한빛사논문
- 조회233
Jeong-Whun Kim 1*, Kyungsu Lee 2*, Hyun Jik Kim 3*, Hae Chan Park 1, Jae Youn Hwang 2, Seok-Won Park 8, Hyoun-Joong Kong 4,5,6, and Jin Youp Kim 7,8
1Department of Otorhinolaryngology–Head and Neck Surgery, Seoul National University Bundang Hospital, Seoul National University College ofMedicine, Seongnam, Republic of Korea;
2Department of Electrical Engineering and Computer Science, Daegu Gyeongbuk Institute of Science and Technology, Daegu, Republic of Korea;
3Department of Otorhinolaryngology–Head and Neck Surgery,
4Department of TransdisciplinaryMedicine,
5Innovative Medical Technology Research Institute, Seoul National University Hospital, Seoul, Republic of Korea,
6Department of Medicine, and
7Interdisciplinary Programof Medical Informatics, Seoul National University College of Medicine, Seoul, Republic of Korea; and
8Department of Otorhinolaryngology–Head and Neck Surgery, Ilsan Hospital, Dongguk University, Gyeonggi, Republic of Korea
*These authors contributed equally to this work.
Corresponding Authors : Jin Youp Kim, MD, Hyoun-Joong Kong, PhD
Abstract
Rationale: The incidence of clinically undiagnosed obstructive sleep apnea (OSA) is high among the general population due to 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 3-dimensional deep learning algorithm.
Methods: One internal dataset (n=798) and two external datasets (n=135 and 85) were used in this study. In the internal dataset, 92 normal, 159 mild, 201 moderate, and 346 severe OSA participants were enrolled to derive the deep learning model. A multimodal deep learning model was elicited from the connection between a 3-dimensional convolutional neural network (CNN)-based part treating unstructured data (CT images) and a multi-layer perceptron (MLP)-based part treating structured data (age, sex, and body mass index) to predict OSA and its severity.
Measurements and main results: In 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 receiver operating characteristics (AUROC), accuracy, sensitivity, specificity, and F1 score were 0.910 (95% CI 0.899-0.922), 91.0% (95% CI 90.1-91.9), 89.9% (95% CI 88.8-90.9), 93.5% (95% CI 92.7-94.3), and 93.2% (95% CI 92.5-93.9), respectively, in the internal dataset. Furthermore, the diagnostic performance of the Airway Net-MM-H model outperformed that of the other six state-of-the-art deep learning models in terms of accuracy for both four- and two-class classifications and AUROC for two-class classification (p<0.001).
Conclusions: A novel deep learning model, including a multimodal deep learning model and an airway-highlighting preprocessing algorithm from CT images obtained for other purposes, can provide significantly precise outcomes for OSA diagnosis.
논문정보
- Research article
- 2024년 07월 (BRIC 등록일 2024-07-16)
- 국내 연구진
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