AI identifies previously unrecognized health insights in routine sleep studies
A novel AI model can use information collected during routine sleep studies to identify patients' long-term health risks, according to a new study published in Nature Communications. Developed by a multidisciplinary research team, the model uncovered hidden sleep patterns linked to risks including heart disease, cognitive decline and death.
A new artificial intelligence model has the ability to uncover long-term health risks from routine sleep studies, according to a recent study published in Nature Communications. Developed by a team of researchers from the Cleveland Clinic and IBM, the AI model analyzed data from polysomnograms, which are overnight sleep studies that typically evaluate sleep apnea severity.
However, the study found that the model was able to identify clinically meaningful patient subtypes with significantly different long-term health risks, such as heart disease, cognitive decline, and mortality risk. The highest-risk group had about twice the 5-year mortality risk compared to the lowest-risk group, a distinction that was not apparent using the standard clinical measure for sleep apnea severity, the apnea-hypopnea index.
The study suggests that routine medical tests like sleep studies may contain more physiological information than currently utilized. This discovery could potentially expand the value of routine sleep testing and lead to earlier and more personalized care for various chronic diseases, including cardiovascular and neurologic conditions.
Written by urgent.news from Medical Xpress's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
