Ciocalteu A, Urhut CM, Streba CT, Kamal A, Mamuleanu M, Sandulescu LD. Artificial intelligence in contrast enhanced ultrasound: A new era for liver lesion assessment. World J Gastroenterol 2025; 31(42): 112196 [DOI: 10.3748/wjg.v31.i42.112196]
Corresponding Author of This Article
Adriana Ciocalteu, MD, PhD, Assistant Professor, Department of Gastroenterology, Research Center of Gastroenterology and Hepatology, University of Medicine and Pharmacy of Craiova, Petru Rares 2, Craiova 200349, Romania. adriana_ciocalteu@yahoo.com
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Gastroenterology & Hepatology
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Minireviews
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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/
Nov 14, 2025 (publication date) through Nov 16, 2025
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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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Ciocalteu A, Urhut CM, Streba CT, Kamal A, Mamuleanu M, Sandulescu LD. Artificial intelligence in contrast enhanced ultrasound: A new era for liver lesion assessment. World J Gastroenterol 2025; 31(42): 112196 [DOI: 10.3748/wjg.v31.i42.112196]
World J Gastroenterol. Nov 14, 2025; 31(42): 112196 Published online Nov 14, 2025. doi: 10.3748/wjg.v31.i42.112196
Artificial intelligence in contrast enhanced ultrasound: A new era for liver lesion assessment
Adriana Ciocalteu, Cristiana M Urhut, Costin Teodor Streba, Adina Kamal, Madalin Mamuleanu, Larisa D Sandulescu
Adriana Ciocalteu, Larisa D Sandulescu, Department of Gastroenterology, Research Center of Gastroenterology and Hepatology, University of Medicine and Pharmacy of Craiova, Craiova 200349, Romania
Cristiana M Urhut, Larisa D Sandulescu, Department of Gastroenterology, Emergency County Hospital of Craiova, Craiova 200642, Romania
Costin Teodor Streba, Oncometrics, S.R.L., Craiova 200677, Romania
Costin Teodor Streba, Department of Pulmonology, University of Medicine and Pharmacy of Craiova, Craiova 200349, Romania
Adina Kamal, Department of Internal Medicine, University of Medicine and Pharmacy of Craiova, Craiova 200349, Romania
Madalin Mamuleanu, Department of Automatic Control and Electronics, University of Craiova, Craiova 200585, Romania
Author contributions: Ciocalteu A was responsible for the conception and design of the literature review, manuscript drafting, and performance of all corresponding author responsibilities; Urhuț CM, Kamal A, and Sandulescu LD contributed to the literature review, manuscript drafting, and performance of critical revision for all successive versions; Mămuleanu M and Streba CT provided technical input on the artificial intelligence methodology, and edited and proofread the manuscript; all authors read and approved the final version of the manuscript.
Conflict-of-interest statement: The authors declare that they have no conflicts of interest. AI tools were used to assist with language editing and table synthesis under full human supervision, correction, and adaptation; all content was critically revised and approved by the authors.
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: Adriana Ciocalteu, MD, PhD, Assistant Professor, Department of Gastroenterology, Research Center of Gastroenterology and Hepatology, University of Medicine and Pharmacy of Craiova, Petru Rares 2, Craiova 200349, Romania. adriana_ciocalteu@yahoo.com
Received: July 21, 2025 Revised: August 20, 2025 Accepted: October 11, 2025 Published online: November 14, 2025 Processing time: 116 Days and 0.5 Hours
Abstract
Artificial intelligence (AI)-augmented contrast-enhanced ultrasonography (CEUS) is emerging as a powerful tool in liver imaging, particularly in enhancing the accuracy of Liver Imaging Reporting and Data System (known as LI-RADS) classification. This review synthesized published data on the integration of machine learning and deep learning techniques into CEUS, revealing that AI algorithms can improve the detection and quantification of contrast enhancement patterns. Such improvements led to more consistent LI-RADS categorization, reduced interoperator variability, and enabled real-time analysis that streamlined workflow. The enhanced sensitivity of AI tools facilitated better differentiation between benign and malignant lesions, ultimately optimizing patient management. These advances suggest that AI-augmented CEUS could transform liver imaging by providing rapid, reliable, and objective assessments. However, the review also highlighted the need for further large-scale, multicenter studies to fully validate these findings and ensure the safe integration of AI into routine clinical practice.
Core Tip: Artificial intelligence (AI) has shown increasing potential in enhancing liver imaging workflows. This review focused on the integration of AI into contrast-enhanced ultrasound for liver lesion assessment with an emphasis on automating the Liver Imaging Reporting and Data System classification. We summarized recent deep learning architectures, radiomics applications, and clinical impact studies, highlighting both diagnostic performance and technical challenges. The review provided a forward-looking perspective on how contrast-enhanced ultrasound-based AI models may standardize interpretation, support real-time decision-making, and transform hepatocellular carcinoma diagnosis in clinical practice.
