Predicting Obstructive Sleep Apnea Based on Computed Tomography Scans Using Deep Learning Models

作者：Jeong‐Whun Kim, Kyungsu Lee, Hyun Jik Kim, Hae Chan Park, Jae Youn Hwang, Seok‐Won Park, Hyoun‐Joong Kong, Jin Youp Kim · 发表于：American Journal of Respiratory and Critical Care Medicine · 年份：2024 · DOI：10.1164/rccm.202304-0767oc · 被引用次数：28 · 研究领域：Obstructive Sleep Apnea Research、Tracheal and airway disorders、Cardiovascular and Diving-Related Complications

Abstract Rationale The incidence of clinically undiagnosed obstructive sleep apnea (OSA) is high among the general population because of limited access to polysomnography. Computed tomography (CT) of craniofacial regions obtained for other purposes can be beneficial in predicting OSA and its severity. Objectives To predict OSA and its severity based on paranasal CT using a three-dimensional deep learning algorithm. Methods One internal dataset (N = 798) and two external datasets (N = 135 and N = 85) were used in this study. In the internal dataset, 92 normal participants and 159 with mild, 201 with moderate, and 346 with severe OSA were enrolled to derive the deep learning model. A multimodal deep learning model was elicited from the connection between a three-dimensional convolutional neural network–based part treating unstructured data (CT images) and a multilayer perceptron–based part treating structured data (age, sex, and body mass index) to predict OSA and its severity. Measurements and Main Results In a four-class classification for predicting the severity of OSA, the AirwayNet-MM-H model (multimodal model with airway-highlighting preprocessing algorithm) showed an average accuracy of 87.6% (95% confidence interval [CI], 86.8–88.6%) in the internal dataset and 84.0% (95% CI, 83.0–85.1%) and 86.3% (95% CI, 85.3–87.3%) in the two external datasets, respectively. In the two-class classification for predicting significant OSA (moderate to severe OSA), the area under the re...