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Abstract #4135

Deep learning assisted detection of chronic lung allograft dysfunction using pulmonary DCE-MRI

Kei Chuen Ma1, Xingxin He1, Antonia Susnjar1, Marvah Hill Pierre-Louis2, Sydney Montesi2, Fang Liu1, and Iris Y. Zhou1
1Athinoula A. Martinos Center for Biomedical Imaging,Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States, 2Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States

Synopsis

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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Keywords