Inflammation is the key driver of liver fibrosis progression in non-alcoholic fatty liver disease (NAFLD). Unfortunately, it is often challenging to assess inflammation in NAFLD due to its dynamic nature and poor correlation with liver biochemical markers. Liver histology keeps its role as the standard tool, yet it is well-known for substantial sampling, intraobserver, and interobserver variability. Serum proinflammatory cytokines and apoptotic markers, namely cytokeratin-18, are well-studied with reasonable accuracy, whereas serum metabolomics and lipidomics have been adopted in some commercially available diagnostic models. Ultrasound and computed tomography imaging techniques are attractive due to their wide availability; yet their accuracies may not be comparable with magnetic resonance imaging-based tools. Machine learning and deep learning models, be they supervised or unsupervised learning, are promising tools to identify various subtypes of NAFLD, including those with dominating liver inflammation, contributing to sustainable care pathways for NAFLD.

In 2023, nonalcoholic fatty liver disease was renamed metabolic dysfunction-associated steatotic liver disease by the American and European liver associations. This new nomenclature recognizes metabolic dysfunction as the central driver of the disease, and the diagnostic criteria now require the presence of hepatic steatosis plus at least one of five cardiometabolic risk factors. B-mode ultrasonography remains the most common and practical method for detecting hepatic steatosis, although newer ultrasound techniques based on attenuation, backscatter, and speed of sound have gained traction as tools to diagnose and quantify hepatic steatosis. Additionally, ultrasound elastography is increasingly used in routine clinical practice to assess liver fibrosis, diagnose cirrhosis, and identify clinically significant portal hypertension.