ReviewWorld journal of gastroenterology2025
Artificial intelligence in contrast enhanced ultrasound: A new era for liver lesion assessment.
Review in World journal of gastroenterology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.