School of Medicine at Mount Sinai and published in Nature medical journal Laboratory Investigation, scientists have created an AI-powered platform to identify a trajectory of the neurodegenerative disease in human brain tissue samples. This range also includes Alzheimer's disease and chronic traumatic encephalopathy. This will help researchers develop biomarkers and therapeutics which would lead to accurate diagnosis of complex brain diseases to improve outcomes.

Since the structure of the brain is so complex, the diagnosis of such diseases with accuracy is challenging and also requires highly trained specialists. The enhancement of abnormal tau proteins in neurofibrillary tangles of the brain is a factor of Alzheimer disease. This also accumulates in other neurodegenerative diseases including chronic traumatic encephalopathy and additional age-related issues.

Researchers have created and used the Precise Informatics Platform at the Centre for Computational and Systems Pathology at Mount Sinai. The platform has been employed to apply strong machine learning approaches to digitize microscopic slides which are groomed using tissue samples from patients having a spectrum of neurodegenerative diseases. These images then used to create a convolutional neural network capable of detecting neurofibrillary tangles by application of deep learning. The network created is a high degree of accuracy directly from digitized images.

Lead investigator John Crary, MD, Ph.D., Professor of Pathology and Neuroscience at the Icahn School of Medicine at Mount Sinai said – "Utilizing artificial intelligence has great potential to improve our ability to detect and quantify neurodegenerative diseases, representing a major advance over existing labor-intensive and poorly reproducible approaches. Ultimately, this project will lead to more efficient and accurate diagnosis of neurodegenerative diseases."

This is the first ever scheme accessible for gauging deep learning algorithms with the usage of large scale image data in neuropathology.  The Informatics Platform developed by Mount Sinai researchers enables data management, visual exploration, object outlining, multi-user review, and calculation of deep learning algorithm outcomes.

Additionally, the researchers at the Mount Sinai Pathology have operated advanced computer science and mathematical techniques along with innovative microscopic technology, computer vision, and AI to categorize a wide range of disease more precisely.

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 at the Icahn School of Medicine asserted – "Mount Sinai is the largest academic pathology department in the country and processes more than 80 million tests a year, which offers researchers access to a broad set of data that can be used to improve testing and diagnostics, ultimately leading to better diagnosis and patient outcomes."