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Updated: Jan 11, 2026
Multi-Modal Home Sleep Monitoring in Older Adults
Published on: January 26, 2019
Multichannel machine learning for polysomnographic diagnosis of obstructive sleep apnea: a Bayesian meta-analysis
Shahana Rani1, Esther Yanxin Gao1,2,3, Joel Zuo Er Ong1
1Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
Background:
Obstructive sleep apnea (OSA) affects 1 billion people globally, yet over 80% remain undiagnosed. The gold standard for diagnosis, overnight polysomnography, is highly accurate but labor-intensive, leading to delays and increased healthcare burden. Artificial intelligence (AI) models offer a promising alternative. This study pools existing evidence to evaluate AI-based OSA diagnostics.
Methods:
A systematic search of PubMed, Embase, Scopus, Web of Science, and IEEE Xplore identified studies comparing AI models against the apnea-hypopnea index (AHI) for OSA diagnosis. Studies evaluating models using random-split test sets or k-fold cross-validation were included in a Bayesian bivariate meta-analysis and meta-regression. Risk of bias and evidence quality were assessed using QUADAS-2 and GRADE.
Results:
From 6,254 records, 7 studies with 19 AI models trained and tested on 7,547 and 7,471 participants were included. No study had a high risk of bias. AI achieved a pooled sensitivity of 89.2% (95% CrI: 81.5-94.3%) and specificity of 87.1% (95% CrI: 82.6-90.8%). Neural networks (NNs) were the best-performing AI model compared to the other subtypes, achieving a sensitivity of 92.8% (95% CrI: 84.8-96.6%) and specificity of 87.8% (95% CrI: 81.1-92.6%). Age and sex had no effect. No publication bias was detected, and the evidence was of high quality.
Conclusion:
Neural Networks AI models trained on polysomnography demonstrated excellent diagnostic accuracy in diagnosing OSA as compared to traditional machine learning. There is a need for further exploration and external validation of AI models to support their integration into routine sleep medicine practice, hence improving access to efficient and accurate OSA diagnosis. PROSPERO registration: CRD42024534235.
