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Review
. 2023 Jul 1;19(7):1337-1363.
doi: 10.5664/jcsm.10532.

Application of artificial intelligence in the diagnosis of sleep apnea

George Bazoukis  1   2 , Sandeep Chandra Bollepalli  3 , Cheuk To Chung  4 , Xinmu Li  5 , Gary Tse  4   6 , Bethany L Bartley  7 , Salma Batool-Anwar  7 , Stuart F Quan  7   8 , Antonis A Armoundas  3   9
Affiliations
Review

Application of artificial intelligence in the diagnosis of sleep apnea

George Bazoukis et al. J Clin Sleep Med. .

Abstract

Study objectives: Machine learning (ML) models have been employed in the setting of sleep disorders. This review aims to summarize the existing data about the role of ML techniques in the diagnosis, classification, and treatment of sleep-related breathing disorders.

Methods: A systematic search in Medline, EMBASE, and Cochrane databases through January 2022 was performed.

Results: Our search strategy revealed 132 studies that were included in the systematic review. Existing data show that ML models have been successfully used for diagnostic purposes. Specifically, ML models showed good performance in diagnosing sleep apnea using easily obtained features from the electrocardiogram, pulse oximetry, and sound signals. Similarly, ML showed good performance for the classification of sleep apnea into obstructive and central categories, as well as predicting apnea severity. Existing data show promising results for the ML-based guided treatment of sleep apnea. Specifically, the prediction of outcomes following surgical treatment and optimization of continuous positive airway pressure therapy can be guided by ML models.

Conclusions: The adoption and implementation of ML in the field of sleep-related breathing disorders is promising. Advancements in wearable sensor technology and ML models can help clinicians predict, diagnose, and classify sleep apnea more accurately and efficiently.

Citation: Bazoukis G, Bollepalli SC, Chung CT, et al. Application of artificial intelligence in the diagnosis of sleep apnea. J Clin Sleep Med. 2023;19(7):1337-1363.

Keywords: artificial intelligence; machine learning; sleep apnea.

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Conflict of interest statement

All authors have seen and approved this manuscript. The work was supported by the Institute of Precision Medicine (17UNPG33840017) from the American Heart Association; the RICBAC Foundation; and National Institutes of Health Grants 1 R01 HL135335-01, 1 R21 HL137870-01, 1 R21EB026164-01, 3R21EB026164-02S1, and 1 R01 HL161008-01. Dr. Quan was partially supported by National Institutes of Health Grant R21 HL159661. The authors report no conflicts of interest.

Figures

Figure 1
Figure 1. Flowchart of the search strategy.
Figure 2
Figure 2. A machine learning pipeline.
An iterative process to obtain an optimal model using training and validation data is displayed; finally, the optimal model is evaluated on unseen test data.
See this image and copyright information in PMC

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