Mount Sinai-led team enhances automated method to detect common sleep disorder affecting millions
A Mount Sinai-led team of researchers has enhanced an artificial intelligence (AI)-powered algorithm to analyze video recordings of clinical sleep tests, ultimately improving accurate diagnosis of a common sleep disorder affecting more than 80 million people worldwide. The study findings were published in the journal Annals of Neurology on January 9.
REM sleep behavior disorder (RBD) is a sleep condition that causes abnormal movements, or the physical acting out of dreams, during the rapid eye movement (REM) phase of sleep. RBD that occurs in otherwise healthy adults ...










