Dynamic Digital Radiography Pulmonary Function Testing: A Machine Learning Lung Study Alternative

Background: Common diagnostic tests for pulmonary disorders include chest radiography and pulmonary function tests (PFTs). Although essential, these tests only offer a static assessment. Chest dynamic digital radiography (DDR) integrates lung and diaphragm motion in one study with limited radiation exposure. DDR is relatively easy to obtain, but barriers to its clinical adoption include time-consuming manual analysis and unclear correlation with PFTs. Research Question: Can a machine learning pipeline automate DDR analysis? What is the strength of the relationship between PFT measures and automated DDR-based lung area measurements? Study Design and Methods: PFT and DDR studies were obtained in 55 participants. We developed an analysis pipeline using convolutional neural networks capable of quantifying lung areas in DDR images to generate DDR-based PFTs (dPFTs). PFT and dPFT measures were correlated in patients with normal, obstructive, and restrictive lung physiology. Results: We observed statistically significant (P < 1 × 10^-6), strong correlations between dPFT areas and PFT volumes, including total lung capacity (r = 0.764), FEV₁ (r = 0.591), vital capacity (r = 0.763), and functional residual capacity (r = 0.756). Automated DDR and lung shape tracking revealed differences between normal, restrictive, and obstructive physiology using diaphragm curvature indices and strain analysis measurements. Linear regressions allowed for derivation of PFT values from dPFT measurements. Interpretation: Statistically significant correlations found between dPFTs and PFTs suggest that dPFTs can act as a surrogate to PFTs when these are not available or unable to be performed. This study contributes to the potential integration of DDR as a reliable alternative to PFTs.