• Humans
• Artificial Intelligence
• Prognosis
• Liver Diseases, Alcoholic/diagnosis
• Liver Diseases, Alcoholic/therapy
• Alcoholism/diagnosis

• ZIA DA000628 Intramural NIH HHS

• MASLD/NAFLD
• chronic viral hepatitides
• chronobiology
• history of hepatology
• liver aging
• liver fibrosis/cirrhosis
• liver transplantation
• liver–microbiome axis
• organokine crosstalk

• MELD
• alcohol
• alcohol-associated liver disease
• glucocorticoids
• inflammation
• liver transplantation

• Humans
• Hepatitis, Alcoholic/therapy
• Pandemics
• COVID-19
• Liver Diseases, Alcoholic

• Humans
• Hyaluronic Acid/therapeutic use
• Hyaluronic Acid/metabolism
• Liver Cirrhosis/drug therapy
• Liver Cirrhosis/metabolism
• Fibrosis
• Extracellular Matrix
• Disease Progression

• P01 CA233452 NCI NIH HHS
• R01 AA027036 NIAAA NIH HHS
• R01 DK085252 NIDDK NIH HHS

• Imbalanced dataset
• Liver fibrosis
• Machine learning
• NHANES
• Oversampling techniques

• Artificial intelligence
• Deep learning
• Liver diseases
• Liver neoplasms
• Ultrasonography

• ACLF, acute on chronic liver failure
• AI, artificial intelligence
• ALD, alcoholic liver disease
• ALT, alanine transaminase
• ANN, artificial neural network
• AST, aspartate aminotransferase
• AUD, alcohol use disorder
• CHB, chronic hepatitis B
• CHC, chronic hepatitis C
• CLD, chronic liver disease
• CNN, convolutional neural network
• DL, deep learning
• FIB-4, fibrosis-4 score
• GGTP, gamma glutamyl transferase
• HCC, hepatocellular carcinoma
• HDL, high density lipoprotein
• ML, machine learning
• MLR, multi-nomial logistic regressions
• NAFLD
• NAFLD, non-alcoholic fatty liver disease
• NASH, non-alcoholic steatohepatitis
• NLP, natural language processing
• RF, random forest
• RTE, real-time tissue elastography
• SOLs, space-occupying lesions
• SVM, support vector machine
• artificial intelligence
• deep learning
• hepatology
• machine learning

Hepatic cirrhosis is a major health problem across the world, causing high morbidity and mortality. This disease has many etiologies, yet the result of chronic hepatic injury is hepatic fibrosis causing cirrhosis and hepatocellular carcinoma, as the liver’s architecture is progressively destroyed. While liver biopsy is currently the gold standard for fibrosis staging, it has significant disadvantages, leading to a growing interest in non-invasive markers. Direct biomarkers – hyaluronic acid, laminin, collagen type III N-peptide, type IV collagen and cholylglycine – are new and rarely applied in routine clinical practice. This is the case primarily because there is no general consensus regarding the clinical application and effectiveness of the individual biomarkers. The usage of these markers in routine clinical practice could be advantageous for patients with liver fibrosis, requiring a simple blood test instead of a biopsy. The former option would be especially attractive for patients who are contraindicated for the latter. This review summarizes recent findings on direct biomarkers of liver fibrosis and highlights their possible applications and potential benefit for liver fibrosis diagnostics and/or staging.

• AC, alcohol-associated cirrhosis
• AH, alcohol-associated hepatitis
• AKR1B10, aldo-keto reductase family 1 member B10
• BTM, blood transcription module
• Classification
• DE, differential expression
• FPKM, fragments per kilobase of exon model per million reads mapped
• GSEA, gene set-enrichment analysis
• IG, information gain
• IPA, Ingenuity Pathway Analysis
• LR, logistic regression
• LTCDS, liver tissue cell distribution system
• LV, liver tissue
• ML, machine learning
• MMP, matrix metalloproteases
• NAFLD, non-alcohol-associated fatty liver disease
• PBMCs, peripheral blood mononuclear cells
• RNA sequencing
• RNA-seq, RNA sequencing
• SCAHC, Southern California Alcoholic Hepatitis Consortium
• SVM, support vector machine
• TNF, tumor necrosis factor
• alcohol-associated liver disease
• biomarker discovery
• kNN, k-nearest neighbors

