Harnessing artificial intelligence for the assessment of liver fibrosis and steatosis via multiparametric ultrasound

Artificial intelligence (AI) is revolutionizing medical imaging, particularly in chronic liver diseases assessment. AI technologies, including machine learning and deep learning, are increasingly integrated with multiparametric ultrasound (US) techniques to provide more accurate, objective, and non-invasive evaluations of liver fibrosis and steatosis. Analyzing large datasets from US images, AI enhances diagnostic precision, enabling better quantification of liver stiffness and fat content, which are essential for diagnosing and staging liver fibrosis and steatosis. Combining advanced US modalities, such as elastography and doppler imaging with AI, has demonstrated improved sensitivity in identifying different stages of liver disease and distinguishing various degrees of steatotic liver. These advancements also contribute to greater reproducibility and reduced operator dependency, addressing some of the limitations of traditional methods. The clinical implications of AI in liver disease are vast, ranging from early detection to predicting disease progression and evaluating treatment response. Despite these promising developments, challenges such as the need for large-scale datasets, algorithm transparency, and clinical validation remain. The aim of this review is to explore the current applications and future potential of AI in liver fibrosis and steatosis assessment using multiparametric US, highlighting the technological advances and clinical relevance of this emerging field.