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Review of Application of Machine Learning as a Screening Tool for Diagnosis of Obstructive Sleep Apnea

Obstructive sleep apnea syndrome (OSAS) is a pervasive disorder with an incidence estimated at 5–14 percent among adults aged 30–70 years. It carries significant morbidity and mortality risk from cardiovascular disease, including ischemic heart disease, atrial fibrillation, and cerebrovascular disea...

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Autores principales: Aiyer, Ishan, Shaik, Likhita, Sheta, Alaa, Surani, Salim
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9696886/
https://www.ncbi.nlm.nih.gov/pubmed/36363530
http://dx.doi.org/10.3390/medicina58111574
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author Aiyer, Ishan
Shaik, Likhita
Sheta, Alaa
Surani, Salim
author_facet Aiyer, Ishan
Shaik, Likhita
Sheta, Alaa
Surani, Salim
author_sort Aiyer, Ishan
collection PubMed
description Obstructive sleep apnea syndrome (OSAS) is a pervasive disorder with an incidence estimated at 5–14 percent among adults aged 30–70 years. It carries significant morbidity and mortality risk from cardiovascular disease, including ischemic heart disease, atrial fibrillation, and cerebrovascular disease, and risks related to excessive daytime sleepiness. The gold standard for diagnosis of OSAS is the polysomnography (PSG) test which requires overnight evaluation in a sleep laboratory and expensive infrastructure, which renders it unsuitable for mass screening and diagnosis. Alternatives such as home sleep testing need patients to wear diagnostic instruments overnight, but accuracy continues to be suboptimal while access continues to be a barrier for many. Hence, there is a continued significant underdiagnosis and under-recognition of sleep apnea in the community, with at least one study suggesting that 80–90% of middle-aged adults with moderate to severe sleep apnea remain undiagnosed. Recently, we have seen a surge in applications of artificial intelligence and neural networks in healthcare diagnostics. Several studies have attempted to examine its application in the diagnosis of OSAS. Signals included in data analytics include Electrocardiogram (ECG), photo-pletysmography (PPG), peripheral oxygen saturation (SpO2), and audio signals. A different approach is to study the application of machine learning to use demographic and standard clinical variables and physical findings to try and synthesize predictive models with high accuracy in assisting in the triage of high-risk patients for sleep testing. The current paper will review this latter approach and identify knowledge gaps that may serve as potential avenues for future research.
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spelling pubmed-96968862022-11-26 Review of Application of Machine Learning as a Screening Tool for Diagnosis of Obstructive Sleep Apnea Aiyer, Ishan Shaik, Likhita Sheta, Alaa Surani, Salim Medicina (Kaunas) Review Obstructive sleep apnea syndrome (OSAS) is a pervasive disorder with an incidence estimated at 5–14 percent among adults aged 30–70 years. It carries significant morbidity and mortality risk from cardiovascular disease, including ischemic heart disease, atrial fibrillation, and cerebrovascular disease, and risks related to excessive daytime sleepiness. The gold standard for diagnosis of OSAS is the polysomnography (PSG) test which requires overnight evaluation in a sleep laboratory and expensive infrastructure, which renders it unsuitable for mass screening and diagnosis. Alternatives such as home sleep testing need patients to wear diagnostic instruments overnight, but accuracy continues to be suboptimal while access continues to be a barrier for many. Hence, there is a continued significant underdiagnosis and under-recognition of sleep apnea in the community, with at least one study suggesting that 80–90% of middle-aged adults with moderate to severe sleep apnea remain undiagnosed. Recently, we have seen a surge in applications of artificial intelligence and neural networks in healthcare diagnostics. Several studies have attempted to examine its application in the diagnosis of OSAS. Signals included in data analytics include Electrocardiogram (ECG), photo-pletysmography (PPG), peripheral oxygen saturation (SpO2), and audio signals. A different approach is to study the application of machine learning to use demographic and standard clinical variables and physical findings to try and synthesize predictive models with high accuracy in assisting in the triage of high-risk patients for sleep testing. The current paper will review this latter approach and identify knowledge gaps that may serve as potential avenues for future research. MDPI 2022-11-01 /pmc/articles/PMC9696886/ /pubmed/36363530 http://dx.doi.org/10.3390/medicina58111574 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Aiyer, Ishan
Shaik, Likhita
Sheta, Alaa
Surani, Salim
Review of Application of Machine Learning as a Screening Tool for Diagnosis of Obstructive Sleep Apnea
title Review of Application of Machine Learning as a Screening Tool for Diagnosis of Obstructive Sleep Apnea
title_full Review of Application of Machine Learning as a Screening Tool for Diagnosis of Obstructive Sleep Apnea
title_fullStr Review of Application of Machine Learning as a Screening Tool for Diagnosis of Obstructive Sleep Apnea
title_full_unstemmed Review of Application of Machine Learning as a Screening Tool for Diagnosis of Obstructive Sleep Apnea
title_short Review of Application of Machine Learning as a Screening Tool for Diagnosis of Obstructive Sleep Apnea
title_sort review of application of machine learning as a screening tool for diagnosis of obstructive sleep apnea
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9696886/
https://www.ncbi.nlm.nih.gov/pubmed/36363530
http://dx.doi.org/10.3390/medicina58111574
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