• Adipokines
• Algorithm
• CVD
• Cardiokines
• Hepatokines

• Adipokines
• Cardiovascular Diseases/epidemiology
• Cardiovascular Diseases/etiology
• Cytokines
• Humans
• Non-alcoholic Fatty Liver Disease/complications
• Risk Factors

The integration of artificial intelligence (AI) and augmented realities into the medical field is being attempted by various researchers across the globe. As a matter of fact, most of the advanced technologies utilized by medical providers today have been borrowed and extrapolated from other industries. The introduction of AI into the field of hepatology and liver surgery is relatively a recent phenomenon. The purpose of this narrative review is to highlight the different AI concepts which are currently being tried to improve the care of patients with liver diseases. We end with summarizing emerging trends and major challenges in the future development of AI in hepatology and liver surgery.

• Liver disease
• Machine learning
• Deep learning
• Artificial neural networks
• Transplantation
• Hepatectomy

• Clinical data
• Data augmentation
• Machine learning
• Synthetic data

The development of artificial intelligence (AI) has increased dramatically in the last 20 years, with clinical applications progressively being explored for most of the medical specialties. The field of gastroenterology and hepatology, substantially reliant on vast amounts of imaging studies, is not an exception. The clinical applications of AI systems in this field include the identification of premalignant or malignant lesions (e.g., identification of dysplasia or esophageal adenocarcinoma in Barrett’s esophagus, pancreatic malignancies), detection of lesions (e.g., polyp identification and classification, small-bowel bleeding lesion on capsule endoscopy, pancreatic cystic lesions), development of objective scoring systems for risk stratification, predicting disease prognosis or treatment response [e.g., determining survival in patients post-resection of hepatocellular carcinoma), determining which patients with inflammatory bowel disease (IBD) will benefit from biologic therapy], or evaluation of metrics such as bowel preparation score or quality of endoscopic examination. The objective of this comprehensive review is to analyze the available AI-related studies pertaining to the entirety of the gastrointestinal tract, including the upper, middle and lower tracts; IBD; the hepatobiliary system; and the pancreas, discussing the findings and clinical applications, as well as outlining the current limitations and future directions in this field.

• Artificial intelligence
• Machine learning
• Deep learning
• Clinical applications
• Gastroenterology

• Artificial Intelligence
• Barrett Esophagus
• Diagnostic Imaging
• Endoscopy
• Gastroenterology
• Humans

Artificial intelligence (AI) is an umbrella term used to describe a cluster of interrelated fields. Machine learning (ML) refers to a model that learns from past data to predict future data. Medicine and particularly gastroenterology and hepatology, are data-rich fields with extensive data repositories, and therefore fruitful ground for AI/ML-based software applications. In this study, we comprehensively review the current applications of AI/ML-based models in these fields and the opportunities that arise from their application. Specifically, we refer to the applications of AI/ML-based models in prevention, diagnosis, management, and prognosis of gastrointestinal bleeding, inflammatory bowel diseases, gastrointestinal premalignant and malignant lesions, other nonmalignant gastrointestinal lesions and diseases, hepatitis B and C infection, chronic liver diseases, hepatocellular carcinoma, cholangiocarcinoma, and primary sclerosing cholangitis. At the same time, we identify the major challenges that restrain the widespread use of these models in healthcare in an effort to explore ways to overcome them. Notably, we elaborate on the concerns regarding intrinsic biases, data protection, cybersecurity, intellectual property, liability, ethical challenges, and transparency. Even at a slower pace than anticipated, AI is infiltrating the healthcare industry. AI in healthcare will become a reality, and every physician will have to engage with it by necessity.

