AI used to determine the cause of Alzheimer's disease and related disorders

AI used to determine the cause of Alzheimer’s disease and related disorders

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According to a study by Mount Sinai researchers, new artificial intelligence methods have revealed unexpected microscopic abnormalities that can predict cognitive impairment. These findings were published in the journal Acta Neuropathologica Communications this week.

“AI represents an entirely new paradigm for the study of dementia and will have a transformative effect on research into complex brain diseases, especially Alzheimer’s disease,” said co-corresponding author John Crary, MD, PhD, Professor of Pathology, Molecular and Cellular Medicine. , neuroscience, artificial intelligence, and human health, at the Icahn School of Medicine at Mount Sinai.

He added that “the deep learning approach has been applied to the prediction of cognitive impairment, a difficult problem for which there are currently no human histopathological diagnostic tools”.

The Mount Sinai team identified and analyzed the underlying architecture and cellular characteristics of two brain regions, the medial temporal lobe and the frontal cortex. In an effort to improve the standard of post-mortem assessment of the brain to identify signs of disease, researchers used a loosely supervised deep learning algorithm to examine images of human brain autopsy tissue slides from d a group of more than 700 elderly donors in order to predict the presence or absence. cognitive disorders.

The weakly supervised deep learning approach, they report, is able to handle noisy, limited, or imprecise sources to provide cues to label large amounts of training data in a supervised learning setting. This pattern was used to identify a reduction in Luxol Fast Blue staining, which is used to quantify the amount of myelin, the protective layer around brain nerves.

The researchers identified a signal of cognitive impairment associated with a decrease in myelin staining; scattered in a non-uniform pattern through the fabric; and concentrated in white matter, which affects learning and brain function. Both sets of models trained and used by the researchers were able to predict the presence of cognitive impairment with better accuracy than random guessing.

The team believe that the decrease in staining intensity in particular areas of the brain identified by AI may serve as an evolutionary platform to assess the presence of brain impairment in other associated diseases. The methodology lays the groundwork for future studies, which could include the deployment of larger-scale artificial intelligence models as well as further dissection of the algorithms to increase their predictive accuracy and reliability. The team said that ultimately the goal of this neuropathological research program is to develop better diagnostic and treatment tools for people with Alzheimer’s disease and related disorders.

“Using AI allows us to examine exponentially more disease-relevant features, a powerful approach when applied to a complex system like the human brain,” said the co-corresponding author. Kurt W. Farrell, PhD, Assistant Professor of Molecular, and Cellular Pathology. Based on medicine, neuroscience, artificial intelligence and human health, at Icahn Mount Sinai. “Further interpretability research in the fields of neuropathology and artificial intelligence is essential, so that advances in deep learning can be translated to improve diagnostic and treatment approaches. Alzheimer’s disease and related disorders safely and effectively.

Senior author Andrew McKenzie, MD, PhD, co-chief research resident in the Department of Psychiatry at Icahn Mount Sinai, added, “Interpretive analysis was able to identify some, but not all, of the signals that artificial intelligence models used to make predictions about cognitive impairment. As a result, additional challenges remain for the deployment and interpretation of these powerful deep learning models in the field of neuropathology.

Researchers from the University of Texas Health Sciences Center in San Antonio, Texas, Newcastle University in Tyne, UK, Boston University School of Medicine in Boston, and UT Southwestern Medical Center in Dallas also contributed to this research.

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