Transparent artificial intelligence-enabled interpretable and interactive sleep apnea assessment across flexible monitoring scenarios

Shuaicong Hu, Jian Liu, Yanan Wang, Cong Fu, Jichu Zhu, Huan Yu () and Cuiwei Yang ()
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Shuaicong Hu: Fudan University
Jian Liu: Fudan University
Yanan Wang: Fudan University
Cong Fu: Sleep and Wake Disorders Center of Fudan University
Jichu Zhu: Sleep and Wake Disorders Center of Fudan University
Huan Yu: Sleep and Wake Disorders Center of Fudan University
Cuiwei Yang: Fudan University

Nature Communications, 2025, vol. 16, issue 1, 1-21

Abstract: Abstract Early detection of widespread undiagnosed sleep apnea is crucial for preventing its severe health complications. However, large-scale diagnosis faces inaccessible monitoring and trust barriers in automated analysis, particularly due to the absence of transparent artificial intelligence frameworks capable of monitoring adaptation. Here, we develop Apnea Interact Xplainer, a transparent system enabling sleep apnea diagnosis through flexible channel analysis across clinical and home settings. Analyzing 15,807 polysomnography recordings from seven independent multi-ethnic cohorts, our system achieves accuracies of 0.738-0.810 for four-level severity classification, with 99.8% accuracy within one severity grade and R-squared of 0.92-0.96 for apnea-hypopnea index prediction on external test cohorts. The system provides multi-level expert-logic interpretable visualization of respiratory patterns enabling transparent collaborative decision-making. Notably, it achieves a sensitivity of 0.970 for early sleep apnea detection using only oximetry signals, while providing nightly risk assessment and intelligent monitoring reports. This study establishes a paradigm shift in advancing early and cost-effective sleep apnea diagnosis through transparent artificial intelligence.

Date: 2025
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DOI: 10.1038/s41467-025-62864-x

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