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Awake speech recordings for machine learning diagnosis of obstructive sleep apnea: a Bayesian meta-analysis
Esther Yanxin Gao1,2,3, Yunrui Hao1, Nicole Kye Wen Tan1
1Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
Summary
Artificial Intelligence (AI) speech analysis shows promising accuracy for screening obstructive sleep apnea (OSA). This AI tool, using awake speech, offers a practical, scalable method for diagnosing OSA, a common but often overlooked condition.
Area of Science:
- Medical Technology
- Artificial Intelligence
- Sleep Medicine
Background:
- Obstructive sleep apnea (OSA) is a widespread condition with significant health implications, frequently underdiagnosed.
- Polysomnography (PSG), the standard diagnostic tool, has limited accessibility.
- AI-based speech analysis emerges as a potential non-invasive alternative for OSA screening.
Purpose of the Study:
- To evaluate the diagnostic accuracy of AI models analyzing awake speech for obstructive sleep apnea.
- To identify factors influencing the performance of AI speech analysis in OSA detection.
Main Methods:
- A systematic literature search was conducted across major scientific databases.
- Bayesian bivariate meta-analysis and meta-regression were employed to synthesize data from eligible studies.
- Risk of bias and evidence quality were assessed using QUADAS-2 and GRADE frameworks.
Main Results:
- Eight studies involving 24 AI models were included, analyzing data from 1,060 participants.
- AI models analyzing awake speech achieved a pooled sensitivity of 82.9% and specificity of 83.3%.
- Higher mean participant age correlated with improved sensitivity; other factors like OSA severity did not significantly impact performance.
Conclusions:
- AI models trained on awake speech recordings demonstrate robust diagnostic accuracy for OSA.
- These AI tools present a viable, scalable, and practical screening solution for obstructive sleep apnea in various settings.
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