Insufficient sleep increases the risk of developing serious medical conditions and shortened lifespan. Furthermore, sleep disorders affect hundreds of millions of people worldwide causing a major burden to the affected individuals, society, and economy. While sleep disorders are treatable, a majority of cases remain undiagnosed. This is mainly because current best-practices in sleep medicine are laborious and expensive, and do not take full advantage of today's technological possibilities.The current diagnostic methods are technologically complex and cumbersome for diagnosing and treating ever-increasing numbers of patients and are particularly unsuitable for personalized early risk prediction, prevention, and intervention. However, recent advances in wearable sensor technology, digital signal processing, artificial intelligence, and big data technologies now provide exciting opportunities to develop new solutions, which can monitor sleep quality and detect a broader range of sleep disorders, better quantify disease severity, and better identify individuals at higher risk for severe health consequences.We welcome submissions from the fields of physics, telemedicine, biomedical engineering, digital signal processing, artificial intelligence, sleep medicine, sleep disorders, wearable sensor technology, sleep trackers, biomedical devices, diagnostic systems, and health economics. Possible themes can include but are not limited to:· Artificial intelligence in analyzing sleep r...

Not just overnight sleep tests, Artificial intelligence (AI) also has the potential to improve efficiencies and precision in sleep medicine, resulting in more patient-centred care and better outcomes, researchers have found. The electrophysiological data collected during polysomnography, the most comprehensive type of sleep study, is well-positioned for enhanced analysis through AI and machine-assisted learning, according to a new position statement from the American Academy of Sleep Medicine. "When we typically think of AI in sleep medicine, the obvious use case is for the scoring of sleep and associated events," said Cathy Goldstein, associate professor of sleep medicine and neurology at the University of Michigan. "This would streamline the processes of sleep laboratories and free up sleep technologist time for direct patient care." Because of the vast amounts of data collected by sleep centres, AI and machine learning could advance sleep care, resulting in a more accurate diagnosis, prediction of disease and treatment prognosis.

Published online as an accepted paper in the Journal of Clinical Sleep Medicine, the position statement was developed by the AASM's Artificial Intelligence in Sleep Medicine Committee. According to the statement, the electrophysiological data collected during polysomnography -- the most comprehensive type of sleep study -- is well-positioned for enhanced analysis through AI and machine-assisted learning. "When we typically think of AI in sleep medicine, the obvious use case is for the scoring of sleep and associated events," said lead author and committee Chair Dr. Cathy Goldstein, associate professor of sleep medicine and neurology at the University of Michigan. "This would streamline the processes of sleep laboratories and free up sleep technologist time for direct patient care." Because of the vast amounts of data collected by sleep centers, AI and machine learning could advance sleep care, resulting in more accurate diagnoses, prediction of disease and treatment prognosis, characterization of disease subtypes, precision in sleep scoring, and optimization and personalization of sleep treatments.

Artificial intelligence has already proved its potential in diverse areas, with performing tedious, mundane tasks in a complex environment, enabling businesses to drive efficiency and more. Today's routine lives are totally impacted by the technology as it provides people a different capability to do their works. AI even offers healthcare professionals the ability to perform crucial treatment with ease. Now the technology could be leveraged to improve efficiencies and precision in sleep disorder treatment, resulting in more improved care and better patient outcomes, according to the American Academy of Sleep Medicine's (AASM) new position statement. Developed by AASM's Artificial Intelligence in Sleep Medicine Committee and published in the Journal of Clinical Sleep Medicine, the position statement noted that the electrophysiological data collected during polysomnography – the most comprehensive study on sleep – is well-positioned for enhanced analysis with AI and machine learning.

Artificial intelligence has the potential to improve efficiencies and precision in sleep medicine, resulting in more patient-centered care and better outcomes, according to a new position statement from the American Academy of Sleep Medicine. Published online as an accepted paper in the Journal of Clinical Sleep Medicine, the position statement was developed by the AASM's Artificial Intelligence in Sleep Medicine Committee. According to the statement, the electrophysiological data collected during polysomnography--the most comprehensive type of sleep study--is well-positioned for enhanced analysis through AI and machine-assisted learning. "When we typically think of AI in sleep medicine, the obvious use case is for the scoring of sleep and associated events," said lead author and committee Chair Dr. Cathy Goldstein, associate professor of sleep medicine and neurology at the University of Michigan. "This would streamline the processes of sleep laboratories and free up sleep technologist time for direct patient care."