Artificial Intelligence in Sleep Apnea

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Artificial Intelligence in Sleep Apnea
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Ritwick Agrawal, MD, MS, FCCP

Click to view more from Pulmonology Data Trends 2024. 

References

  1. Benjafield AV, Ayas NT, Eastwood PR, et al. Estimation of the global prevalence and burden of obstructive sleep apnoea: a literature-based analysis. Lancet Respir Med. 2019;7(8):687-698. doi:10.1016/S2213-2600(19)30198-5 

  1. Peppard PE, Young T, Barnet JH, Palta M, Hagen EW, Hia KM. Increased prevalence of sleep-disordered breathing in adults. Am J Epidemiol. 2013;177(9):1006-1014. doi:10.1093/aje/kws342 

  1. Nag DS, Swain A, Sahu S, Chatterjee A, Swain BP. Relevance of sleep for wellness: new trends in using artificial intelligence and machine learning. World J Clin Cases. 2024;12(7):1196-1199. doi:10.12998/wjcc.v12.i7.1196 

  1. Duarte M, Pereira-Rodrigues P, Ferreira-Santos D. The role of novel digital clinical tools in the screening or diagnosis of obstructive sleep apnea: systematic review. J Med Internet Res. 2023;25:e47735. doi:10.2196/47735 

  1. Bandyopadhyay A, Goldstein C. Clinical applications of artificial intelligence in sleep medicine: a sleep clinician's perspective. Sleep Breath. 2023;27(1):39-55. doi:10.1007/s11325-022-02592-4 

  1. Verma RK, Dhillon G, Grewal H, et al. Artificial intelligence in sleep medicine: present and future. World J Clin Cases. 2023;11(34):8106-8110. doi:10.12998/wjcc.v11.i34.8106 

  1. Brennan HL, Kirby SD. The role of artificial intelligence in the treatment of obstructive sleep apnea. J Otolaryngol Head Neck Surg. 2023;52(1):7. doi:10.1186/s40463-023-00621-0 

  1. Chung TT, Lee MT, Ku MC, Yang KC, Wei CY. Efficacy of a smart antisnore pillow in patients with obstructive sleep apnea syndrome. Behav Neurol. 2021;2021:8824011. doi:10.1155/2021/8824011 

  1. Rusk S, Nygate YN, Fernandez C, et al. 0463 Deep learning classification of future PAP adherence based on CMS and other adherence criteria. Sleep. 2023;46(suppl 1):A206. doi:10.1093/sleep/zsad077.0463  

Author and Disclosure Information

Ritwick Agrawal, MD, MS, FCCP
Director, Sleep Medicine
Huntington Hospital
Northwell Health
Huntington, NY

Dr. Agrawal has no relevant financial disclosures.

Publications
MDedge Internal Medicine
CHEST Physician
Topics
Sleep Medicine
Author(s)
Ritwick Agrawal, MD, MS, FCCP
Author(s)
Ritwick Agrawal, MD, MS, FCCP
Author and Disclosure Information

Ritwick Agrawal, MD, MS, FCCP
Director, Sleep Medicine
Huntington Hospital
Northwell Health
Huntington, NY

Dr. Agrawal has no relevant financial disclosures.

Author and Disclosure Information

Ritwick Agrawal, MD, MS, FCCP
Director, Sleep Medicine
Huntington Hospital
Northwell Health
Huntington, NY

Dr. Agrawal has no relevant financial disclosures.

Click to view more from Pulmonology Data Trends 2024. 

Click to view more from Pulmonology Data Trends 2024. 

References

  1. Benjafield AV, Ayas NT, Eastwood PR, et al. Estimation of the global prevalence and burden of obstructive sleep apnoea: a literature-based analysis. Lancet Respir Med. 2019;7(8):687-698. doi:10.1016/S2213-2600(19)30198-5 

  1. Peppard PE, Young T, Barnet JH, Palta M, Hagen EW, Hia KM. Increased prevalence of sleep-disordered breathing in adults. Am J Epidemiol. 2013;177(9):1006-1014. doi:10.1093/aje/kws342 

  1. Nag DS, Swain A, Sahu S, Chatterjee A, Swain BP. Relevance of sleep for wellness: new trends in using artificial intelligence and machine learning. World J Clin Cases. 2024;12(7):1196-1199. doi:10.12998/wjcc.v12.i7.1196 

  1. Duarte M, Pereira-Rodrigues P, Ferreira-Santos D. The role of novel digital clinical tools in the screening or diagnosis of obstructive sleep apnea: systematic review. J Med Internet Res. 2023;25:e47735. doi:10.2196/47735 

  1. Bandyopadhyay A, Goldstein C. Clinical applications of artificial intelligence in sleep medicine: a sleep clinician's perspective. Sleep Breath. 2023;27(1):39-55. doi:10.1007/s11325-022-02592-4 

