Mount Sinai researchers use artificial intelligence to uncover the cellular origins of Alzheimer’s disease and other cognitive disorders – EurekAlert

Mount Sinai researchers have used novel artificial intelligence methods to examine structural and cellular features of human brain tissues to help determine the causes of Alzheimers disease and other related disorders. The research team found that studying the causes of cognitive impairment by using an unbiased AI-based methodas opposed to traditional markers such as amyloid plaquesrevealed unexpected microscopic abnormalities that can predict the presence of cognitive impairment. These findings were published in the journal Acta Neuropathologica Communications on September 20.

AI represents an entirely new paradigm for studying dementia and will have a transformative effect on research into complex brain diseases, especially Alzheimers disease, said co-corresponding author John Crary, MD, PhD, Professor of Pathology, Molecular and Cell-Based Medicine, Neuroscience, and Artificial Intelligence and Human Health, at the Icahn School of Medicine at Mount Sinai. The deep learning approach was applied to the prediction of cognitive impairment, a challenging problem for which no current human-performed histopathologic diagnostic tool exists.

The Mount Sinai team identified and analyzed the underlying architecture and cellular features of two regions in the brain, the medial temporal lobe and frontal cortex. In an effort to improve the standard of postmortem brain assessment to identify signs of diseases, the researchers used a weakly supervised deep learning algorithm to examine slide images of human brain autopsy tissues from a group of more than 700 elderly donors to predict the presence or absence of cognitive impairment. The weakly supervised deep learning approach is able to handle noisy, limited, or imprecise sources to provide signals for labeling large amounts of training data in a supervised learning setting. This deep learning model was used to pinpoint a reduction in Luxol fast blue staining, which is used to quantify the amount of myelin, the protective layer around brain nerves. The machine learning models identified a signal for cognitive impairment that was associated with decreasing amounts of myelin staining; scattered in a non-uniform pattern across the tissue; and focused in the white matter, which affects learning and brain functions. The two sets of models trained and used by the researchers were able to predict the presence of cognitive impairment with an accuracy that was better than random guessing.

In their analysis, the researchers believe the diminished staining intensity in particular areas of the brain identified by AI may serve as a scalable platform to evaluate the presence of brain impairment in other associated diseases. The methodology lays the groundwork for future studies, which could include deploying larger scale artificial intelligence models as well as further dissection of the algorithms to increase their predictive accuracy and reliability. The team said, ultimately, the goal of this neuropathologic research program is to develop better tools for diagnosis and treatment of people suffering from Alzheimers disease and related disorders.

Leveraging AI allows us to look at exponentially more disease relevant features, a powerful approach when applied to a complex system like the human brain, said co-corresponding author Kurt W. Farrell, PhD, Assistant Professor of Pathology, Molecular and Cell-Based Medicine, Neuroscience, and Artificial Intelligence and Human Health, at Icahn Mount Sinai. It is critical to perform further interpretability research in the areas of neuropathology and artificial intelligence, so that advances in deep learning can be translated to improve diagnostic and treatment approaches for Alzheimers disease and related disorders in a safe and effective manner.

Lead author Andrew McKenzie, MD, PhD, Co-Chief Resident for Research in the Department of Psychiatry at Icahn Mount Sinai, added: Interpretation analysis was able to identify some, but not all, of the signals that the artificial intelligence models used to make predictions about cognitive impairment. As a result, additional challenges remain for deploying and interpreting these powerful deep learning models in the neuropathology domain.

Researchers from the University of Texas Health Science Center in San Antonio, Texas, Newcastle University in Tyne, United Kingdom, Boston University School of Medicine in Boston, and UT Southwestern Medical Center in Dallas also contributed to this research. The study was supported by funding from the National Institute of Neurological Disorders and Stroke, the National Institute on Aging, and the Tau Consortium by the Rainwater Charitable Foundation.

About the Mount Sinai Health System

Mount Sinai Health System is one of the largest academic medical systems in the New York metro area, with more than 43,000 employees working across eight hospitals, over 400 outpatient practices, nearly 300 labs, a school of nursing, and a leading school of medicine and graduate education. Mount Sinai advances health for all people, everywhere, by taking on the most complex health care challenges of our time discovering and applying new scientific learning and knowledge; developing safer, more effective treatments; educating the next generation of medical leaders and innovators; and supporting local communities by delivering high-quality care to all who need it.

Through the integration of its hospitals, labs, and schools, Mount Sinai offers comprehensive health care solutions from birth through geriatrics, leveraging innovative approaches such as artificial intelligence and informatics while keeping patients medical and emotional needs at the center of all treatment. The Health System includes approximately 7,300 primary and specialty care physicians; 13 joint-venture outpatient surgery centers throughout the five boroughs of New York City, Westchester, Long Island, and Florida; and more than 30 affiliated community health centers. We are consistently ranked by U.S. News & World Report's Best Hospitals, receiving high Honor Roll status, and are highly ranked: No. 1 in Geriatrics and top 20 in Cardiology/Heart Surgery, Diabetes/Endocrinology, Gastroenterology/GI Surgery, Neurology/Neurosurgery, Orthopedics, Pulmonology/Lung Surgery, Rehabilitation, and Urology. New York Eye and Ear Infirmary of Mount Sinai is ranked No. 12 in Ophthalmology. U.S. News & World Reports Best Childrens Hospitals ranks Mount Sinai Kravis Children's Hospital among the countrys best in several pediatric specialties. The Icahn School of Medicine at Mount Sinai is one of three medical schools that have earned distinction by multiple indicators: It is consistently ranked in the top 20 by U.S. News & World Reports Best Medical Schools, aligned with a U.S. News & World Report Honor Roll Hospital, and top 20 in the nation for National Institutes of Health funding and top 5 in the nation for numerous basic and clinical research areas. Newsweeks Worlds Best Smart Hospitals ranks The Mount Sinai Hospital as No. 1 in New York City and in the top five globally, and Mount Sinai Morningside in the top 30 globally; Newsweek also ranks The Mount Sinai Hospital highly in 11 specialties in Worlds Best Specialized Hospitals, and in Americas Best Physical Rehabilitation Centers.

For more information, visit https://www.mountsinai.org or find Mount Sinai on Facebook, Twitter and YouTube.

Acta Neuropathologica Communications

Interpretable deep learning of myelin histopathology in age-related cognitive impairment

20-Sep-2022

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Mount Sinai researchers use artificial intelligence to uncover the cellular origins of Alzheimer's disease and other cognitive disorders - EurekAlert