Scientists have developed an artificial intelligence system that can predict a person’s risk of developing conditions ranging from dementia to heart failure by analyzing a single night of sleep data. The findings suggest that sleep patterns contain far more information about future health than previously recognized. Researchers at Stanford University and collaborators trained an AI model called Sleep FM on polysomnography recordings from more than 65,000 people, representing over 585,000 hours of sleep data. Polysomnography is the gold standard sleep study that records brain activity, heart rhythms, breathing patterns, and muscle movements throughout the night. After analyzing these overnight recordings, the model identified elevated future risk for 130 medical conditions, often years before clinical diagnosis. For all-cause mortality, the system achieved a concordance index of 0.84, meaning it correctly ranked patient risk 84% of the time. Similar accuracy emerged for dementia (0.85), heart attack (0.81), heart failure (0.80), chronic kidney disease (0.79), stroke (0.78), and atrial fibrillation (0.78). Researchers at Stanford University and collaborators trained an AI model called Sleep FM on polysomnography recordings from more than 65,000 people, representing over 585,000 hours of sleep data. Polysomnography is the gold standard sleep study that records brain activity, heart rhythms, breathing patterns, and muscle movements throughout the night. After analyzing these overnight recordings, the model identified elevated future risk for 130 medical conditions, often years before clinical diagnosis. For all-cause mortality, the system achieved a concordance index of 0.84, meaning it correctly ranked patient risk 84% of the time. Similar accuracy emerged for dementia (0.85), heart attack (0.81), heart failure (0.80), chronic kidney disease (0.79), stroke (0.78), and atrial fibrillation (0.78). Sleep recordings capture intricate interactions across physiological systems that change over time, likely reflecting underlying processes that contribute to or signal future disease development. For mortality risk, research has linked factors including high arousal burden, low REM sleep, sleep-disordered breathing, low oxygen levels, and poor sleep efficiency to increased death rates. The model’s success with dementia prediction is noteworthy given that sleep abnormalities are strongly associated with preclinical Alzheimer’s disease, including reduced slow-wave activity, REM sleep disturbances, and decreased spindle activity. Parkinson’s disease is frequently preceded by REM sleep behavior disorder, characterized by abnormal muscle activity during REM sleep and distinctive patterns in brain and heart recordings.
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