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Self-helped detection of obstructive sleep apnea based on automated facial recognition and machine learning

  • Sleep Breathing Physiology and Disorders • Original Article
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Abstract

Purpose

The diagnosis of obstructive sleep apnea (OSA) relies on time-consuming and complicated procedures which are not always readily available and may delay diagnosis. With the widespread use of artificial intelligence, we presumed that the combination of simple clinical information and imaging recognition based on facial photos may be a useful tool to screen for OSA.

Methods

We recruited consecutive subjects suspected of OSA who had received sleep examination and photographing. Sixty-eight points from 2-dimensional facial photos were labelled by automated identification. An optimized model with facial features and basic clinical information was established and tenfold cross-validation was performed. Area under the receiver operating characteristic curve (AUC) indicated the model’s performance using sleep monitoring as the reference standard.

Results

A total of 653 subjects (77.2% males, 55.3% OSA) were analyzed. CATBOOST was the most suitable algorithm for OSA classification with a sensitivity, specificity, accuracy, and AUC of 0.75, 0.66, 0.71, and 0.76 respectively (P < 0.05), which was better than STOP-Bang questionnaire, NoSAS scores, and Epworth scale. Witnessed apnea by sleep partner was the most powerful variable, followed by body mass index, neck circumference, facial parameters, and hypertension. The model’s performance became more robust with a sensitivity of 0.94, for patients with frequent supine sleep apnea.

Conclusion

The findings suggest that craniofacial features extracted from 2-dimensional frontal photos, especially in the mandibular segment, have the potential to become predictors of OSA in the Chinese population. Machine learning–derived automatic recognition may facilitate the self-help screening for OSA in a quick, radiation-free, and repeatable manner.

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Data availability

The datasets generated from original facial photos in the current study are available from the corresponding author on reasonable request.

Abbreviations

AHI:

Apnea-hypopnea index

AUC:

Area under curve

BMI:

Body mass index

OSA:

Obstructive sleep apnea

PM:

Portable monitoring

PSG:

Polysomnography

ROC:

Receiver operating characteristic curve

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Acknowledgements

All authors gave final approval of the version to be published and agreed to be accountable for all aspects of the work.

Funding

This study was supported by the National Key Research and Development Program of China (No. 2020YFC2003600). The sponsor had no role in the design or conduct of this research.

Author information

Author notes

  1. Qi Chen and Zhe Liang contributed equally to this work.

Authors and Affiliations

  1. Sleep Medical Center, Beijing Anzhen Hospital, Capital Medical University, Beijing, China

    Qi Chen, Chenyao Ma, Hu Liu, Fei Xie & Fang Fang

  2. Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China

    Zhe Liang

  3. Department of Automation, Tsinghua University, Beijing, China

    Qing Wang

  4. Pharmacovigilance Research Center for Information Technology and Data Science, Cross-Strait Tsinghua Research Institute, Xiamen, China

    Qing Wang

  5. Beijing Institute of Heart Lung and Blood Vessel Diseases, Beijing Anzhen Hospital, Capital Medical University, Beijing, China

    Chenyao Ma, John E. Sanderson, Hongfeng Jiang & Fang Fang

  6. School of Software Engineering, Faculty of Information Technology, Beijing University of Technology, Beijing, China

    Yi Lei

  7. Automation School, Beijing University of Posts and Telecommunications, Beijing, China

    Xu Hu & Weihao Lin

Authors
  1. Qi Chen
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  2. Zhe Liang
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  3. Qing Wang
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  7. Xu Hu
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  9. Hu Liu
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  11. Hongfeng Jiang
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  12. Fang Fang
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Corresponding authors

Correspondence to Hongfeng Jiang or Fang Fang.

Ethics declarations

Ethics approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the Ethics Committee of Beijing Anzhen Hospital and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Conflict of interest

The authors declare no competing interests.

Study registration

This study was registered in the Chinese Clinical Trial Registry (No. ChiCTR-ROC-17011027).

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Chen, Q., Liang, Z., Wang, Q. et al. Self-helped detection of obstructive sleep apnea based on automated facial recognition and machine learning. Sleep Breath 27, 2379–2388 (2023). https://doi.org/10.1007/s11325-023-02846-9

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  • DOI: https://doi.org/10.1007/s11325-023-02846-9

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