Viceconti N, Andaloro S, Paratore M, Miliani S, D’Acunzo G, Cerniglia G, Mancuso F, Melita E, Gasbarrini A, Riccardi L, Garcovich M. Harnessing artificial intelligence for the assessment of liver fibrosis and steatosis via multiparametric ultrasound. World J Gastroenterol 2026; 32(2): 113059 [DOI: 10.3748/wjg.v32.i2.113059]
Corresponding Author of This Article
Mattia Paratore, MD, Doctor, Department of Medical and Surgical Sciences, Diagnostic and Interventional Ultrasound Unit, CEMAD Digestive Disease Center, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Largo Agostino Gemelli, 8, Rome 00168, Italy. mattia.paratore@guest.policlinicogemelli.it
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Gastroenterology & Hepatology
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Review
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This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Jan 14, 2026 (publication date) through Jan 12, 2026
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Publication Name
World Journal of Gastroenterology
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1007-9327
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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
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Viceconti N, Andaloro S, Paratore M, Miliani S, D’Acunzo G, Cerniglia G, Mancuso F, Melita E, Gasbarrini A, Riccardi L, Garcovich M. Harnessing artificial intelligence for the assessment of liver fibrosis and steatosis via multiparametric ultrasound. World J Gastroenterol 2026; 32(2): 113059 [DOI: 10.3748/wjg.v32.i2.113059]
World J Gastroenterol. Jan 14, 2026; 32(2): 113059 Published online Jan 14, 2026. doi: 10.3748/wjg.v32.i2.113059
Harnessing artificial intelligence for the assessment of liver fibrosis and steatosis via multiparametric ultrasound
Nicholas Viceconti, Silvia Andaloro, Mattia Paratore, Sara Miliani, Giulia D’Acunzo, Giuseppe Cerniglia, Fabrizio Mancuso, Elena Melita, Antonio Gasbarrini, Laura Riccardi, Matteo Garcovich
Nicholas Viceconti, Silvia Andaloro, Mattia Paratore, Sara Miliani, Giulia D’Acunzo, Giuseppe Cerniglia, Fabrizio Mancuso, Elena Melita, Laura Riccardi, Matteo Garcovich, Department of Medical and Surgical Sciences, Diagnostic and Interventional Ultrasound Unit, CEMAD Digestive Disease Center, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome 00168, Italy
Antonio Gasbarrini, Department of Medical and Surgical Sciences, Internal Medicine and Gastroenterology Unit, CEMAD Digestive Disease Center, Fondazione Policlinico Universitario Gemelli IRCCS, Rome 00168, Italy
Antonio Gasbarrini, Department of Translational Medicine and Surgery, Catholic University of the Sacred Heart, Fondazione Policlinico Universitario Gemelli IRCCS, Rome 00168, Italy
Co-first authors: Nicholas Viceconti and Silvia Andaloro.
Author contributions: Viceconti N and Andaloro S contributed equally to this work in conceptualizing, designing and writing the first draft; Paratore M, Riccardi L and Garcovich M conceptualized, designed, supervised and made critical revisions; Viceconti N, Andaloro S, Paratore M, Miliani S, D’Acunzo G, Cerniglia G, Mancuso F, Melita E, Gasbarrini A, Riccardi L, and Garcovich M prepared the draft and approved the submitted version.
Conflict-of-interest statement: The authors declare that they have no conflict of interest.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Mattia Paratore, MD, Doctor, Department of Medical and Surgical Sciences, Diagnostic and Interventional Ultrasound Unit, CEMAD Digestive Disease Center, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Largo Agostino Gemelli, 8, Rome 00168, Italy. mattia.paratore@guest.policlinicogemelli.it
Received: August 14, 2025 Revised: November 4, 2025 Accepted: December 2, 2025 Published online: January 14, 2026 Processing time: 151 Days and 17.6 Hours
Abstract
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.
Core Tip: The emergence of artificial intelligence has led to its application across various fields, including hepatology and medical imaging. Its enormous potential has already been recognized and documented in numerous studies. This review explores the current application and future potential of artificial intelligence in ultrasound imaging, emphasizing its role in chronic liver disease early diagnosis and follow-up.
