Accuracy of Artificial Intelligence in Diagnosing Obstructive Sleep Apnea Using Photoplethysmography: Systematic Review and Meta-Analysis - Takeaways - 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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  • 1

    Obstructive sleep apnea (OSA) affects approximately 1 billion people globally, necessitating effective diagnostic strategies.

  • 2

    Polysomnography (PSG) is the gold standard for diagnosing OSA, but its use is limited by high costs and logistical challenges.

  • 3

    Artificial intelligence (AI) is increasingly applied in medical diagnostics, including the evaluation of OSA through machine learning.

  • 4

    This systematic review and meta-analysis aims to assess the diagnostic accuracy of AI models using photoplethysmography (PPG) for OSA.

  • 5

    The study includes observational investigations of adults that utilize AI for OSA diagnosis, comparing results against established testing methods.

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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