The role of artificial intelligence in the treatment of obstructive sleep apnea

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Abstract

Background

The first-line and most common treatment for obstructive sleep apnea is nasal continuous positive airway pressure, which serves as a pneumatic splint to stabilize the upper airway and is effective when used with appropriate adherence. Continuous positive airway pressure compliance rates remain significantly low despite machine improvements and compliance intervention. Other treatment options include oral appliances, myofunctional therapy, and surgery. The aim of this project is to elucidate the role of artificial intelligence within improving the treatment of obstructive sleep apnea.

Methods

Related publications between 1999 and 2022 were reviewed from PubMed and Embase databases utilizing search terms “artificial intelligence,” “machine learning,” “obstructive sleep apnea,” and “treatment.” Both authors independently screened the results by title/abstract then by full text review. 126 non-duplicate articles were screened, 38 articles were included after title and abstract screen and 30 articles were included after full text review. The inclusion criteria are outline in the PICO framework and involved studies focused on artificial intelligence application in guiding and evaluating obstructive sleep apnea treatment. Non-English articles were excluded.

Results

The role of artificial intelligence in the treatment of OSA was categorized into the following sections: Predicting treatment outcomes of various treatment options, Improving/Evaluating treatment, and Personalizing treatment with improving understanding of underlying mechanisms of OSA.

Conclusions

Artificial intelligence has the capacity to improve the treatment of OSA through predicting outcomes of treatment options, evaluating the treatment the patient is currently utilizing and increasing understanding of the mechanisms that contribute to OSA disease process and physiology. Implementing AI in guiding treatment decisions allows patients to connect with treatment methods that would be most effective on an individual basis.

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Most cited references 40

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Trends in CPAP adherence over twenty years of data collection: a flattened curve

Brian Rotenberg, Dorian Murariu, Kenny Pang (2016)

Background Obstructive sleep apnea (OSA) is a common disorder, and continuous airway positive pressure (CPAP) is considered to be the gold standard of therapy. CPAP however is known to have problems with adherence, with many patients eventually abandoning the device. The purpose of this paper is to assess secular trends in CPAP adherence over the long term to see if there have been meaningful improvements in adherence in light of the multiple interventions proposed to do so. Methods A comprehensive systematic literature review was conducted using the Medline-Ovid, Embase, and Pubmed databases, searching for data regarding CPAP adherence over a twenty year timeframe (1994–2015). Data was assessed for quality and then extracted. The main outcome measure was reported CPAP non-adherence. Secondary outcomes included changes in CPAP non-adherence when comparing short versus long-term, and changes in terms of behavioral counseling. Results Eighty-two papers met study inclusion/exclusion criteria. The overall CPAP non-adherence rate based on a 7-h/night sleep time that was reported in studies conducted over the twenty year time frame was 34.1 %. There was no significant improvement over the time frame. Behavioral intervention improved adherence rates by ~1 h per night on average. Conclusions The rate of CPAP adherence remains persistently low over twenty years worth of reported data. No clinically significant improvement in CPAP adherence was seen even in recent years despite efforts toward behavioral intervention and patient coaching. This low rate of adherence is problematic, and calls into question the concept of CPAP as gold-standard of therapy for OSA.

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Obstructive sleep apnea: current perspectives

Amal M Osman, Sophie Carter, Jayne Carberry … (2018)

The prevalence of obstructive sleep apnea (OSA) continues to rise. So too do the health, safety, and economic consequences. On an individual level, the causes and consequences of OSA can vary substantially between patients. In recent years, four key contributors to OSA pathogenesis or “phenotypes” have been characterized. These include a narrow, crowded, or collapsible upper airway “anatomical compromise” and “non-anatomical” contributors such as ineffective pharyngeal dilator muscle function during sleep, a low threshold for arousal to airway narrowing during sleep, and unstable control of breathing (high loop gain). Each of these phenotypes is a target for therapy. This review summarizes the latest knowledge on the different contributors to OSA with a focus on measurement techniques including emerging clinical tools designed to facilitate translation of new cause-driven targeted approaches to treat OSA. The potential for some of the specific pathophysiological causes of OSA to drive some of the key symptoms and consequences of OSA is also highlighted.

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Phenotypes in obstructive sleep apnea: A definition, examples and evolution of approaches.

Andrey Zinchuk, Mark Gentry, John Concato … (2017)

Obstructive sleep apnea (OSA) is a complex and heterogeneous disorder and the apnea hypopnea index alone can not capture the diverse spectrum of the condition. Enhanced phenotyping can improve prognostication, patient selection for clinical trials, understanding of mechanisms, and personalized treatments. In OSA, multiple condition characteristics have been termed "phenotypes." To help classify patients into relevant prognostic and therapeutic categories, an OSA phenotype can be operationally defined as: "A category of patients with OSA distinguished from others by a single or combination of disease features, in relation to clinically meaningful attributes (symptoms, response to therapy, health outcomes, quality of life)." We review approaches to clinical phenotyping in OSA, citing examples of increasing analytic complexity. Although clinical feature based OSA phenotypes with significant prognostic and treatment implications have been identified (e.g., excessive daytime sleepiness OSA), many current categorizations lack association with meaningful outcomes. Recent work focused on pathophysiologic risk factors for OSA (e.g., arousal threshold, craniofacial morphology, chemoreflex sensitivity) appears to capture heterogeneity in OSA, but requires clinical validation. Lastly, we discuss the use of machine learning as a promising phenotyping strategy that can integrate multiple types of data (genomic, molecular, cellular, clinical) to identify unique, meaningful OSA phenotypes.

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Author and article information

Contributors

Hannah L. Brennan:

ORCID: http://orcid.org/0000-0003-1004-3170

hlb423@mun.ca

Simon D. Kirby: e35sdk@mun.ca

Journal

Journal ID (nlm-ta): J Otolaryngol Head Neck Surg

Journal ID (iso-abbrev): J Otolaryngol Head Neck Surg

Title: Journal of Otolaryngology - Head & Neck Surgery

Publisher: BioMed Central (London )

ISSN (Print): 1916-0208

ISSN (Electronic): 1916-0216

Publication date (Electronic): 7 February 2023

Publication date PMC-release: 7 February 2023

Publication date Collection: 2023

Volume: 52

Electronic Location Identifier: 7

Affiliations

GRID grid.25055.37, ISNI 0000 0000 9130 6822, Faculty of Medicine, , Memorial University of Newfoundland and Labrador, ; 98 Pearltown Rd, St. John’s, NL A1G 1P3 Canada

Author information

Hannah L. Brennan http://orcid.org/0000-0003-1004-3170

Article

Publisher ID: 621

DOI: 10.1186/s40463-023-00621-0

PMC ID: 9903572

PubMed ID: 36747273

SO-VID: 08a8378e-ab6f-44a4-910d-865a11884069

License:

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

History

Date received : 23 October 2022

Date accepted : 1 February 2023

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Keywords: obstructive sleep apnea,treatment,personalized treatment,artificial intelligence,machine learning

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Keywords: obstructive sleep apnea, treatment, personalized treatment, artificial intelligence, machine learning

The role of artificial intelligence in the treatment of obstructive sleep apnea

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Abstract

Background

Methods

Results

Conclusions

Related collections

Artificial Intelligence in Medicine

Most cited references 40

Trends in CPAP adherence over twenty years of data collection: a flattened curve

Obstructive sleep apnea: current perspectives

Phenotypes in obstructive sleep apnea: A definition, examples and evolution of approaches.

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