Scientists at Stanford University created the first artificial intelligence (AI) model that could help predict the risk of manifestation of some 130 diseases from information collected in a single night of sleep, according to a study published in the journal Nature Medicine.
The SleepFM model has been trained with almost 600,000 hours of sleep data from about 65,000 participants, combining brain, heart, muscle and respiratory signals, collected through polysomnography (PSG), the “gold standard” for sleep analysis, the researchers stated in the article.
This would be the first research to use AI to analyze sleep data on a large scale.
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The new tool could identify risks for diseases with high mortality rates such as dementia, myocardial infarction, heart failure, chronic kidney disease, stroke and atrial fibrillation.
“SleepFM produces latent representations of sleep that capture the physiological and temporal structure of sleep and allow accurate prediction of future disease risk,” the text explains.
The study insists that artificial intelligence makes it possible to overcome the challenges of analyzing the amount of data obtained from polysomnography.
“From an AI perspective, sleep is relatively understudied,” said James Zou, PhD, associate professor of biomedical data science and co-author of the study, on the website of Stanford School of Medicine, one of the elite American academic centers, located in California.
With information from EFE
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