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Background: The past few years have seen a rapid emergence of artificial intelligence (AI)-enabled technology in the field of sleep medicine. AI refers to the capability of computer systems to perform tasks conventionally considered to require human intelligence, such as speech recognition, decision-making, and visual recognition of patterns and objects. The practice of sleep tracking and measuring physiological signals in sleep is widely practiced. Therefore, sleep monitoring in both the laboratory and ambulatory environments results in the accrual of massive amounts of data that uniquely positions the field of sleep medicine to gain from AI., Method: The purpose of this article is to provide a concise overview of relevant terminology, definitions, and use cases of AI in sleep medicine. This was supplemented by a thorough review of relevant published literature., Results: Artificial intelligence has several applications in sleep medicine including sleep and respiratory event scoring in the sleep laboratory, diagnosing and managing sleep disorders, and population health. While still in its nascent stage, there are several challenges which preclude AI's generalizability and wide-reaching clinical applications. Overcoming these challenges will help integrate AI seamlessly within sleep medicine and augment clinical practice., Conclusion: Artificial intelligence is a powerful tool in healthcare that may improve patient care, enhance diagnostic abilities, and augment the management of sleep disorders. However, there is a need to regulate and standardize existing machine learning algorithms prior to its inclusion in the sleep clinic., (© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Background: Although sleep disturbances are common among people with Multiple Sclerosis (PwMS), understanding of their impact has been stymied by limitations in approaches to sleep measurement within this population. The aim of this study was to comprehensively phenotype sleep patterns in PwMS through application of an emerging seven-domain framework that includes sleep duration, continuity, timing, quality, rhythmicity, regularity, and sleepiness., Methods: Sleep domains were estimated from wrist-worn accelerometry, Epworth Sleepiness Scale and Pittsburgh Sleep Quality Index responses. Extreme sleep values within each domain were constructed using previously published guidelines. A composite score of extreme values was calculated for each participant. Associations between sleep domains and severity of MS symptoms were explored (pain, fatigue, depressive symptoms, and cognitive dysfunction)., Results: Among n = 49 participants, median total sleep time was 456.3 min. Median time spent awake after sleep onset was 37 min. Sleepiness, abnormal sleep timing, and poor sleep quality affected 33%, 35%, and 45% of participants, respectively. Seventy-six percent had ≥2 sleep domains in extreme ranges. PwMS had longer sleep duration and decreased sleep regularity compared to a non-MS historical cohort of older men. Greater daytime sleepiness, poorer sleep quality, and higher composite sleep health score were associated with more depressive symptoms, and lower sleep rhythmicity was associated with higher fatigue. Associations were observed between measures of cognitive function and sleep fragmentation, duration, quality, rhythmicity, and composite score., Conclusion: Application of a seven-domain sleep health framework that captures the dynamic and multifaceted aspects of sleep is feasible in PwMS, and offers potential for an improved understanding of the scope and impact of sleep disturbances in PwMS., (Copyright © 2021. Published by Elsevier B.V.)

Study Objectives: Obstructive sleep apnea and other sleep disorders overlap with comorbidities associated with poor outcomes related to severe acute respiratory syndrome coronavirus 2 infection. However, the prevalence of obstructive sleep apnea among patients hospitalized for COVID-19 and relationship to outcomes is poorly characterized, and the relevance of other sleep disorders remains unknown. The objective of this study was to identify the prevalence of pre-existing sleep disorders and association with outcomes related to severe COVID-19 illness., Methods: Patients with severe acute respiratory syndrome coronavirus 2 infection admitted to the University of Michigan Hospital System were included. Electronic medical records were queried for sleep disorders diagnostic codes. Data were extracted from polysomnography and home sleep testing in a subgroup with previous diagnostic testing at our center. Logistic regression was used to examine the association of sleep disorders with mechanical ventilation requirement, treatment with vasopressors, and death and Cox proportional hazards regression for time to discharge., Results: Among n = 572 adult patients hospitalized for COVID-19, 113 (19.8%) patients had obstructive sleep apnea, 4 patients had central sleep apnea (0.7%), 5 had hypoventilation (0.9%), 63 had insomnia (11.0%), and 22 had restless legs syndrome or periodic limb movements disorder (3.9%). After adjusting for age, sex, body mass index, and race, no significant relationship was apparent between sleep disorders diagnoses or indices of sleep-disordered breathing severity and outcomes., Conclusions: This is the first study to determine the prevalence of obstructive sleep apnea and other sleep disorders in a well-characterized cohort of patients hospitalized for COVID-19. Once hospitalized, a significant contribution of sleep disorders to outcomes was not identified. Therefore, future evaluations should focus on earlier outcomes, such as infection or clinical manifestations after exposure to severe acute respiratory syndrome coronavirus 2., (© 2021 American Academy of Sleep Medicine.)

Consumer sleep technologies have rapidly evolved from wrist-worn activity trackers to multisensory products. These technologies reflect a widespread interest in sleep health, and their ubiquitous ownership allows for remote sleep monitoring. Therefore, these technologies may play a valuable role in telemedicine. However, clinical usefulness remains contested and is limited by a lack of transparency in data acquisition and analysis, and uncertain accuracy of consumer sleep technology-derived sleep metrics. Collaboration between manufacturers, sleep scientists, and clinical sleep medicine teams is required to produce useable, verified technologies that can enhance and personalize the care of patients with sleep disorders., (Copyright © 2020 Elsevier Inc. All rights reserved.)

None: Sleep medicine is well positioned to benefit from advances that use big data to create artificially intelligent computer programs. One obvious initial application in the sleep disorders center is the assisted (or enhanced) scoring of sleep and associated events during polysomnography (PSG). This position statement outlines the potential opportunities and limitations of integrating artificial intelligence (AI) into the practice of sleep medicine. Additionally, although the most apparent and immediate application of AI in our field is the assisted scoring of PSG, we propose potential clinical use cases that transcend the sleep laboratory and are expected to deepen our understanding of sleep disorders, improve patient-centered sleep care, augment day-to-day clinical operations, and increase our knowledge of the role of sleep in health at a population level., (© 2020 American Academy of Sleep Medicine.)