Accuracy of Artificial Intelligence in Diagnosing Obstructive Sleep Apnea Using Photoplethysmography: Systematic Review and Meta-Analysis - Report - MDSpire
Clinical Report: Evaluating the Precision of AI in Diagnosing OSA Through PPG
Overview
This systematic review and meta-analysis evaluates the diagnostic accuracy of AI models utilizing photoplethysmography (PPG) for detecting obstructive sleep apnea (OSA).
Background
Obstructive sleep apnea (OSA) affects approximately 1 billion people globally, leading to significant health risks if left undiagnosed. Traditional diagnostic methods, such as polysomnography, are often limited by cost and accessibility.
Data Highlights
No numerical data was provided in the source material.
Key Findings
AI models trained on PPG data have shown promise in diagnosing OSA.
PPG is a noninvasive and cost-effective method for measuring blood oxygen saturation.
Existing studies report varying accuracy levels for AI-based OSA diagnosis using PPG.
Comprehensive evaluation of AI models for OSA diagnosis is timely and clinically relevant.
Clinical Implications
Clinicians should remain informed about advancements in AI technologies in sleep medicine.
Conclusion
This meta-analysis highlights the need for systematic evaluation of AI models in diagnosing OSA using PPG.
by Brian Sheng Yep Yeo, Esther Yanxin Gao, Joan Ern Xin Tan, Jun Yuan Koo, Yi Siang Lee, Nicole Kye Wen Tan, Adele Chin Wei Ng, Zhou Hao Leong, Thun How Ong, Leong Chai Leow, Guang-Bin Huang, Benjamin Kye Jyn Tan, Song Tar Toh
