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

Liver has an ability to regenerate itself in mammals, whereas the mechanism has not been fully explained. Here we used a GC/MS-based metabolomic method to profile the dynamic endogenous metabolic change in the serum of C57BL/6J mice at different times after 2/3 partial hepatectomy (PHx), and nine machine learning methods including Least Absolute Shrinkage and Selection Operator Regression (LASSO), Partial Least Squares Regression (PLS), Principal Components Regression (PCR), k-Nearest Neighbors (KNN), Support Vector Machines (SVM), Random Forest (RF), eXtreme Gradient Boosting (xgbDART), Neural Network (NNET) and Bayesian Regularized Neural Network (BRNN) were used for regression between the liver index and metabolomic data at different stages of liver regeneration. We found a tree-based random forest method that had the minimum average Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and the maximum R square (R²) and is time-saving. Furthermore, variable of importance in the project (VIP) analysis of RF method was performed and metabolites with VIP ranked top 20 were selected as the most critical metabolites contributing to the model. Ornithine, phenylalanine, 2-hydroxybutyric acid, lysine, etc. were chosen as the most important metabolites which had strong correlations with the liver index. Further pathway analysis found Arginine biosynthesis, Pantothenate and CoA biosynthesis, Galactose metabolism, Valine, leucine and isoleucine degradation were the most influenced pathways. In summary, several amino acid metabolic pathways and glucose metabolism pathway were dynamically changed during liver regeneration. The RF method showed advantages for predicting the liver index after PHx over other machine learning methods used and a metabolic clock containing four metabolites is established to predict the liver index during liver regeneration.

• GC/MS
• liver regeneration
• machine learning
• metabolomics
• partial hepatectomy

• Drug analysis
• parasitology < Infectious diseases
• pharmacokinetics
• pharmacometrics
• tropical diseases < Infectious diseases

• Hepatomegaly
• Liver
• Liver volume
• Magnetic resonance imaging

• Complications
• Hepatectomy
• Liver failure
• Risk models
• Surgery/mortality

The amount of the future liver remnant volume is fundamental for hepato-biliary surgery, representing an important potential risk-factor for the development of post-hepatectomy liver failure. Despite this, there is no uniform consensus about the amount of hepatic parenchyma that can be safely resected, nor about the modality that should be chosen for this evaluation. The pre-operative evaluation of hepatic volume, along with a precise identification of vascular and biliar anatomy and variants, are therefore necessary to reduce surgical complications, especially for extensive resections. Some studies have tried to validate imaging methods [ultrasound, computed tomography (CT), magnetic resonance imaging] for the assessment of liver volume, but there is no clear evidence about the most accurate method for this evaluation. Furthermore, this volumetric evaluation seems to have a certain degree of error, tending to overestimate the actual hepatic volume, therefore some conversion factors, which should give a more reliable evaluation of liver volume, have been proposed. It is widespread among non-radiologists the use of independent software for an off-site volumetric analysis, performed on digital imaging and communications in medicine images with their own personal computer, but very few studies have provided a validation of these methods. Moreover, while the pre-transplantation volumetric assessment is fundamental, it remains unclear whether it should be routinely performed in all patients undergoing liver resection. In this editorial the role of imaging in the estimation of liver volume is discussed, providing a review of the most recent literature and a brief personal series of correlations between liver volumes and resection specimens’ weight, in order to assess the precision of the volumetric CT evaluation.

• Liver
• Hepatectomy
• Ultrasound
• Computed tomography
• Magnetic resonance imaging