Single Inspiratory Chest CT-based Generative Deep Learning Models to Evaluate Functional Small Airway Disease.
Journal:
Radiology. Artificial intelligence
Published Date:
Jul 16, 2025
Abstract
Purpose To develop a deep learning model that uses a single inspiratory chest CT scan to generate parametric response maps (PRM) and predict functional small airway disease (fSAD). Materials and Methods In this retrospective study, predictive and generative deep learning models for PRM using inspiratory chest CT were developed using a model development dataset with fivefold cross-validation, with PRM derived from paired respiratory CT as the reference standard. Voxel-wise metrics, including sensitivity, area under the receiver operating characteristic curve (AUC), and structural similarity, were used to evaluate model performance in predicting PRM and expiratory CT images. The best performing model was tested on three internal test sets and an external test set. Results The model development dataset of 308 patients (median age, 67 years, [IQR: 62-70 years]; 113 female) was divided into the training set ( = 216), the internal validation set ( = 31), and the first internal test set ( = 61). The generative model outperformed the predictive model in detecting fSAD (sensitivity 86.3% vs 38.9%; AUC 0.86 vs 0.70). The generative model performed well in the second internal (AUCs of 0.64, 0.84, 0.97 for emphysema, fSAD and normal lung tissue), the third internal (AUCs of 0.63, 0.83, 0.97), and the external (AUCs of 0.58, 0.85, 0.94) test sets. Notably, the model exhibited exceptional performance in the PRISm group of the fourth internal test set (AUC = 0.62, 0.88, and 0.96). Conclusion The proposed generative model, using a single inspiratory CT, outperformed existing algorithms in PRM evaluation, achieved comparable results to paired respiratory CT. Published under a CC BY 4.0 license.
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