Previous studies suggest that respiratory events are the leading cause of airway management-related deaths. Successful prediction of difficult-to-intubate (DI) patients can help clinicians precisely allocate the respiratory resources for good airway management. However, the current clinical bedside tests are highly biased with respect to anesthesiologists’ experience and yield moderate accuracy with low sensitivity. Therefore, a diagnostic tool with high accuracy, especially sensitivity, is in critical need. For these reasons, we propose an AI-based method to predict DI patients based on their facial images from frontal and profile views. Our ensemble model based on frontal and profile views of the face achieve an AUC of 0.713 and a sensitivity of 68.5%. In addition, our ensemble model increases sensitivity of thyromental distance from 26.9% to 79.2% while maintaining an acceptable trade-off in specificity. Overall, our model can meaningfully augment the accuracy of current clinical tests for DI. We envision that our model may be embedded on smartphones to serve as a bedside test for DI and that our study provides a basis for future studies for AI-based methods on facial images for DI prediction.
KEYWORDS: Machine learning, Data modeling, Polysomnography, Education and training, Sleep apnea, Deep learning, Performance modeling, Pulmonary disorders, Neurological disorders, Medicine
Obstructive sleep apnea (OSA) is a prevalent disease affecting 10 to 15% of Americans and nearly one billion people worldwide. It leads to multiple symptoms including daytime sleepiness; snoring, choking, or gasping during sleep; fatigue; headaches; non-restorative sleep; and insomnia due to frequent arousals. Although polysomnography (PSG) is the gold standard for OSA diagnosis, it is expensive, not universally available, and time-consuming, so many patients go undiagnosed due to lack of access to the test. Given the incomplete access and high cost of PSG, many studies are seeking alternative diagnosis approaches based on different data modalities. Here, we propose a machine learning model to predict OSA severity from 2D frontal view craniofacial images. In a cross-validation study of 280 patients, our method achieves an average AUC of 0.780. In comparison, the craniofacial analysis model proposed by a recent study only achieves 0.638 AUC on our dataset. The proposed model also outperforms the widely used STOP-BANG OSA screening questionnaire, which achieves an AUC of 0.52 on our dataset. Our findings indicate that deep learning has the potential to significantly reduce the cost of OSA diagnosis.
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