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Abstract #0332

A Deep Transfer Learning Model for Liver Stiffness Classification using Clinical and T2-Weighted MRI Data

Hailong Li1,2, Lili He1,2,3, Jonathan Dudley2,4, Thomas Maloney2,4, Elanchezhian Somasundaram4, Samuel L. Brady4,5, Nehal A. Parikh 1,3, and Jonathan R. Dillman2,4,5
1The Perinatal Institute and Section of Neonatology, Perinatal and Pulmonary Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States, 2Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States, 3Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States, 4Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States, 5Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, United States

Detection and monitoring of chronic liver diseases is typically assessed using a combination of clinical history, physical examination, laboratory testing, biopsy with histopathologic assessment, and imaging. The aim of this study is to develop a deep transfer learning model (DeepLiverNet) to categorically classify the severity of liver stiffening (no/mild vs. moderate/severe) using both anatomic T2-weighted MR images and clinical data. The DeepLiverNet model achieved accuracies of 88.0% and 80.0% on the risk stratification of liver stiffness in internal and external validation datasets, respectively. This demonstrates that a deep learning model may provide a means for stratifying liver stiffness without elastography.

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