Abstract #0332

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

Hailong Li^1,2, Lili He^1,2,3, Jonathan Dudley^2,4, Thomas Maloney^2,4, Elanchezhian Somasundaram⁴, Samuel L. Brady^4,5, Nehal A. Parikh ^1,3, and Jonathan R. Dillman^2,4,5

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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