Identification of age-dependent features of human bronchi using explainable artificial intelligence

Ageing induces functional and structural alterations in organs, and age-dependent parameters have been identified in various medical data sources. However, there is currently no specific clinical test to quantitatively evaluate age-related changes in bronchi. This study aimed to identify age-dependent bronchial features using explainable artificial intelligence for bronchoscopy images.

The present study included 11 374 bronchoscopy images, divided into training and test datasets based on the time axis. We constructed convolutional neural network (CNN) models and evaluated these models using the correlation coefficient between the chronological age and the "bronchial age" calculated from bronchoscopy images. We employed gradient-weighted class activation mapping (Grad-CAM) to identify age-dependent bronchial features that the model focuses on. We assessed the universality of our model by comparing the distribution of bronchial age for each respiratory disease or smoking history.

We constructed deep-learning models using four representative CNN architectures to calculate bronchial age. Although the bronchial age showed a significant correlation with chronological age in each CNN architecture, EfficientNetB3 achieved the highest Pearson's correlation coefficient (0.9617). The application of Grad-CAM to the EfficientNetB3-based model revealed that the model predominantly attended to bronchial bifurcation sites, regardless of whether the model accurately predicted chronological age or exhibited discrepancies. There were no significant differences in the discrepancy between the bronchial age and chronological age among different respiratory diseases or according to smoking history.

Bronchial bifurcation sites are universally important age-dependent features in bronchi, regardless of the type of respiratory disease or smoking history.