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. © RSNA, 2023 Supplemental material is available for this article.

Diet is a health-related factor that can modify lung function. This study hypothesized that the change in age-related dietary intake affects lung function. The subjects who undertook a dietary assessment and spirometry in 2012 and 2017, were retrospectively collected in a health screening center. Dietary intakes were directly evaluated using food frequency questionnaires (FFQ) administered by trained dietitians and were compared at the baseline (2012) and 5-year follow-up (2017). A forced expiratory volume in one second (FEV₁)/forced vital capacity (FVC) value below 0.70 was defined as airflow limitation. Logistic regression models were used to estimate the odds ratio (ORs) adjusted for potential confounders. A total of 1439 subjects with normal spirometry were enrolled. New airflow limitations were detected in 48 subjects (3.3%) at the 5-year follow-up, including 41 (85.4%) men and 11 (22.9%) current smokers. After adjusting for age, sex, smoking history, and baseline FEV1/FVC, the odd ratios (OR) for new airflow limitation in fiber, vitamin C, and folic acid per 10% decrease in daily recommended requirement were 2.714 (95% confidence interval (CI), 1.538–4.807; p = 0.001), 1.083 (95% CI: 1.020–1.149; p = 0.007), and 1.495 (95% CI: 1.172–1.913; p = 0.001), respectively. A decreased intake of dietary fiber, vitamin C, and folic acid is associated with a newly developed airflow limitation.

Jung YJ, et al. J Korean Med Sci. 2018 Oct;33(53):e342. https://doi.org/10.3346/jkms.2018.33.e342