한빛사논문
- 조회370
Hyunjung Park, Jihye Yun, Sang Min Lee, Hye Jeon Hwang, Joon Beom Seo, Young Ju Jung, Jeongeun Hwang, Se Hee Lee, Sei Won Lee* , Namkug Kim*
From the Department of Medical Science and Department of Bioengineering, Asan Medical Institute of Convergence Science and Technology (H.P., N.K.), Department of Radiology and Research Institute of Radiology (J.Y., S.M.L., H.J.H., J.B.S., N.K.), Department of Pulmonology and Critical Care Medicine and Clinical Research Center for Chronic Obstructive Airway Diseases (S.W.L.), and Health Screening and Promotion Center (Y.J.J.), Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea; Division of Medical Oncology, Department of Internal Medicine, Korea University College of Medicine, Seoul, Republic of Korea (J.H.); Department of Biomedical Research Center, Korea University Guro Hospital, Seoul, Republic of Korea (J.H.); and Department of Pulmonology, Allergy and Critical Care Medicine, CHA Bundang Medical Center, CHA University, Seongnam, Republic of Korea (S.H.L.).
* S.W.L. and N.K. are co-senior authors.
Address correspondence to S.W.L.
Abstract
Background
Low-dose chest CT screening is recommended for smokers with the potential for lung function abnormality, but its role in predicting lung function remains unclear.
Purpose
To develop a deep learning algorithm to predict pulmonary function with low-dose CT images in participants using health screening services.
Materials and Methods
In this retrospective study, participants underwent health screening with same-day low-dose CT and pulmonary function testing with spirometry at a university affiliated tertiary referral general hospital between January 2015 and December 2018. The data set was split into a development set (model training, validation, and internal test sets) and temporally independent test set according to first visit year. A convolutional neural network was trained to predict the forced expiratory volume in the first second of expiration (FEV1) and forced vital capacity (FVC) from low-dose CT. The mean absolute error and concordance correlation coefficient (CCC) were used to evaluate agreement between spirometry as the reference standard and deep-learning prediction as the index test. FVC and FEV1 percent predicted (hereafter, FVC% and FEV1%) values less than 80% and percent of FVC exhaled in first second (hereafter, FEV1/FVC) less than 70% were used to classify participants at high risk.
Results
A total of 16 148 participants were included (mean age, 55 years ± 10 [SD]; 10 981 men) and divided into a development set (n = 13 428) and temporally independent test set (n = 2720). In the temporally independent test set, the mean absolute error and CCC were 0.22 L and 0.94, respectively, for FVC and 0.22 L and 0.91 for FEV1. For the prediction of the respiratory high-risk group, FVC%, FEV1%, and FEV1/FVC had respective accuracies of 89.6% (2436 of 2720 participants; 95% CI: 88.4, 90.7), 85.9% (2337 of 2720 participants; 95% CI: 84.6, 87.2), and 90.2% (2453 of 2720 participants; 95% CI: 89.1, 91.3) in the same testing data set. The sensitivities were 61.6% (242 of 393 participants; 95% CI: 59.7, 63.4), 46.9% (226 of 482 participants; 95% CI: 45.0, 48.8), and 36.1% (91 of 252 participants; 95% CI: 34.3, 37.9), respectively.
Conclusion
A deep learning model applied to volumetric chest CT predicted pulmonary function with relatively good performance.
논문정보
- Research article
- 2023년 02월 (BRIC 등록일 2023-03-07)
- 국내 연구진
- 바이오·의료융합 > 의료기기
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