A team of researchers led by Mount Sinai has significantly advanced an artificial intelligence (AI)-powered algorithmdesigned to analyze video recordings from clinical sleep tests, enhancing the accuracy of diagnosing REM Sleep Behavior Disorder (RBD)—a common sleep disorder affecting over 80 million people worldwide. This breakthrough, published in the journal Annals of Neurology on January 9, promises to improve diagnostic precision and aid early detection of Parkinson’s disease and dementia, conditions often heralded by RBD.
RBD is characterized by abnormal movements or the acting out of dreams during the REM phase of sleep. When it occurs in otherwise healthy individuals, it is referred to as “isolated RBD,” which affects more than one million people in the United States alone. Nearly all cases of isolated RBD are early indicators of neurodegenerative conditions such as Parkinson’s disease or dementia.
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