Accuracy of Artificial Intelligence in Diagnosing Obstructive Sleep Apnea Using Photoplethysmography: Systematic Review and Meta-Analysis - Summary - MDSpire
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Evaluating the Precision of AI in Diagnosing Obstructive Sleep Apnea Through Photoplethysmography: A Systematic Review and Meta-Analysis

  • 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

  • September 25, 2026

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Objective:

To evaluate the diagnostic accuracy of AI models that use photoplethysmography (PPG) to detect obstructive sleep apnea (OSA).

Approach:
  • Study Design: A systematic review and meta-analysis registered with PROSPERO (CRD42024534235) following PRISMA guidelines.
  • Search Strategy: Systematic searches were conducted in PubMed, Embase, Scopus, Web of Science, and IEEE Xplore using terms related to OSA, AI, and PPG.
  • Study Selection: Eligible studies included observational investigations of adults addressing OSA diagnosis using AI with reference standards based on apnea-hypopnea index (AHI).
  • Data Extraction: Relevant information was extracted by independent authors and included variables such as publication year, sample size, and diagnostic accuracy data.
  • Statistical Analysis: A Bayesian bivariate random-effects model was utilized for analysis.

Original Source(s)

Accuracy of Artificial Intelligence in Diagnosing Obstructive Sleep Apnea Using Photoplethysmography: Systematic Review and Meta-Analysis

  • 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

  • September 25, 2026

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