AI and the science of sleep

Artificial intelligence is improving healthcare systems around the world, and the prospects for its use in medicine are limitless. And scientists have suggested that combining AI data with other health information could prove useful in diagnosing and treating sleep disorders. Machine algorithms could also provide a deeper understanding of the role of sleep in maintaining good health.

Already, recent advances in sensor technology, big data analysis, and artificial intelligence are enabling truly ubiquitous yet unobtrusive monitoring of sleep and circadian rhythms. And scientists are actively developing best practices that will help incorporate new and emerging technologies into the daily practice of clinicians — healthcare providers as well as other stakeholders. Artificial intelligence, if systematically mobilized with human involvement, can improve sleep medicine and use sleep science to support the health and well-being of patients.

To date, the consideration of sleep disorders and the selection of treatment for such conditions has involved the use of polysomnography, a special study that brings in a wealth of electrophysiological data. And the application of AI-based programs can be of enormous benefit in this aspect, interpreting these data and finding patterns and abnormalities.

AI allows:
- to accurately classify disorders and diseases, to make a correct diagnosis;
- predict the possible development of problems in the near or distant future;
- divide diseases into subtypes;
- carry out accurate and automated processing in terms of sleep evaluation;
- choose the most effective individual treatment, etc.

By their very nature, machine learning algorithms and programs capture patterns by adjusting variables and improving predictive performance. Electrophysiological data obtained from vast numbers of polysomnography recordings is the basis for AI apps.
And scientists have figured out how to supplement this information with more individual factors, including genetic information, demographic data, as well as data on behavior, lifestyle, etc. With this personalized approach, AI becomes a truly effective and powerful tool for providing previously unknown information, enabling accurate diagnosis and clinical treatment of patients with sleep disorders.