Artificial intelligence in the diagnosis of obstructive sleep apnea: a scoping review.

PURPOSE

The gold standard diagnostic modality of Obstructive Sleep Apnea (OSA) is polysomnography (PSG), which is resource-intensive, requires specialized facilities, and may not be accessible to all patients. There is a growing body of research exploring the potential of artificial intelligence (AI) to offer more accessible, efficient, and cost-effective alternatives for the diagnosis of OSA.

METHODS

We conducted a scoping review of studies applying AI techniques to diagnose and assess OSA in adult populations. A comprehensive search was performed in the Web of Science database using terms related to "obstructive sleep apnea," "artificial intelligence," "machine learning," and related approaches.

RESULTS

A total of 344 articles met the inclusion criteria. The findings highlight various methodologies of disease evaluation, including binary classification distinguishing between OSA-positive and OSA-negative individuals in 118 articles, OSA event detection in 211 articles, severity evaluation in 38 articles, topographic diagnostic evaluation in 8 articles, and apnea-hypopnea index (AHI) estimation in 26 articles. 40 distinct types of data sources were identified. The three most prevalent data types were electrocardiography (ECG), used in 108 articles, photoplethysmography (PPG) in 62 articles, and respiratory effort and body movement in 44 articles. The AI techniques most frequently applied were convolutional neural networks (CNNs) in 104 articles, support vector machines (SVMs) in 91 articles, and K-Nearest Neighbors (KNN) in 57 articles. Of these studies, 229 used direct patient recruitment, and 115 utilized existing datasets.

CONCLUSION

While AI demonstrates substantial potential with high accuracy rates in certain studies, challenges remain such as model transparency, validation across diverse populations, and seamless integration into clinical practice. These challenges may stem from factors such as overfitting to specific datasets, limited generalizability, and the need for standardized protocols in clinical settings.