Proceedings Article | 3 April 2024
KEYWORDS: Lung, Computed tomography, Chest, Error analysis, Education and training, Deep learning, Lung cancer, Correlation coefficients, Pulmonary disorders
Purpose: To develop a deep learning model to estimate lung age based on low-dose chest computed tomography (CT) images and investigate the feasibility of its application in health management. Materials and Methods: Deep learning models (InceptionV3, DenseNet, VGG16, and ResNet50) were trained on a set of low-dose chest CT images of 4,646 non-smokers. They were adjusted for an equal number of participants in each age group to estimate the chronological age and functional lung age. They were further tested on a dataset of 5,841 nonsmokers and 16,709 smokers. The estimated ages were evaluated based on the mean absolute error, correlation coefficients with the actual ages, and Bland-Altman analysis. In addition, we tested whether the estimated age was higher in the group with a longer the smoking history. Results: There was a correlation between the estimated and actual ages in all models (chronological/functional lung ages): r=0.74/0.54, r=0.75/0.53, r=0.68/0.50, and r=0.67/0.34 for InceptionV3, DenseNet, VGG16, and ResNet50, respectively. The InceptionV3 model showed the best performance with an estimation error of ±4.02 and ±11.18 years for chronological and functional lung ages, respectively. Furthermore, among smokers, the longer the smoking history, the higher the estimated chronological age, suggesting that the trained model can predict lung changes other than aging, such as the effects of smoking and signs of disease onset. Conclusion: We developed an age estimation model based on low-dose chest CT images that can quantify lung damage due to smoking as an increase in predicted age. The feasibility of CT image-based lung age estimation for health care was demonstrated.
