AIMC Topic: Fatty Liver
Clear Filters Showing 71 to 80 of 94 articles
Transfer learning with deep convolutional neural network for liver steatosis assessment in ultrasound images.
International journal of computer assisted radiology and surgery
Aug 9, 2018
PURPOSE: The nonalcoholic fatty liver disease is the most common liver abnormality. Up to date, liver biopsy is the reference standard for direct liver steatosis quantification in hepatic tissue samples. In this paper we propose a neural network-base...
Computer-assisted liver graft steatosis assessment via learning-based texture analysis.
International journal of computer assisted radiology and surgery
May 23, 2018
PURPOSE: Fast and accurate graft hepatic steatosis (HS) assessment is of primary importance for lowering liver dysfunction risks after transplantation. Histopathological analysis of biopsied liver is the gold standard for assessing HS, despite being ...
Symtosis: A liver ultrasound tissue characterization and risk stratification in optimized deep learning paradigm.
Computer methods and programs in biomedicine
Dec 16, 2017
Background and Objective Fatty Liver Disease (FLD) - a disease caused by deposition of fat in liver cells, is predecessor to terminal diseases such as liver cancer. The machine learning (ML) techniques applied for FLD detection and risk stratificatio...
Accurate Identification of Fatty Liver Disease in Data Warehouse Utilizing Natural Language Processing.
Digestive diseases and sciences
Aug 31, 2017
INTRODUCTION: Natural language processing is a powerful technique of machine learning capable of maximizing data extraction from complex electronic medical records.
An Ensemble Multilabel Classification for Disease Risk Prediction.
Journal of healthcare engineering
Jun 15, 2017
It is important to identify and prevent disease risk as early as possible through regular physical examinations. We formulate the disease risk prediction into a multilabel classification problem. A novel Ensemble Label Power-set Pruned datasets Joint...
Pulse Diagnosis Signals Analysis of Fatty Liver Disease and Cirrhosis Patients by Using Machine Learning.
TheScientificWorldJournal
Nov 28, 2015
OBJECTIVE: . To compare the signals of pulse diagnosis of fatty liver disease (FLD) patients and cirrhosis patients.
Integration of Metabolomics, Lipidomics, and Machine Learning for Developing a Biomarker Panel to Distinguish the Severity of Metabolic-Associated Fatty Liver Disease.
Biomedical chromatography : BMC
Jul 1, 2025
Metabolic-associated fatty liver disease (MAFLD), a global health challenge linked to metabolic syndrome, requires accurate severity stratification for clinical management. Current invasive diagnostic methods limit practical implementation. This stud...
Liver Fat Fraction and Machine Learning Improve Steatohepatitis Diagnosis in Liver Transplant Patients.
NMR in biomedicine
Jul 1, 2025
Machine learning identifies liver fat fraction (FF) measured by H MR spectroscopy, insulinemia, and elastography as robust, non-invasive biomarkers for diagnosing steatohepatitis in liver transplant patients, validated through decision tree analysis....
Deep Learning Reveals Liver MRI Features Associated With PNPLA3 I148M in Steatotic Liver Disease.
Liver international : official journal of the International Association for the Study of the Liver
Jul 1, 2025
BACKGROUND: Steatotic liver disease (SLD) is the most common liver disease worldwide, affecting 30% of the global population. It is strongly associated with the interplay of genetic and lifestyle-related risk factors. The genetic variant accounting f...
Volatomics for Diagnosis and Risk Stratification of MASLD: A Proof-Of-Concept Study.
Alimentary pharmacology & therapeutics
Jul 1, 2025
BACKGROUND AND AIMS: Human breath contains numerous volatile organic compounds (VOCs) produced by physiological and metabolic processes or perturbed in pathological states. Electronic nose (eNose) technology has been extensively validated as a non-in...
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