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Deep Learning-Based Automated Assessment of Hepatic Fibrosis on Magnetic Resonance Images and Non-Image Data
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inferredcites →https://doi.org/10.1002/hep.20701openalex-extracted
Design and validation of a histological scoring system for nonalcoholic fatty liver disease†
inferredcites →https://doi.org/10.1002/hep.21178openalex-extracted
Development of a simple noninvasive index to predict significant fibrosis in patients with HIV/HCV coinfection†‡
inferredcites →https://doi.org/10.1002/hep.21972openalex-extracted
Magnetic resonance imaging of hepatic fibrosis: Emerging clinical applications
inferredcites →https://doi.org/10.1002/hep.30974openalex-extracted
Clinical Utility of an Increase in Magnetic Resonance Elastography in Predicting Fibrosis Progression in Nonalcoholic Fatty Liver Disease
inferredcites →https://doi.org/10.1002/jmri.23585openalex-extracted
Accuracy of MR elastography and anatomic MR imaging features in the diagnosis of severe hepatic fibrosis and cirrhosis
inferredcites →https://doi.org/10.1002/jmri.25328openalex-extracted
Application of texture analysis on parametricT1andT2maps for detection of hepatic fibrosis
inferredcites →https://doi.org/10.1002/sim.2930openalex-extracted
Interval estimation of binomial proportion in clinical trials with a two‐stage design
inferredcites →https://doi.org/10.1007/s00261-014-0321-8openalex-extracted
Magnetic resonance elastography (MRE) in assessing hepatic fibrosis: performance in a cohort of patients with histological data
inferredcites →https://doi.org/10.1007/s00261-017-1096-5openalex-extracted
Texture analysis of the liver at MDCT for assessing hepatic fibrosis
inferredcites →https://doi.org/10.1007/s00330-018-5499-7openalex-extracted
Deep learning for staging liver fibrosis on CT: a pilot study
inferredcites →https://doi.org/10.1007/s00330-019-06407-1openalex-extracted
Deep learning with ultrasonography: automated classification of liver fibrosis using a deep convolutional neural network
inferredcites →https://doi.org/10.1007/s00330-020-06665-4openalex-extracted
Assessment of liver fibrosis severity using computed tomography–based liver and spleen volumetric indices in patients with chronic liver disease
inferredcites →https://doi.org/10.1007/s00330-020-07475-4openalex-extracted
Fully automated prediction of liver fibrosis using deep learning analysis of gadoxetic acid–enhanced MRI
inferredcites →https://doi.org/10.1016/0168-8278(91)90084-oopenalex-extracted
Classification of chronic viral hepatitis: a need for reassessment
inferredcites →https://doi.org/10.1016/j.bpg.2011.02.003openalex-extracted
Invasive and non-invasive methods for the assessment of fibrosis and disease progression in chronic liver disease
inferredcites →https://doi.org/10.1016/j.jhep.2008.10.016openalex-extracted
Can imaging modalities diagnose and stage hepatic fibrosis and cirrhosis accurately?
inferredcites →https://doi.org/10.1016/j.jhep.2018.09.014openalex-extracted
Burden of liver diseases in the world
inferredcites →https://doi.org/10.1053/j.gastro.2004.11.018openalex-extracted
Prospective comparison of transient elastography, Fibrotest, APRI, and liver biopsy for the assessment of fibrosis in chronic hepatitis C
inferredcites →https://doi.org/10.1053/j.gastro.2005.03.084openalex-extracted
Sampling Variability of Liver Biopsy in Nonalcoholic Fatty Liver Disease
inferredcites →https://doi.org/10.1053/j.gastro.2012.02.017openalex-extracted
Noninvasive Methods to Assess Liver Disease in Patients With Hepatitis B or C
inferredcites →https://doi.org/10.1053/jhep.2003.50346openalex-extracted
A Simple Noninvasive Index Can Predict Both Significant Fibrosis and Cirrhosis in Patients With Chronic Hepatitis C
inferredcites →https://doi.org/10.1109/bibe50027.2020.00166openalex-extracted
Deep Learning based NAS Score and Fibrosis Stage Prediction from CT and Pathology Data
inferredcites →https://doi.org/10.1136/gutjnl-2018-316204openalex-extracted
Deep learning Radiomics of shear wave elastography significantly improved diagnostic performance for assessing liver fibrosis in chronic hepatitis B: a prospective multicentre study
inferredcites →https://doi.org/10.1148/radiol.2017171928openalex-extracted
Liver Fibrosis: Deep Convolutional Neural Network for Staging by Using Gadoxetic Acid–enhanced Hepatobiliary Phase MR Images
inferredcites →https://doi.org/10.1148/radiol.2018180763openalex-extracted
Development and Validation of a Deep Learning System for Staging Liver Fibrosis by Using Contrast Agent–enhanced CT Images in the Liver
inferredcites →https://doi.org/10.1148/radiol.2272020193openalex-extracted
Severe Liver Fibrosis or Cirrhosis: Accuracy of US for Detection—Analysis of 300 Cases
inferredcites →https://doi.org/10.1148/rg.296095516openalex-extracted
Use of 3.0-T MR Imaging for Evaluation of the Abdomen
inferredcites →https://doi.org/10.11613/bm.2012.031openalex-extracted
Interrater reliability: the kappa statistic
inferredcites →https://doi.org/10.1371/journal.pone.0044930openalex-extracted
Performance of Transient Elastography for the Staging of Liver Fibrosis in Patients with Chronic Hepatitis B: A Meta-Analysis
inferredcites →https://doi.org/10.1371/journal.pone.0114224openalex-extracted
Histogram Analysis of Gadoxetic Acid-Enhanced MRI for Quantitative Hepatic Fibrosis Measurement
inferredcites →https://doi.org/10.20524/aog.2019.0406openalex-extracted
Liver fibrosis staging with combination of APRI and FIB-4 scoring systems in chronic hepatitis C as an alternative to transient elastography
inferredcites →https://doi.org/10.2307/2531595openalex-extracted
Comparing the Areas under Two or More Correlated Receiver Operating Characteristic Curves: A Nonparametric Approach
inferredcites →https://doi.org/10.4254/wjh.v7.i3.607openalex-extracted
Staging of liver fibrosis or cirrhosis: The role of hepatic venous pressure gradient measurement
inferredcites →https://doi.org/10.7326/0003-4819-158-11-201306040-00005openalex-extracted
Blood Tests to Diagnose Fibrosis or Cirrhosis in Patients With Chronic Hepatitis C Virus Infection
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