Sleep has long been associated with well-being and recovery.
New research now shows that sleep may also contain information that goes far beyond tiredness and sleep quality.
A study published in Nature Medicine describes how artificial intelligence can use data from a single night’s sleep to assess the risk of a wide range of diseases.
AI examines sleep
The study was conducted by researchers from Stanford Medicine and their collaborators.
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They developed an AI model called SleepFM, which was trained on nearly 600,000 hours of sleep data from approximately 65,000 individuals.
The data come from polysomnography, a hospital-based examination in which brain activity, heart rhythm, breathing, and movement are measured throughout the night.
According to Nature Medicine, these sleep data were linked to the patients’ electronic health records, which in some cases extended up to 25 years into the future.
This allowed the researchers to examine whether specific sleep patterns were associated with the later development of disease.
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What the results show
The model was able to predict the risk of 130 different diseases with reasonable accuracy.
These include certain types of cancer, cardiovascular disease, mental disorders, and neurological diseases, Nature Medicine reports.
Stanford Medicine states that the results were strongest when multiple bodily signals were analyzed together rather than individually.
What does it mean?
The researchers emphasize that the model is not yet able to explain its assessments in a way that is easy to use in clinical practice.
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As a result, the technology is not ready for routine clinical use.
However, the study suggests that existing sleep data could play a greater role in future disease prevention if the method is further developed and tested more broadly.
Sources: Stanford Medicine, and Nature Medicine.
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