  1. Verma RK, Dhillon G, Grewal H, et al. Artificial intelligence in sleep medicine: present and future. World J Clin Cases. 2023;11(34):8106-8110. doi:10.12998/wjcc.v11.i34.8106 

  1. Brennan HL, Kirby SD. The role of artificial intelligence in the treatment of obstructive sleep apnea. J Otolaryngol Head Neck Surg. 2023;52(1):7. doi:10.1186/s40463-023-00621-0 

  1. Chung TT, Lee MT, Ku MC, Yang KC, Wei CY. Efficacy of a smart antisnore pillow in patients with obstructive sleep apnea syndrome. Behav Neurol. 2021;2021:8824011. doi:10.1155/2021/8824011 

  1. Rusk S, Nygate YN, Fernandez C, et al. 0463 Deep learning classification of future PAP adherence based on CMS and other adherence criteria. Sleep. 2023;46(suppl 1):A206. doi:10.1093/sleep/zsad077.0463  

References

  1. Benjafield AV, Ayas NT, Eastwood PR, et al. Estimation of the global prevalence and burden of obstructive sleep apnoea: a literature-based analysis. Lancet Respir Med. 2019;7(8):687-698. doi:10.1016/S2213-2600(19)30198-5 

  1. Peppard PE, Young T, Barnet JH, Palta M, Hagen EW, Hia KM. Increased prevalence of sleep-disordered breathing in adults. Am J Epidemiol. 2013;177(9):1006-1014. doi:10.1093/aje/kws342 

  1. Nag DS, Swain A, Sahu S, Chatterjee A, Swain BP. Relevance of sleep for wellness: new trends in using artificial intelligence and machine learning. World J Clin Cases. 2024;12(7):1196-1199. doi:10.12998/wjcc.v12.i7.1196 

  1. Duarte M, Pereira-Rodrigues P, Ferreira-Santos D. The role of novel digital clinical tools in the screening or diagnosis of obstructive sleep apnea: systematic review. J Med Internet Res. 2023;25:e47735. doi:10.2196/47735 

  1. Bandyopadhyay A, Goldstein C. Clinical applications of artificial intelligence in sleep medicine: a sleep clinician's perspective. Sleep Breath. 2023;27(1):39-55. doi:10.1007/s11325-022-02592-4 

  1. Verma RK, Dhillon G, Grewal H, et al. Artificial intelligence in sleep medicine: present and future. World J Clin Cases. 2023;11(34):8106-8110. doi:10.12998/wjcc.v11.i34.8106 

  1. Brennan HL, Kirby SD. The role of artificial intelligence in the treatment of obstructive sleep apnea. J Otolaryngol Head Neck Surg. 2023;52(1):7. doi:10.1186/s40463-023-00621-0 

  1. Chung TT, Lee MT, Ku MC, Yang KC, Wei CY. Efficacy of a smart antisnore pillow in patients with obstructive sleep apnea syndrome. Behav Neurol. 2021;2021:8824011. doi:10.1155/2021/8824011 

  1. Rusk S, Nygate YN, Fernandez C, et al. 0463 Deep learning classification of future PAP adherence based on CMS and other adherence criteria. Sleep. 2023;46(suppl 1):A206. doi:10.1093/sleep/zsad077.0463  

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OSA disrupts the lives of nearly 1 billion adults globally due to recurrent episodes of upper airway obstruction during sleep.1 This condition can lead to severe  cardiovascular issues, cognitive impairments, and decreased quality of life.2 Despite the prevalence of OSA, underdiagnosis and undertreatment are significant challenges, exacerbated by the limitations of the current gold-standard diagnostic method, overnight polysomnography. This method is resource-intensive, expensive, and often inaccessible due to high demand in sleep laboratories.3,4

Artificial intelligence (AI) has the potential to revolutionize the field of sleep medicine, particularly in the management and diagnosis of sleep disorders such as OSA. AI applications in sleep medicine extend from automating sleep stage scoring with neural networks to enhancing the understanding of sleep disorder  pathophysiology through machine learning (ML) models.5,6 By analyzing patterns in large-scale data, AI has helped identify various OSA endotypes, as well as predict continuous positive airway pressure (CPAP) adherence patterns and surgical success rates, which can influence clinical decision-making.5,7 Paired with the portability and unobtrusiveness of most AI-based devices, these technologies could offer both effective and convenient treatment alternatives for patients.

However, the integration of AI into clinical practice comes with challenges, including the need for standardized validation of AI algorithms, the creation of representative and comprehensive training datasets, and the security and privacy of health data. Furthermore, addressing disparities in AI application and ensuring equitable health outcomes are crucial steps as this technology becomes more ubiquitous in sleep medicine.5,6

While AI presents promising advancements in understanding and managing OSA, careful consideration and implementation are required to realize its full potential in clinical settings, ensuring that all patients benefit from this technological evolution in health care.

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