• Artificial intelligence
• Machine learning
• Gastroenterology
• Hepatology
• Artificial neural networks
• Support vector machine

• Artificial Intelligence
• Gastroenterology
• Humans
• Liver Diseases
• Machine Learning
• Prognosis

• deep learning
• endoscopy
• machine learning
• radiology
• ultrasonography

• 21K15951 Japan Society for the Promotion of Science

• Artificial Intelligence
• Deep Learning
• Gastroenterology
• Hepatic disease
• Machine Learning
• Pancreatic adenocarcinoma

• Bayes Theorem
• Calibration
• Clinical Decision-Making
• Machine Learning
• Probability

• Alcohol
• Fatty liver
• Metabolic syndrome

• liver enzymes
• non-invasive prediction
• percutaneous coronary intervention
• troponin

• Deep Learning
• Digestive System
• Machine Learning
• Neural Network

• Cardiovascular risk
• LASSO regression
• Machine learning
• Pulse wave velocity

• Artificial intelligence
• Deep learning
• Diabetes
• Diagnostics
• E-health
• Machine learning

• Alcoholic hepatitis
• Behavioural research
• Liver disease
• Qualitative research

• Raman micro-spectroscopic imaging
• biochemical component analysis
• model fitting
• non-alcoholic fatty liver disease
• non-alcoholic steatohepatitis

• Animals
• Female
• Image Processing, Computer-Assisted
• Mice
• Mice, Inbred C57BL
• Microscopy
• Non-alcoholic Fatty Liver Disease/pathology
• Pregnancy
• Spectrum Analysis, Raman

• P41 EB015871 NIBIB NIH HHS

• signal transduction
• alcoholic liver disease

• Animals
• Ethanol/toxicity
• Gene Expression Regulation/drug effects
• Humans
• Hyaluronic Acid/administration & dosage
• Hyaluronic Acid/metabolism
• Intracellular Signaling Peptides and Proteins/genetics
• Kupffer Cells/drug effects
• Kupffer Cells/metabolism
• Liver Diseases, Alcoholic/drug therapy
• Liver Diseases, Alcoholic/genetics
• Liver Diseases, Alcoholic/pathology
• Macrophages/metabolism
• MicroRNAs/genetics
• Rats
• Signal Transduction/drug effects
• Toll-Like Receptor 4/genetics
• Tumor Necrosis Factor-alpha/metabolism

• UL1 RR024989 NCRR NIH HHS
• UL1 TR002548 NCATS NIH HHS
• P01 HL107147 NHLBI NIH HHS
• K99 AA025386 NIAAA NIH HHS
• R21 AA024387 NIAAA NIH HHS
• S10 RR026820 NCRR NIH HHS
• UM1 TR004528 NCATS NIH HHS
• P50 AA024333 NIAAA NIH HHS
• F31 AA024017 NIAAA NIH HHS
• S10 OD018058 NIH HHS
• S10 OD018058 ODCDC CDC HHS
• R00 AA025386 NIAAA NIH HHS
• P30 DK097948 NIDDK NIH HHS
• U01 AA021890 NIAAA NIH HHS

• Bayesian
• Calibration
• Machine learning
• Nonparametric
• Statistics

• Adolescent
• Bayes Theorem
• Calibration
• Decision Support Systems, Clinical/standards
• Humans
• Machine Learning
• Statistics, Nonparametric
• Suicide
• Support Vector Machine

• Ensemble learning
• Feature selection
• Machine learning
• R-package

• Alcohol-Dependent Subjects
• Endotoxemia
• Mild Alcoholic Liver Disease

Molecular signatures identified from high-throughput transcriptomic studies often have poor reliability and fail to reproduce across studies. One solution is to combine independent studies into a single integrative analysis, additionally increasing sample size. However, the different protocols and technological platforms across transcriptomic studies produce unwanted systematic variation that strongly confounds the integrative analysis results. When studies aim to discriminate an outcome of interest, the common approach is a sequential two-step procedure; unwanted systematic variation removal techniques are applied prior to classification methods.

To limit the risk of overfitting and over-optimistic results of a two-step procedure, we developed a novel multivariate integration method, MINT, that simultaneously accounts for unwanted systematic variation and identifies predictive gene signatures with greater reproducibility and accuracy. In two biological examples on the classification of three human cell types and four subtypes of breast cancer, we combined high-dimensional microarray and RNA-seq data sets and MINT identified highly reproducible and relevant gene signatures predictive of a given phenotype. MINT led to superior classification and prediction accuracy compared to the existing sequential two-step procedures.

