AIMC Topic: Hepatitis B, Chronic
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A deep learning model with data integration of ultrasound contrast-enhanced micro-flow cines, B-mode images, and clinical parameters for diagnosing significant liver fibrosis in patients with chronic hepatitis B.
European radiology
Feb 3, 2023
OBJECTIVE: To develop and investigate a deep learning model with data integration of ultrasound contrast-enhanced micro-flow (CEMF) cines, B-mode images, and patients' clinical parameters to improve the diagnosis of significant liver fibrosis (≥ F2) ...
Diagnosis of significant liver fibrosis in patients with chronic hepatitis B using a deep learning-based data integration network.
Hepatology international
Mar 21, 2022
BACKGROUND AND AIMS: Chronic hepatitis B virus (CHB) infection remains a major global health burden and the non-invasive and accurate diagnosis of significant liver fibrosis (≥ F2) in CHB patients is clinically very important. This study aimed to ass...
Deep Learning in the Classification of Stage of Liver Fibrosis in Chronic Hepatitis B with Magnetic Resonance ADC Images.
Contrast media & molecular imaging
Dec 22, 2021
Liver fibrosis in chronic hepatitis B is the pathological repair response of the liver to chronic injury, which is a key step in the development of various chronic liver diseases to cirrhosis and an important link affecting the prognosis of chronic l...
Optimizing hepatitis B virus screening in the United States using a simple demographics-based model.
Hepatology (Baltimore, Md.)
Dec 7, 2021
BACKGROUND AND AIMS: Chronic hepatitis B (CHB) affects >290 million persons globally, and only 10% have been diagnosed, presenting a severe gap that must be addressed. We developed logistic regression (LR) and machine learning (ML; random forest) mod...
A baseline model including quantitative anti-HBc to predict response of peginterferon in HBeAg-positive chronic hepatitis B patients.
Antiviral therapy
Nov 16, 2021
BACKGROUND: Few models to predict antiviral response of peginterferon were used in hepatitis B e antigen (HBeAg)-positive chronic hepatitis B patients and the prediction efficacy was unsatisfied. Quantitative antibody to hepatitis B core antigen (ant...
An artificial intelligence model to predict hepatocellular carcinoma risk in Korean and Caucasian patients with chronic hepatitis B.
Journal of hepatology
Oct 2, 2021
BACKGROUND & AIMS: Several models have recently been developed to predict risk of hepatocellular carcinoma (HCC) in patients with chronic hepatitis B (CHB). Our aims were to develop and validate an artificial intelligence-assisted prediction model of...
A model based on clinico-biochemical characteristics and deep learning features from MR images for assessing necroinflammatory activity in chronic hepatitis B.
Journal of viral hepatitis
Aug 8, 2021
High-dimensional hepatopath data analysis by machine learning for predicting HBV-related fibrosis.
Scientific reports
Mar 3, 2021
Chronic HBV infection, the main cause of liver cirrhosis and hepatocellular carcinoma, has become a global health concern. Machine learning algorithms are particularly adept at analyzing medical phenomenon by capturing complex and nonlinear relations...
The role of quantitative HBsAg in the natural history of e antigen-negative chronic hepatitis B: A Tunisian prospective study.
Infectious diseases now
Dec 30, 2020
BACKGROUND/AIMS: During the natural course of Chronic Hepatitis B (CHB) infection, differentiation between inactive carrier (IC) and HBeAg negative CHB is a subject of ongoing debate. We studied the role of hepatitis B surface antigen (HBsAg) level a...
Transfer learning radiomics based on multimodal ultrasound imaging for staging liver fibrosis.
European radiology
Jan 21, 2020
OBJECTIVES: To propose a transfer learning (TL) radiomics model that efficiently combines the information from gray scale and elastogram ultrasound images for accurate liver fibrosis grading.
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