• Machine learning
• Metabolic dysfunction-associated steatotic liver disease
• Prediction model
• SHAP

• Humans
• Machine Learning
• Male
• Female
• Middle Aged
• Adult
• Cross-Sectional Studies
• Fatty Liver/diagnosis
• Fatty Liver/metabolism
• ROC Curve
• Algorithms
• Nutrition Surveys
• Aged
• Risk Factors
• Area Under Curve
• Elasticity Imaging Techniques

• 82160144 National Natural Science Foundation of China
• QKH-ZK[2023]-219 Guizhou Provincial Science and Technology Department
• QKHCG2023-ZD010 Guizhou Science & Technology Department
• Guizhou Science & Technology Department

• algorithms
• computational approaches
• evolutive models
• hepatic steatosis
• predictive parameters
• progression prediction

• NASH
• artificial intelligence
• fatty liver
• liver fibrosis
• mortality

• Male
• Humans
• Female
• Non-alcoholic Fatty Liver Disease/diagnosis
• Non-alcoholic Fatty Liver Disease/epidemiology
• Liver Cirrhosis/complications
• Metabolic Syndrome/complications
• Blood Glucose
• Biopsy
• Fibrosis
• Asia/epidemiology
• Obesity/complications
• Aspartate Aminotransferases
• Liver/pathology

• Humans
• Metabolic Diseases
• Machine Learning
• Non-alcoholic Fatty Liver Disease/diagnosis
• Patients
• Supervised Machine Learning

• Innovative Medicines Initiative 2 Joint Undertaking

• Artificial Intelligence
• Cirrhosis
• Disease Progression
• Risk Stratification

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

• XGBoost
• cardiovascular disease
• diabetes
• machine learning
• models
• obesity
• omics
• personalized nutrition
• random forest

• 3D simulation
• Artificial intelligence
• Liver surgery
• Liver volumetry
• Segmentectomy

• computational models
• cellular imaging

• Artificial Intelligence
• Flow Cytometry
• Humans
• Imaging, Three-Dimensional
• Non-alcoholic Fatty Liver Disease/diagnostic imaging
• Prognosis

• Artificial intelligence
• Big data
• Deep learning
• Hepatology
• Machine learning
• liver diseases

• Artificial Intelligence
• Gastroenterology/methods
• Humans
• Liver Diseases

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

Non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease, affecting over 30% of the United States population. Early patient identification using a simple method is highly desirable.

To create machine learning models for predicting NAFLD in the general United States population.

Using the NHANES 1988-1994. Thirty NAFLD-related factors were included. The dataset was divided into the training (70%) and testing (30%) datasets. Twenty-four machine learning algorithms were applied to the training dataset. The best-performing models and another interpretable model (i.e., coarse trees) were tested using the testing dataset.

There were 3235 participants (n = 3235) that met the inclusion criteria. In the training phase, the ensemble of random undersampling (RUS) boosted trees had the highest F1 (0.53). In the testing phase, we compared selective machine learning models and NAFLD indices. Based on F1, the ensemble of RUS boosted trees remained the top performer (accuracy 71.1% and F1 0.56) followed by the fatty liver index (accuracy 68.8% and F1 0.52). A simple model (coarse trees) had an accuracy of 74.9% and an F1 of 0.33.

Not every machine learning model is complex. Using a simpler model such as coarse trees, we can create an interpretable model for predicting NAFLD with only two predictors: fasting C-peptide and waist circumference. Although the simpler model does not have the best performance, its simplicity is useful in clinical practice.

