Evolving B-mode ultrasound-based techniques for assessing metabolic dysfunction-associated steatotic liver disease: Now and beyond
10.1016/j.livres.2026.05.001
- Author:
Walaa Abdelhamed
;
Mohamed Elbadry
;
Mohamed El-Kassas
- Publication Type:Review
- From:
Liver Research
2026;10(2):109-120
- CountryChina
- Language:English
-
Abstract:
Metabolic dysfunction-associated steatotic liver disease (MASLD) has emerged as the leading cause of chronic liver disease worldwide, coinciding with the growing burden of obesity and type 2 diabetes mellitus. While liver biopsy remains the gold standard for assessing hepatic steatosis and fibrosis, its invasiveness, sampling variability, and limited feasibility have necessitated the establishment of non-invasive diagnostic alternatives. Among non-invasive alternatives, conventional B-mode ultrasound (US) has retained a central role as the first-line imaging modality owing to its wide availability, low cost, and reasonable sensitivity and specificity, particularly in moderate-to-severe steatosis. However, traditional B-mode US has several limitations, including operator dependence, poor sensitivity in mild steatosis, and reduced accuracy in obese individuals. Semi-quantitative scoring systems and emerging technologies such as attenuation imaging, shear wave elastography, and vibration-controlled transient elastography, have been recently introduced to improve diagnostic accuracy. Additionally, artificial intelligence (AI) is increasingly being integrated into US platforms to enhance image interpretation, standardize assessments, and reduce interobserver variability. This review provides a comprehensive appraisal of the diagnostic performance, strengths, and limitations of conventional B-mode US and its advanced products in the context of MASLD. US-based techniques are also compared with magnetic resonance spectroscopy and histological assessment, highlighting the evolving role of AI in US diagnostics. Given the global rise of MASLD, optimizing and standardizing US-based approaches are essential to improve early detection, risk stratification, and monitoring strategies. With continued technological refinement and integration of AI, US remains a cornerstone of MASLD diagnosis in clinical practice.
