Available online 15 October 2025

Cystic fibrosis is characterized by progressive lung damage, requiring life-long medical treatment and monitoring. This emphasizes the need for reliable, radiation-free imaging and automated analysis of lung disease activity. We present a deep learning-based approach for classifying two key pathologies, bronchiectasis/wall thickening and mucus plugging, on T2-weighted chest MRI. Retrospectively, 627 MRI scans from 164 patients (mean age 7.0 ± 6.2 years; range 0.1-53.0 years) were collected. Chest MRI were preprocessed with an nnU-Net to segment lung halves, followed by an atlas-based lung lobe approximation. Leveraging a dual-view architecture processing coronal and axial slices, our approach addresses limitations inherent in manual scoring, such as reader variability and substantial labor requirements. We evaluated a single model trained on all lobes and models specialized for each lobe. Cross-validation revealed substantial agreement for mucus plugging (κ = 0.68) with strong discrimination (macro AUROC = 0.90) and excellent reliability (Pearson’s r = 0.84). For bronchiectasis/wall thickening, agreement was moderate (κ = 0.53) but discrimination remained strong (macro AUROC = 0.87), with Pearson’s r = 0.74. The mean differences and 95% limits of agreement for both pathologies aligned closely with the reader variability previously reported. Grad-CAM analyses demonstrated spatial alignment of model attention with relevant pathologies, and external testing on ten patients from an independent centre confirmed promising generalization. These findings represent a significant step toward automated MRI-based assessment for CF-related lung changes. Extending the approach to additional MRI scoring items may also improve granularity and clinical applicability, ultimately aiding in more personalized CF management.

cystic fibrosis

deep learning

magnetic resonance imaging

lung

© 2025 Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.