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

• Published in: Medicina (Kaunas, Lithuania) Lithuania), 2022-11, Vol.58 (11), p.1574
• Main Authors: Aiyer, Ishan, Shaik, Likhita, Sheta, Alaa, Surani, Salim
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Analysis; Artificial intelligence; Automation; Care and treatment; Classification; Complications and side effects; Diagnosis; Health aspects; Ischemia; Machine learning; Methods; Neural networks; Questionnaires; Regression analysis; Review; screening; Sleep apnea; Sleep apnea syndromes; Support vector machines; Variables
• ISSN: 1648-9144; 1010-660X; 1648-9144
• DOI: 10.3390/medicina58111574

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.