Automated scoring of airway abnormalities and mucus plugging in chest magnetic resonance imaging of cystic fibrosis using artificial intelligence

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 (

• Ringwald, F. G.
• Wucherpfennig, L.
• Martynova, A.
• Hagen, N.
• Kürschner, J.
• Zhao, S.
• Stahl, M.
• Sommerburg, O.
• Mall, M. A.
• Graeber, S. Y.
• Steinke, E.
• Knaup, P.
• Wielpütz, M. O.
• Eisenmann, U.