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Researchers use artificial intelligence to explore cellular origins of Alzheimer’s disease along with other cognitive disorders

Alzheimer's disease
PET scan of a mind with Alzheimer’s disease. Credit: public domain

Mount Sinai researchers purchased novel artificial intelligence solutions to examine structural and cellular top features of mind tissues to greatly help determine the sources of Alzheimer’s disease along with other related disorders. The study team discovered that studying the sources of cognitive impairment through the use of an unbiased AI-based methodas against traditional markers such as for example amyloid plaquesrevealed unexpected microscopic abnormalities that may predict the current presence of cognitive impairment. These findings were published in the journal Acta Neuropathologica Communications on September 20.

“AI represents a completely new paradigm for studying and can have a transformative influence on research into complex brain diseases, especially Alzheimer’s disease,” said co-corresponding author John Crary, MD, Ph.D., 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 put on the prediction of cognitive impairment, a challenging problem that no current human-performed histopathologic diagnostic tool exists.”

The Mount Sinai team identified and analyzed the underlying architecture and cellular top features of two regions in the mind, the medial temporal lobe and frontal cortex. In order to enhance the standard of postmortem brain assessment to recognize signs of diseases, the researchers used a weakly supervised deep learning algorithm to look at slide images of mind autopsy tissues from the group of a lot more than 700 elderly donors to predict the presence or lack of cognitive impairment. The weakly supervised deep learning approach has the capacity to handle noisy, limited, or imprecise sources to supply signals for labeling huge amounts of training data in a supervised learning setting.

This deep learning model was used to pinpoint a decrease in Luxol fast blue staining, that is used to quantify the quantity of myelin, the protective layer around brain nerves. The device learning models identified a sign for cognitive impairment that has been connected with decreasing levels of myelin staining; scattered in a non-uniform pattern over the tissue; and focused in the white matter, which affects learning and brain functions. Both sets of models trained and utilized by the researchers could actually predict the current presence of cognitive impairment having an accuracy that has been much better than random guessing.

Within their analysis, the researchers believe the diminished staining intensity specifically areas of the mind identified by AI may serve as a scalable platform to judge the current presence of brain impairment in other associated diseases. The methodology lays the groundwork for future studies, that could include deploying larger scale artificial intelligence models and also further dissection of the algorithms to improve their predictive accuracy and reliability. The team said that ultimately, the purpose of this neuropathologic research program would be to develop better tools for diagnosis and treatment of individuals experiencing Alzheimer’s disease and related disorders.

“Leveraging AI we can look at exponentially more disease relevant features, a robust approach when put on a complex system just like the mind,” said co-corresponding author Kurt W. Farrell, Ph.D., Assistant Professor of Pathology, Molecular and Cell-Based Medicine, Neuroscience, and Artificial Intelligence and Human Health, at Icahn Mount Sinai. “It is advisable to perform further interpretability research in the regions of neuropathology and artificial intelligence, in order that advances in deep learning could be translated to boost diagnostic and treatment approaches for Alzheimer’s disease and related disorders in a effective and safe manner.”

Lead author Andrew McKenzie, MD, Ph.D., Co-Chief Resident for Research in the Department of Psychiatry at Icahn Mount Sinai, added, “Interpretation analysis could identify some, however, not all, of the signals that the models used to create predictions about . Because of this, 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, UK, Boston University School of Medicine in Boston, and UT Southwestern INFIRMARY in Dallas also contributed to the research.

More info: Interpretable deep learning of myelin histopathology in age-related cognitive impairment, Acta Neuropathologica Communications (2022). actaneurocomms.biomedcentral.c 6/s40478-022-01425-5

Citation: Researchers use artificial intelligence to explore cellular origins of Alzheimer’s disease along with other cognitive disorders (2022, September 20) retrieved 21 September 2022 from

This document is at the mercy of copyright. Aside from any fair dealing for the intended purpose of private study or research, no part could be reproduced minus the written permission. This content is provided for information purposes only.

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