Predicting obstructive sleep apnea severity from craniofacial images using ensemble machine learning models

Ziyu Su; Sandhya Kumar; Thomas E. Tavolara; Metin N. Gurcan; Scott Segal; M. Khalid Khan Niazi

doi:10.1117/12.2654353

7 April 2023 Predicting obstructive sleep apnea severity from craniofacial images using ensemble machine learning models

Ziyu Su, Sandhya Kumar, Thomas E. Tavolara, Metin N. Gurcan, Scott Segal, M. Khalid Khan Niazi

Author Affiliations +

Proceedings Volume 12465, Medical Imaging 2023: Computer-Aided Diagnosis; 124652P (2023) https://doi.org/10.1117/12.2654353
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Conference Poster

Abstract

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.

Citation Download Citation

Ziyu Su, Sandhya Kumar, Thomas E. Tavolara, Metin N. Gurcan, Scott Segal, and M. Khalid Khan Niazi "Predicting obstructive sleep apnea severity from craniofacial images using ensemble machine learning models", Proc. SPIE 12465, Medical Imaging 2023: Computer-Aided Diagnosis, 124652P (7 April 2023); https://doi.org/10.1117/12.2654353

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