A novel machine learning model for screening the risk of obstructive sleep apnea using craniofacial photography with questionnaires.

Study Objectives: Undiagnosed or untreated moderate to severe obstructive sleep apnea (OSA) increases cardiovascular risks and mortality. Early and efficient detection is critical, given its high prevalence. We aimed to develop a practical and efficient approach for obstructive sleep apnea screening, using simple facial photography and sleep questionnaires.

Methods: We retrospectively included 748 participants who completed polysomnography, sleep questionnaires (STOP-BANG, SBQ), and facial photographs at a university hospital between 2012 and 2023. Owing to class imbalance, we randomly undersampled the participants, categorized into the moderate/severe or no/mild OSA group, based on an apnea-hypopnea index of 15 events/h. Using a validated convolutional neural network, we extracted the OSA probability scores from photographs, which were used as the input for the questionnaires. Four machine learning models were employed to classify the moderate/severe versus no/mild groups and evaluated in the test dataset.

Results: We analyzed 426 participants (213 each in the moderate/severe and no/mild groups). The mean (standard deviation) age was 44.6 (14.7) years; 80.8% were men. Logistic regression achieved the highest performance: the area under the receiver operator curve was 97.2%, and accuracy was 91.9%. Adding OSA probability, retrieved from facial photographs, to the questionnaires improved performance, compared with using questionnaires or photographs alone (area under the receiver operator curve: 97.2%, 64% and 79.1% for threshold SBQ 3 and 4, and 85.7%, respectively).

Conclusions: Using simple facial photographs and sleep questionnaires, a two-stage approach (convolutional neural network + machine learning) accurately classified OSA into moderate/severe versus no/mild OSA groups. This method may facilitate optimal OSA treatment and avoid unnecessary costly evaluations.