Laila Khalil

Background: Worldwide, 30%-45% of adults have sleep disorders, which are linked to major health issues such as diabetes and cardiovascular disease. Long-term monitoring with traditional in-lab testing is impractical due to high costs. Wearable artificial intelligence (AI)–powered solutions offer accessible, scalable, and continuous monitoring, improving the identification and treatment of sleep problems. Objective: This scoping review aims to provide an overview of AI-powered wearable devices used for sleep disorders, focusing on study characteristics, wearable technology features, and AI methodologies for detection and analysis. Methods: Seven electronic databases (MEDLINE, PsycINFO, Embase, IEEE Xplore, ACM Digital Library, Google Scholar, and Scopus) were searched for peer-reviewed literature published before March 2024. Keywords were selected based on 3 domains: sleep disorders, AI, and wearable devices. The primary selection criterion was the inclusion of studies that utilized AI algorithms to detect or predict various sleep disorders using data from wearable devices. Study selection was conducted in 2 steps: first, by reviewing titles and abstracts, followed by full-text screening. Two reviewers independently conducted study selection and data extraction, resolving discrepancies by consensus. The extracted data were synthesized using a narrative approach. Results: The initial search yielded 615 articles, of which 46 met the eligibility criteria and were included in the final analysis. The majority of studies focused on sleep apnea. Wearable AI was widely deployed for diagnosing and screening disorders; however, none of the studies used it for treatment. Commercial devices were the most commonly used type of wearable technology, appearing in 30 out of 46 (65%) studies. Among these, various brands were utilized rather than a single large, well-known brand; 19 (41%) studies used wrist-worn devices. Respiratory data were used by 25 of 46 (54%) studies as the primary data for model development, followed by heart rate (22/46, 48%) and body movement (17/46, 37%). The most popular algorithm was the convolutional neural network, adopted by 17 of 46 (37%) studies, followed by random forest (14/46, 30%) and support vector machines (12/46, 26%). Conclusions: Wearable AI technology offers promising solutions for sleep disorders. These devices can be used for screening and diagnosis; however, research on wearable technology for sleep disorders other than sleep apnea remains limited. To statistically synthesize performance and efficacy results, more reviews are needed. Technology companies should prioritize advancements such as deep learning algorithms and invest in wearable AI for treating sleep disorders, given its potential. Further research is necessary to validate machine learning techniques using clinical data from wearable devices and to develop useful analytics for data collection, monitoring, prediction, classification, and recommendation in the context of sleep disorders.

BACKGROUND Worldwide, 30%-45% of adults have sleep disorders, which are linked to major health issues such as diabetes and cardiovascular disease. Long-term monitoring with traditional in-lab testing is impractical due to high costs. Wearable artificial intelligence (AI)–powered solutions offer accessible, scalable, and continuous monitoring, improving the identification and treatment of sleep problems. OBJECTIVE This scoping review aims to provide an overview of AI-powered wearable devices used for sleep disorders, focusing on study characteristics, wearable technology features, and AI methodologies for detection and analysis. METHODS Seven electronic databases (MEDLINE, PsycINFO, Embase, IEEE Xplore, ACM Digital Library, Google Scholar, and Scopus) were searched for peer-reviewed literature published before March 2024. Keywords were selected based on 3 domains: sleep disorders, AI, and wearable devices. The primary selection criterion was the inclusion of studies that utilized AI algorithms to detect or predict various sleep disorders using data from wearable devices. Study selection was conducted in 2 steps: first, by reviewing titles and abstracts, followed by full-text screening. Two reviewers independently conducted study selection and data extraction, resolving discrepancies by consensus. The extracted data were synthesized using a narrative approach. RESULTS The initial search yielded 615 articles, of which 46 met the eligibility criteria and were included in the final analysis. The majority of studies focused on sleep apnea. Wearable AI was widely deployed for diagnosing and screening disorders; however, none of the studies used it for treatment. Commercial devices were the most commonly used type of wearable technology, appearing in 30 out of 46 (65%) studies. Among these, various brands were utilized rather than a single large, well-known brand; 19 (41%) studies used wrist-worn devices. Respiratory data were used by 25 of 46 (54%) studies as the primary data for model development, followed by heart rate (22/46, 48%) and body movement (17/46, 37%). The most popular algorithm was the convolutional neural network, adopted by 17 of 46 (37%) studies, followed by random forest (14/46, 30%) and support vector machines (12/46, 26%). CONCLUSIONS Wearable AI technology offers promising solutions for sleep disorders. These devices can be used for screening and diagnosis; however, research on wearable technology for sleep disorders other than sleep apnea remains limited. To statistically synthesize performance and efficacy results, more reviews are needed. Technology companies should prioritize advancements such as deep learning algorithms and invest in wearable AI for treating sleep disorders, given its potential. Further research is necessary to validate machine learning techniques using clinical data from wearable devices and to develop useful analytics for data collection, monitoring, prediction, classification, and recommendation in the context of sleep disorders.

Objectives: The purpose of this study is to assess the efficacy, short- and long-term cardiovascular and non-cardiovascular mortalities and postoperative morbidities of surgical pulmonary embolectomy (SPE) for patients with massive or submassive pulmonary embolism. Methods: A comprehensive literature review was performed to identify articles reporting SPE for pulmonary embolism. The outcomes included in-hospital and long-term mortality in addition to postoperative morbidities. The random effect inverse variance method was used. Cumulative meta-analysis, leave-one-out sensitivity analysis, subgroup analysis and meta-regression were performed. Results: Among the 1949 searched studies in our systematic literature search, 78 studies met our inclusion criteria, including 6859 cases. The mean age ranged from 42 to 65 years. The percentage of males ranged from 25.6% to 86.7%. The median rate of preoperative cardiac arrest was 27.6%. The percentage of contraindications to preoperative systemic thrombolysis was 30.4%. The preoperative systemic thrombolysis use was 11.5%. The in-hospital mortality was estimated to be 21.96% (95% CI: 19.21–24.98); in-hospital mortality from direct cardiovascular causes was estimated to be 16.05% (95% CI: 12.95–19.73). With a weighted median follow-up of 3.05 years, the late cardiovascular and non-cardiovascular mortality incidence rates were 0.39 and 0.90 per person-year, respectively. The incidence of pulmonary bleeding, gastrointestinal bleeding, surgical site bleeding, non-surgical site bleeding and wound complications was 0.62%, 4.70%, 4.84%, 5.80% and 7.2%, respectively. Cumulative meta-analysis showed a decline in hospital mortality for SPE from 42.86% in 1965 to 20.56% in 2024. Meta-regression revealed that the publication year and male sex were associated with lower in-hospital mortality, while preoperative cardiac arrest, the need for inotropes or vasopressors and preoperative mechanical ventilation were associated with higher in-hospital mortality. Conclusions: This study demonstrates acceptable perioperative mortality rates and late cardiovascular and non-cardiovascular mortality in patients who undergo SPE for massive or submassive pulmonary embolism.