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

• Published in: Sleep & breathing Vol. 27; no. 6; pp. 2379 - 2388
• Main Authors: Chen, Qi, Liang, Zhe, Wang, Qing, Ma, Chenyao, Lei, Yi, Sanderson, John E., Hu, Xu, Lin, Weihao, Liu, Hu, Xie, Fei, Jiang, Hongfeng, Fang, Fang
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.12.2023; Springer Nature B.V
• Subjects: Apnea; Artificial intelligence; Automation; Body mass index; Dentistry; Diagnosis; Internal Medicine; Learning algorithms; Machine learning; Medicine; Medicine & Public Health; Neurology; Otorhinolaryngology; Pattern recognition; Pediatrics; Pneumology/Respiratory System; Sleep apnea; Sleep Breathing Physiology and Disorders • Original Article; Sleep disorders; Craniofacial feature; Obstructive sleep apnea; Photogrammetry; Machine learning; Predictive model
• ISSN: 1520-9512; 1522-1709; 1522-1709
• DOI: 10.1007/s11325-023-02846-9

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.