MINT is a powerful approach and the first of its kind to solve the integrative classification framework in a single step by combining multiple independent studies. MINT is computationally fast as part of the mixOmics R CRAN package, available at http://www.mixOmics.org/mixMINT/and http://cran.r-project.org/web/packages/mixOmics/.

The online version of this article (doi:10.1186/s12859-017-1553-8) contains supplementary material, which is available to authorized users.

• Algorithm
• Classification
• Integration
• Multivariate
• Partial-least-square
• Transcriptome analysis

• Biomarker discovery
• Ensemble learning
• Feature selection
• Machine learning
• Random forest

Chronic liver diseases represent a significant public health problem worldwide. The degree of liver fibrosis secondary to these diseases is important, because it is the main predictor of their evolution and prognosis.

Hyaluronic acid is studied as a non-invasive marker of liver fibrosis in chronic liver diseases, in an attempt to avoid the complications of liver puncture biopsy, considered the gold standard in the evaluation of fibrosis. We review the advantages and limitations of hyaluronc acid, a biomarker, used to manage patients with chronic viral hepatitis B or C infection, non-alcoholic fatty liver disease, HIV-HCV coinfection, alcoholic liver disease, primary biliary cirrhosis, biliary atresia, hereditary hemochromatosis and cystic fibrosis.

• chronic liver diseases
• hyaluronic acid
• liver fibrosis

The present review spans a broad spectrum of topics dealing with alcoholic liver disease (ALD), including clinical and translational research. It focuses on the role of the immune system and the signaling pathways of cytokines in the pathogenesis of ALD. An additional factor that contributes to the pathogenesis of ALD is lipopolysaccharide (LPS), which plays a central role in the induction of steatosis, inflammation, and fibrosis in the liver. LPS derived from the intestinal microbiota enters the portal circulation, and is recognized by macrophages (Kupffer cells) and hepatocytes. In individuals with ALD, excessive levels of LPS in the liver affect immune, parenchymal, and non-immune cells, which in turn release various inflammatory cytokines and recruit neutrophils and other inflammatory cells. In this review, we elucidate the mechanisms by which alcohol contributes to the activation of Kupffer cells and the inflammatory cascade. The role of the stellate cells in fibrogenesis is also discussed.

• Toll-like receptor
• alcoholic liver disease
• hepatocellular carcinoma
• lipopolysaccharide
• transforming growth factor
• tumor necrosis factor

• Alcoholism/metabolism
• Cytokines/metabolism
• Humans
• Lipopolysaccharides/metabolism
• Liver Diseases, Alcoholic/metabolism

Key features of the metabolic syndrome are insulin resistance and diabetes. The liver as central metabolic organ is not only affected by the metabolic syndrome as non-alcoholic fatty liver disease (NAFLD), but may contribute to insulin resistance and metabolic alterations. We aimed to identify potential associations between liver injury markers and diabetes in the population-based Heinz Nixdorf RECALL Study. Demographic and laboratory data were analyzed in participants (n = 4814, age 45 to 75y). ALT and AST values were significantly higher in males than in females. Mean BMI was 27.9 kg/m² and type-2-diabetes (known and unkown) was present in 656 participants (13.7%). Adiponectin and vitamin D both correlated inversely with BMI. ALT, AST, and GGT correlated with BMI, CRP and HbA1c and inversely correlated with adiponectin levels. Logistic regression models using HbA1c and adiponectin or HbA1c and BMI were able to predict diabetes with high accuracy. Transaminase levels within normal ranges were closely associated with the BMI and diabetes risk. Transaminase levels and adiponectin were inversely associated. Re-assessment of current normal range limits should be considered, to provide a more exact indicator for chronic metabolic liver injury, in particular to reflect the situation in diabetic or obese individuals.

• Fibrosis
• Histological activity index
• Hyaluronic acid
• Liver biopsy
• Non-invasive biomarkers

• HIV mono-infection
• hepatic steatosis
• liver fibrosis
• serum biomarkers