Computer-assisted Diagnosis of Obstructive Sleep Apnea in Adults: A Narrative Review - 09/01/26

Doi : 10.1016/j.irbm.2026.100928

Ying Jiang ^a, Huiping Luo ^b, Mrzayan Yerbolat ^a, Jun Tao ^a
^a Fudan University, Shanghai, People's Republic of China
^b Fudan University Eye Ear Nose and Throat Hospital, Shanghai, People's Republic of China

Sous presse. Manuscrit accepté. Disponible en ligne depuis le Friday 09 January 2026

Abstract

Obstructive Sleep Apnea (OSA) is the most commonly diagnosed sleep-related breathing disorder, affecting over one billion individuals globally. While polysomnography (PSG) remains the gold standard for diagnosis, it is resource-intensive and limited in accessibility. Consequently, the development of automated diagnostic systems has emerged as a vital research area, particularly those leveraging machine learning (ML) and deep learning (DL) techniques.

Objectives

This narrative review aims to provide a comprehensive comparison of ML and DL-based methods for computer-assisted diagnosis of OSA in adults. The review emphasizes model architectures, performance metrics, application scenarios, and real-world deployment challenges. Special attention is given to advanced DL architectures—such as hybrid models and Transformers—as well as the potential role of wearable technologies in scalable diagnosis.

Material and methods

The literature search was conducted using Web of Science, IEEE Xplore, and PubMed to identify peer-reviewed articles published between 2008 and 2024. Search terms included Obstructive Sleep Apnea (OSA), k-Nearest Neighbor (k-NN), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Logistic Regression (LR), Ensemble Algorithm, Artificial Neural Network (ANN), Convolution Neural Network (CNN), Recurrent Neural Network (RNN), Deep Belief Network (DBN), hybrid neural network, and Transformers.

Results

DL-based models have demonstrated superior performance over conventional ML approaches, particularly in their ability to perform automated, hierarchical feature extraction and model complex physiological patterns. Hybrid and Transformer-based networks stand out for their diagnostic accuracy and scalability. However, most models remain limited to benchmark dataset validation and lack hardware-level implementation. Key challenges include data heterogeneity, poor model interpretability, and limited clinical generalizability.

Conclusion

DL-driven diagnostic frameworks—especially those incorporating multimodal signals and wearable data—represent the most promising direction for achieving accurate, scalable, and accessible OSA detection. Future research should prioritize clinical validation across diverse populations, integration of explainable AI techniques, and real-world deployment through user-centered design and IoT-based wearable platforms.

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Highlights

•	A structured comparison of ML and DL techniques for OSA diagnosis in adults.
•	Summary of 64 studies involving signal types and benchmark datasets.
•	Overview of DL models, including hybrid and Transformer-based approaches.
•	Analysis of wearable AI systems and clinical implementation challenges.
•	Discussion of interpretability, generalizability, and future directions.