In a study performed at the Icahn School of Medicine at Mount Sinai, scientists have created an artificial intelligence platform to detect various neurodegenerative disease in samples of human brain tissue, including chronic traumatic encephalopathy and Alzheimer’s disease.
The study findings will help researchers develop targeted biomarkers and therapeutics, leading to a more accurate diagnosis of complex brain diseases that enhances patient outcomes. The study has been published in the Nature medical journal Laboratory Investigation.
The accumulation of abnormal tau proteins in the brain in neurofibrillary tangles is a characteristic of Alzheimer’s disease; however, it also builds up in other neurodegenerative diseases, for example, chronic traumatic encephalopathy and other age-related conditions. Precise diagnosis of neurodegenerative diseases is difficult and needs a highly experienced specialist.
Scientists at the Center for Computational and Systems Pathology at Mount Sinai created and used the Precise Informatics Platform to employ potent machine learning strategies to digitized microscopic slides made using tissue samples from patients with a range of neurodegenerative diseases. With the application of deep learning, these images were used to produce a convolutional neural network that can identify neurofibrillary tangles directly from digitized images with a high degree of accuracy.
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.
John Crary, MD, PhD, Lead Investigator and Professor, Pathology and Neuroscience, Icahn School of Medicine, Mount Sinai
This is the first-ever framework available for assessing deep learning algorithms with the help of large-scale image data in neuropathology. The Precise Informatics Platform enables visual exploration, data management, multi-user review, object outlining, and assessment of deep learning algorithm results.
Scientists at the Center for Computational and Systems Pathology at Mount Sinai have used sophisticated computer science and mathematical methods in combination with advanced microscope technology, artificial intelligence, and computer vision to more precisely categorize a wide spectrum of diseases.
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.
Carlos Cordon-Cardo, MD, PhD, Study Author and Chair of the Department of Pathology, Mount Sinai Health System
Cordon-Cardo is also Professor of pathology, genetics and genomic sciences, and oncological sciences at the Icahn School of Medicine.
Boston University School of Medicine, VA Boston Healthcare System, and UT Southwestern Medical Center contributed to this research.
The work was supported by grants from the Department of Defense, the National Institutes of Health, Alzheimer’s Association, and the Rainwater Charitable Foundation.