Machine learning for image-based detection of patients with obstructive sleep apnea: an exploratory study

Purpose In 2-dimensional lateral cephalometric radiographs, patients with severe obstructive sleep apnea (OSA) exhibit a more crowded oropharynx in comparison with non-OSA. We tested the hypothesis that machine learning, an application of artificial intelligence (AI), could be used to detect patients with severe OSA based on 2-dimensional images. Methods A deep convolutional neural network was developed ( n = 1258; 90%) and tested ( n = 131; 10%) using data from 1389 (100%) lateral cephalometric radiographs obtained from individuals diagnosed with severe OSA ( n = 867; apnea hypopnea index > 30 events/h sleep) or non-OSA ( n = 522; apnea hypopnea index