European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2025
Multichannel machine learning for polysomnographic diagnosis of obstructive sleep apnea: a Bayesian meta-analysis.
СтатьяМетаанализСистематический обзорНазвание и аннотация: Перевод пока не подготовлен
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Rani Shahana, Gao Esther Yanxin, Ong Joel Zuo Er, Tan Nicole Kye Wen, Ng Adele Chin Wei, Leong Zhou Hao, Phua Chu Qin, Ong Thun How, Leow Leong Chai, Huang Guang-Bin, Tan Benjamin Kye Jyn, Toh Song Tar
Аннотация
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.
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.