AIMC Topic: Non-alcoholic Fatty Liver Disease

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Metabolic syndrome, fatty liver, and artificial intelligence-based epicardial adipose tissue measures predict long-term risk of cardiac events: a prospective study.

Cardiovascular diabetology Jan 29, 2021
BACKGROUND: We sought to evaluate the association of metabolic syndrome (MetS) and computed tomography (CT)-derived cardiometabolic biomarkers (non-alcoholic fatty liver disease [NAFLD] and epicardial adipose tissue [EAT] measures) with long-term ris...
Risk Assessment Prevalence Deep Learning Time Factors Metabolic Syndrome Female Aged Aged, 80 and over Male Middle Aged Los Angeles Heart Diseases Adiposity Pericardium Cardiometabolic Risk Factors Adipose Tissue Predictive Value of Tests Humans Radiographic Image Interpretation, Computer-Assisted Prospective Studies Registries Prognosis Non-alcoholic Fatty Liver Disease Tomography, X-Ray Computed
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Use of a convolutional neural network and quantitative ultrasound for diagnosis of fatty liver.

Ultrasound in medicine & biology Dec 25, 2020
Quantitative ultrasound (QUS) was used to classify rabbits that were induced to have liver disease by placing them on a fatty diet for a defined duration and/or periodically injecting them with CCl. The ground truth of the liver state was based on li...
Liver Rabbits Non-alcoholic Fatty Liver Disease Dietary Fats Neural Networks, Computer Machine Learning Animals Male Ultrasonography Fatty Liver
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A large-scale internal validation study of unsupervised virtual trichrome staining technologies on nonalcoholic steatohepatitis liver biopsies.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc Dec 9, 2020
Non-alcoholic steatohepatitis (NASH) is a fatty liver disease characterized by accumulation of fat in hepatocytes with concurrent inflammation and is associated with morbidity, cirrhosis and liver failure. After extraction of a liver core biopsy, tis...
Humans Neural Networks, Computer Software Decision Support Techniques Retrospective Studies Young Adult Adult Male Middle Aged Severity of Illness Index Clinical Decision-Making Female Reproducibility of Results Aged Aged, 80 and over Microscopy Azo Compounds Image Interpretation, Computer-Assisted Eosine Yellowish-(YS) Hematoxylin Liver Methyl Green Predictive Value of Tests Biopsy Non-alcoholic Fatty Liver Disease Coloring Agents Adolescent Child Staining and Labeling Liver Cirrhosis
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Type IV Collagen 7S Is the Most Accurate Test For Identifying Advanced Fibrosis in NAFLD With Type 2 Diabetes.

Hepatology communications Nov 16, 2020
This study aimed to examine whether the diagnostic accuracy of four noninvasive tests (NITs) for detecting advanced fibrosis in nonalcoholic fatty liver disease (NAFLD) is maintained or is inferior to with or without the presence of type 2 diabetes. ...
Adolescent Non-alcoholic Fatty Liver Disease Humans Young Adult ROC Curve Female Collagen Type IV Adult Diabetes Mellitus, Type 2 Dyslipidemias Cross-Sectional Studies Aged Liver Cirrhosis Platelet Count Male Aged, 80 and over Age Factors Middle Aged
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Selecting the best machine learning algorithm to support the diagnosis of Non-Alcoholic Fatty Liver Disease: A meta learner study.

PloS one Oct 20, 2020
BACKGROUND & AIMS: Liver ultrasound scan (US) use in diagnosing Non-Alcoholic Fatty Liver Disease (NAFLD) causes costs and waiting lists overloads. We aimed to compare various Machine learning algorithms with a Meta learner approach to find the best ...
Adult Cross-Sectional Studies Humans Male Ultrasonography Machine Learning Middle Aged Age Factors Algorithms Child, Preschool Glucose Female Non-alcoholic Fatty Liver Disease Risk Factors Sex Factors
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Development of a QSAR model to predict hepatic steatosis using freely available machine learning tools.

Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association Jun 14, 2020
There are various types of hepatic steatosis of which non-alcoholic fatty liver disease, which may be caused by exposure to chemicals and environmental pollutants is the most prevalent, representing a potential major health risk. QSAR modelling has t...
Quantitative Structure-Activity Relationship Humans Machine Learning Algorithms Environmental Pollutants Non-alcoholic Fatty Liver Disease
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Utilization of a Deep Learning Algorithm for Microscope-Based Fatty Vacuole Quantification in a Fatty Liver Model in Mice.

Toxicologic pathology Jun 8, 2020
Quantification of fatty vacuoles in the liver, with differentiation from lumina of liver blood vessels and bile ducts, is an example where the traditional semiquantitative pathology assessment can be enhanced with artificial intelligence (AI) algorit...
Non-alcoholic Fatty Liver Disease Microscopy Image Interpretation, Computer-Assisted Vacuoles Animals Deep Learning Mice Disease Models, Animal Liver Artificial Intelligence Machine Learning Algorithms
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Automated quantification and architectural pattern detection of hepatic fibrosis in NAFLD.

Annals of diagnostic pathology Apr 12, 2020
Accurate detection and quantification of hepatic fibrosis remain essential for assessing the severity of non-alcoholic fatty liver disease (NAFLD) and its response to therapy in clinical practice and research studies. Our aim was to develop an integr...
Humans Artificial Intelligence Severity of Illness Index Image Processing, Computer-Assisted Liver Cirrhosis Practice Patterns, Physicians' Clinical Trials as Topic Fibrosis Organ Dysfunction Scores Azo Compounds Eosine Yellowish-(YS) Collagen Liver Methyl Green Automation Biopsy Portal Vein Pathologists Supervised Machine Learning Non-alcoholic Fatty Liver Disease
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Fatty Liver Disease: Artificial Intelligence Takes on the Challenge.

Radiology Feb 25, 2020
Humans Neural Networks, Computer Artificial Intelligence Ultrasonography Non-alcoholic Fatty Liver Disease
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Noninvasive Diagnosis of Nonalcoholic Fatty Liver Disease and Quantification of Liver Fat with Radiofrequency Ultrasound Data Using One-dimensional Convolutional Neural Networks.

Radiology Feb 25, 2020
Background Radiofrequency ultrasound data from the liver contain rich information about liver microstructure and composition. Deep learning might exploit such information to assess nonalcoholic fatty liver disease (NAFLD). Purpose To develop and eval...
Magnetic Resonance Imaging Predictive Value of Tests Non-alcoholic Fatty Liver Disease Random Allocation Radio Waves Sensitivity and Specificity Male Middle Aged Ultrasonography Image Processing, Computer-Assisted Humans Neural Networks, Computer Prospective Studies Female
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