Fully Automated Quantification of Functional Small Airway Disease at Inspiratory and Expiratory Chest CT Using Deep Learning.

Purpose To evaluate the accuracy and time efficiency of a deep learning (DL)-based tool for automated quantification of functional small airway disease (fSAD) at chest CT compared with a conventional semimanual method. Materials and Methods This retrospective study included paired inspiratory and expiratory chest CT examinations performed from January 2016 to July 2022, with fSAD defined according to established criteria. Semimanual fSAD assessment (fSADman) was performed using commercial postprocessing software with multiple manual steps, whereas automatic fSAD assessment (fSADauto) used a DL-based tool to fully automate the analysis. Time for fSADman was measured by two cardiothoracic radiologists in 20 randomly selected cases. The Wilcoxon signed rank test assessed systematic differences between reader measurements. Agreement between fSADman and fSADauto was evaluated using Spearman correlation and Bland-Altman analysis. Classification accuracy for clinically relevant fSAD (≥28% lung involvement) was also assessed. Results The study included 249 CT examinations from 196 patients (median age, 56 [IQR, 48-65] years; 120 [61.2%] male). Median lung volumes were 5196 mL (IQR, 4315-6192 mL) at inspiration and 2802 mL (IQR, 2401-3451 mL) at expiration. Agreement between fSADman and fSADauto was excellent (Spearman r = 0.93 [95% CI: 0.89, 0.96]; P < .01), with a small bias of -1.7% on Bland-Altman analysis. For detecting fSAD of 28% or greater, fSADauto achieved 100% sensitivity, 96.8% specificity, and 97.2% accuracy. No significant systematic difference was observed between readers (Wilcoxon W = 1; P = .66). Median semimanual analysis times were 3.47 (IQR, 3.06-3.97) minutes and 5.21 (IQR, 3.57-6.65) minutes, respectively. Conclusion DL-based automated quantification of fSAD demonstrated excellent agreement with semimanual assessment and high diagnostic accuracy and may serve as an efficient and reliable alternative to the traditional semimanual method. Keywords: Pulmonary, Lung, Machine Learning, Workflow Optimization, Segmentation Supplemental material is available for this article. © RSNA, 2026.