• Artificial intelligence
• Machine learning
• Non-alcoholic fatty liver disease
• Fatty liver
• United States population
• NHANES

Non-Alcoholic Fatty Liver Disease (NAFLD) affects about 20–30% of the adult population in developed countries and is an increasingly important cause of hepatocellular carcinoma. Liver ultrasound (US) is widely used as a noninvasive method to diagnose NAFLD. However, the intensive use of US is not cost-effective and increases the burden on the healthcare system. Electronic medical records facilitate large-scale epidemiological studies and, existing NAFLD scores often require clinical and anthropometric parameters that may not be captured in those databases. Our goal was to develop and validate a simple Neural Network (NN)-based web app that could be used to predict NAFLD particularly its absence. The study included 2970 subjects; training and testing of the neural network using a train–test-split approach was done on 2869 of them. From another population consisting of 2301 subjects, a further 100 subjects were randomly extracted to test the web app. A search was made to find the best parameters for the NN and then this NN was exported for incorporation into a local web app. The percentage of accuracy, area under the ROC curve, confusion matrix, Positive (PPV) and Negative Predicted Value (NPV) values, precision, recall and f1-score were verified. After that, Explainability (XAI) was analyzed to understand the diagnostic reasoning of the NN. Finally, in the local web app, the specificity and sensitivity values were checked. The NN achieved a percentage of accuracy during testing of 77.0%, with an area under the ROC curve value of 0.82. Thus, in the web app the NN evidenced to achieve good results, with a specificity of 1.00 and sensitivity of 0.73. The described approach can be used to support NAFLD diagnosis, reducing healthcare costs. The NN-based web app is easy to apply and the required parameters are easily found in healthcare databases.

Background. Hepatocellular carcinoma (HCC) is most commonly considered as a complication of cirrhosis. However, an increasing number of HCC in subjects with non-alcoholic fatty liver disease (NAFLD) without cirrhosis is being reported. We conducted a meta-analysis to assess the prevalence of the absence of cirrhosis in NAFLD-associated HCC. Methods. Four databases were searched until March 2021 (CRD42021242969). The original articles included were those reporting data on the presence or absence of cirrhosis among at least 50 subjects with NAFLD-associated HCC. The number of subjects with absent cirrhosis in each study was extracted. For statistical pooling of data, a random-effects model was used. Subgroup analyses according to the continent, target condition and reference standard for the diagnosis of cirrhosis were conducted. Results. Thirty studies were included, evaluating 13,371 subjects with NAFLD-associated HCC. The overall prevalence of cases without cirrhosis was 37% (95%CI 28 to 46). A higher prevalence was reported in Asia versus Europe, North America and South America (45, 36, 37 and 22%, respectively) as well as in studies adopting histology only as the reference standard for the diagnosis of cirrhosis versus histology and other modalities (e.g., radiology, endoscopy, biochemistry or overt clinical findings) (53 and 27%, respectively). No difference was found between studies including subjects with non-alcoholic steatohepatitis (NASH) only, versus NAFLD with or without NASH (p = 0.385). One in three subjects with NAFLD-associated HCC presented without cirrhosis. This should be reflected in future guidelines and surveillance programs adapted to allow for the early detection of these cancers too.

• cirrhosis
• hepatocellular carcinoma
• meta-analysis
• non-alcoholic fatty liver disease
• non-alcoholic steatohepatitis

• Noninvasive tests
• advanced liver fibrosis
• cirrhosis
• diagnostic biomarker
• liver disease
• machine learning
• nonalcoholic fatty liver disease
• nonalcoholic steatohepatitis
• test accuracy

Hepatic biopsy is the gold standard for staging nonalcoholic fatty liver disease (NAFLD). Unfortunately, accessing the liver is invasive, requires a multidisciplinary team and is too expensive to be conducted on large segments of the population. NAFLD starts quietly and can progress until liver damage is irreversible. Given this complex situation, the search for noninvasive alternatives is clinically important. A hallmark of NAFLD progression is the dysregulation in lipid metabolism. In this context, recent advances in the area of machine learning have increased the interest in evaluating whether multi-omics data analysis performed on peripheral blood can enhance human interpretation. In the present review, we show how the use of machine learning can identify sets of lipids as predictive biomarkers of NAFLD progression. This approach could potentially help clinicians to improve the diagnosis accuracy and predict the future risk of the disease. While NAFLD has no effective treatment yet, the key to slowing the progression of the disease may lie in predictive robust biomarkers. Hence, to detect this disease as soon as possible, the use of computational science can help us to make a more accurate and reliable diagnosis. We aimed to provide a general overview for all readers interested in implementing these methods.

• NASH
• adipose tissue
• artificial intelligence
• bariatric surgery
• deep learning
• metabolism