Machine Listening for OSA Diagnosis

作者：Benjamin Kye Jyn Tan, Esther Yanxin Gao, Nicole Kye Wen Tan, Brian Sheng Yep Yeo, Claire Jing‐Wen Tan, Adele Chin Wei Ng, Zhou Hao Leong, Chu Qin Phua, Maythad Uataya, Liang Chye Goh, Thun How Ong, Leong Chai Leow, Guang-Bin Huang, Song Tar Toh · 发表于：CHEST Journal · 年份：2025 · DOI：10.1016/j.chest.2025.04.006 · 被引用次数：10 · 研究领域：Obstructive Sleep Apnea Research、Phonocardiography and Auscultation Techniques、Cardiovascular and Diving-Related Complications

BACKGROUND: Among 1 billion patients worldwide with OSA, 90% remain undiagnosed. The main barrier to diagnosis is the overnight polysomnogram, which requires specialized equipment, skilled technicians, and inpatient beds available only in tertiary sleep centers. Recent advances in artificial intelligence (AI) have enabled OSA detection using breathing sound recordings. RESEARCH QUESTION: What is the diagnostic accuracy of and how can we optimize machine listening for OSA? STUDY DESIGN AND METHODS: PubMed, Embase, Scopus, Web of Science, and IEEE Xplore databases were systematically searched. Two masked reviewers selected studies comparing the patient-level diagnostic performance of AI approaches using overnight audio recordings vs conventional diagnosis (apnea-hypopnea index) using a train-test split or k-fold cross-validation. Bayesian bivariate meta-analysis and meta-regression were performed. Publication bias was assessed by using a selection model. Risk of bias and evidence quality were assessed by using the Quality Assessment of Diagnostic Accuracy Studies-2 and the Grading of Recommendations, Assessment, Development, and Evaluation tools. RESULTS: From 6,254 records, 16 studies (41 models) trained on 4,864 participants and tested on 2,370 participants were included. No study had a high risk of bias. Machine listening achieved a pooled sensitivity (95% credible interval) of 90.3% (86.9%-93.1%), a specificity of 86.7% (83.1%-89.7%), a diagnostic OR of 60.8 (39.4-99.9), an...