Michal Byra1,2, Aiguo Han3, Andrew S Boehringer4, Yingzhen N Zhang4, William D O'Brien3, John W Erdman5, Rohit Loomba6, Claude B Sirlin4, Michael Andre1. 1. Department of Radiology, University of California, La Jolla, California, USA. 2. Department of Ultrasound, Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Poland. 3. Bioacoustics Research Laboratory, Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA. 4. Liver Imaging Group, Department of Radiology, University of California, La Jolla, California, USA. 5. Department of Food Science and Human Nutrition, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA. 6. NAFLD Research Center, Division of Gastroenterology, Department of Medicine, University of California, La Jolla, California, USA.
Abstract
OBJECTIVES: To develop and evaluate deep learning models devised for liver fat assessment based on ultrasound (US) images acquired from four different liver views: transverse plane (hepatic veins at the confluence with the inferior vena cava, right portal vein, right posterior portal vein) and sagittal plane (liver/kidney). METHODS: US images (four separate views) were acquired from 135 participants with known or suspected nonalcoholic fatty liver disease. Proton density fat fraction (PDFF) values derived from chemical shift-encoded magnetic resonance imaging served as ground truth. Transfer learning with a deep convolutional neural network (CNN) was applied to develop models for diagnosis of fatty liver (PDFF ≥ 5%), diagnosis of advanced steatosis (PDFF ≥ 10%), and PDFF quantification for each liver view separately. In addition, an ensemble model based on all four liver view models was investigated. Diagnostic performance was assessed using the area under the receiver operating characteristics curve (AUC), and quantification was assessed using the Spearman correlation coefficient (SCC). RESULTS: The most accurate single view was the right posterior portal vein, with an SCC of 0.78 for quantifying PDFF and AUC values of 0.90 (PDFF ≥ 5%) and 0.79 (PDFF ≥ 10%). The ensemble of models achieved an SCC of 0.81 and AUCs of 0.91 (PDFF ≥ 5%) and 0.86 (PDFF ≥ 10%). CONCLUSION: Deep learning-based analysis of US images from different liver views can help assess liver fat.
OBJECTIVES: To develop and evaluate deep learning models devised for liver fat assessment based on ultrasound (US) images acquired from four different liver views: transverse plane (hepatic veins at the confluence with the inferior vena cava, right portal vein, right posterior portal vein) and sagittal plane (liver/kidney). METHODS: US images (four separate views) were acquired from 135 participants with known or suspected nonalcoholic fatty liver disease. Proton density fat fraction (PDFF) values derived from chemical shift-encoded magnetic resonance imaging served as ground truth. Transfer learning with a deep convolutional neural network (CNN) was applied to develop models for diagnosis of fatty liver (PDFF ≥ 5%), diagnosis of advanced steatosis (PDFF ≥ 10%), and PDFF quantification for each liver view separately. In addition, an ensemble model based on all four liver view models was investigated. Diagnostic performance was assessed using the area under the receiver operating characteristics curve (AUC), and quantification was assessed using the Spearman correlation coefficient (SCC). RESULTS: The most accurate single view was the right posterior portal vein, with an SCC of 0.78 for quantifying PDFF and AUC values of 0.90 (PDFF ≥ 5%) and 0.79 (PDFF ≥ 10%). The ensemble of models achieved an SCC of 0.81 and AUCs of 0.91 (PDFF ≥ 5%) and 0.86 (PDFF ≥ 10%). CONCLUSION: Deep learning-based analysis of US images from different liver views can help assess liver fat.
Authors: Michał Byra; Katarzyna Dobruch-Sobczak; Hanna Piotrzkowska-Wroblewska; Ziemowit Klimonda; Jerzy Litniewski Journal: J Ultrason Date: 2022-04-27
Authors: Liu-Liu Cao; Mei Peng; Xiang Xie; Gong-Quan Chen; Shu-Yan Huang; Jia-Yu Wang; Fan Jiang; Xin-Wu Cui; Christoph F Dietrich Journal: World J Gastroenterol Date: 2022-07-21 Impact factor: 5.374