Artificial intelligence in contrast enhanced ultrasound: A new era for liver lesion assessment

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.

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Publication Details

Journal
World Journal of Gastroenterology
Published
2025-11-13
DOI
https://doi.org/10.3748/wjg.v31.i42.112196
Primary Topic
Hepatocellular Carcinoma Treatment and Prognosis
Type
article
Field-Weighted Citation Impact
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article

Artificial intelligence in contrast enhanced ultrasound: A new era for liver lesion assessment

Mădălin Mămuleanu, Costin Teodor Streba, Cristiana Marinela Urhuț, Adriana Ciocâlteu et al.
World Journal of Gastroenterology
Hepatocellular Carcinoma Treatment and Prognosis
article

Artificial intelligence in contrast enhanced ultrasound: A new era for liver lesion assessment

Mădălin Mămuleanu, Costin Teodor Streba, Cristiana Marinela Urhuț, Adriana Ciocâlteu, Adina Maria Kamal, Larisa Săndulescu
article en

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.

World Journal of GastroenterologyVol. 31(42)
University of Medicine and Pharmacy of Craiova (RO)
Openalex Percentile: Top 55%
Hepatocellular Carcinoma Treatment and Prognosis
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