Automated Evaluation of Upper Airway Obstruction Based on Deep Learning
Yunho Jeong, Yeeyeewin Nang, Zhihe Zhao
Abstract
Objectives . This study is aimed at developing a screening tool that could evaluate the upper airway obstruction on lateral cephalograms based on deep learning. Methods . We developed a novel and practical convolutional neural network model to automatically evaluate upper airway obstruction based on ResNet backbone using the lateral cephalogram. A total of 1219 X‐ray images were collected for model training and testing. Results . In comparison with VGG16, our model showed a better performance with sensitivity of 0.86, specificity of 0.89, PPV of 0.90, NPV of 0.85, and F1‐score of 0.88, respectively. The heat maps of cephalograms showed a deeper understanding of features learned by deep learning model. Conclusion . This study demonstrated that deep learning could learn effective features from cephalograms and automated evaluate upper airway obstruction according to X‐ray images. Clinical Relevance . A novel and practical deep convolutional neural network model has been established to relieve dentists’ workload of screening and improve accuracy in upper airway obstruction.