AIMC Topic: 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...
Artificial Intelligence Machine Learning Algorithms Animals Non-alcoholic Fatty Liver Disease Deep Learning Mice Microscopy Disease Models, Animal Image Interpretation, Computer-Assisted Liver Vacuoles
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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 Organ Dysfunction Scores Eosine Yellowish-(YS) Methyl Green Fibrosis Azo Compounds Collagen Automation Portal Vein Practice Patterns, Physicians' Clinical Trials as Topic Biopsy Pathologists Supervised Machine Learning Non-alcoholic Fatty Liver Disease Image Processing, Computer-Assisted Liver Cirrhosis Liver
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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...
Middle Aged Ultrasonography Image Processing, Computer-Assisted Magnetic Resonance Imaging Female Sensitivity and Specificity Male Radio Waves Predictive Value of Tests Non-alcoholic Fatty Liver Disease Random Allocation Humans Neural Networks, Computer Prospective Studies
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Deep learning enables pathologist-like scoring of NASH models.

Scientific reports Dec 5, 2019
Non-alcoholic fatty liver disease (NAFLD) and the progressive form of non-alcoholic steatohepatitis (NASH) are diseases of major importance with a high unmet medical need. Efficacy studies on novel compounds to treat NAFLD/NASH using disease models a...
Non-alcoholic Fatty Liver Disease Microscopy Datasets as Topic Liver Animals Biopsy Severity of Illness Index Feasibility Studies Deep Learning Image Processing, Computer-Assisted Mice Rats Disease Models, Animal Humans
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Predictive models with the use of omics and supervised machine learning to diagnose non-alcoholic fatty liver disease: A "non-invasive alternative" to liver biopsy?

Metabolism: clinical and experimental Nov 9, 2019
Biopsy Supervised Machine Learning Non-alcoholic Fatty Liver Disease Humans Metabolomics
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Non-invasive diagnosis of non-alcoholic steatohepatitis and fibrosis with the use of omics and supervised learning: A proof of concept study.

Metabolism: clinical and experimental Nov 9, 2019
BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) affects 25-30% of the general population and is characterized by the presence of non-alcoholic fatty liver (NAFL) that can progress to non-alcoholic steatohepatitis (NASH), liver fibrosis and cirr...
Female Male Middle Aged Case-Control Studies Fatty Liver Liver Cirrhosis Metabolomics Humans Non-alcoholic Fatty Liver Disease Fatty Acids Proof of Concept Study Lipids Polysaccharides Supervised Machine Learning
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Automated Liver Fat Quantification at Nonenhanced Abdominal CT for Population-based Steatosis Assessment.

Radiology Sep 17, 2019
Background Nonalcoholic fatty liver disease and its consequences are a growing public health concern requiring cross-sectional imaging for noninvasive diagnosis and quantification of liver fat. Purpose To investigate a deep learning-based automated l...
Humans Retrospective Studies Middle Aged Tomography, X-Ray Computed Radiography, Abdominal Prevalence Female Deep Learning Male Non-alcoholic Fatty Liver Disease
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Raman spectroscopic histology using machine learning for nonalcoholic fatty liver disease.

FEBS letters Jul 8, 2019
Histopathology requires the expertise of specialists to diagnose morphological features of cells and tissues. Raman imaging can provide additional biochemical information to benefit histological disease diagnosis. Using a dietary model of nonalcoholi...
Non-alcoholic Fatty Liver Disease Signal-To-Noise Ratio Spectrum Analysis, Raman Rats, Sprague-Dawley Liver Machine Learning Animals Male Rats
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Augmented intelligence with natural language processing applied to electronic health records for identifying patients with non-alcoholic fatty liver disease at risk for disease progression.

International journal of medical informatics Jul 6, 2019
OBJECTIVE: Electronic health record (EHR) systems contain structured data (such as diagnostic codes) and unstructured data (clinical documentation). Clinical insights can be derived from analyzing both. The use of natural language processing (NLP) al...
Cohort Studies Female Natural Language Processing Humans Non-alcoholic Fatty Liver Disease Male Algorithms Middle Aged Electronic Health Records Disease Progression
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