Keywords: Diagnosis/Prediction, Lung, DCE-MRI, Chronic Lung Allograft Dysfunction
Motivation: Chronic Lung Allograft Dysfunction (CLAD) is a significant cause of mortality among lung transplant recipients, making early detection crucial for timely intervention.
Goal(s): This study aimed to evaluate a deep learning-based method for distinguishing patients with CLAD from non-CLAD using 3D DCE-MRI of the lungs.
Approach: We developed a model using transfer learning from pre-trained VGG-16 model weights and evaluated model performance by 8-fold cross-validation.
Results: The model achieved an average AUROC of 98.5%, indicating high accuracy in distinguishing between CLAD and non-CLAD cases.
Impact: This deep learning approach effectively combines spatial, depth, and temporal information from 3D DCE-MRI, offering a promising tool for enhancing CLAD diagnostic precision.
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