Evidence map›Paper›PMID 41278165›Full record

ReviewWorld journal of gastroenterology2025

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

Abstract readReview
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Adriana CiocalteuDepartment of Gastroenterology, Research Center of Gastroenterology and Hepatology, University of Medicine and Pharmacy of Craiova, Craiova 200349, Romania. adriana_ciocalteu@yahoo.com.
Cristiana M UrhutDepartment of Gastroenterology, Emergency County Hospital of Craiova, Craiova 200642, Romania.
Costin Teodor StrebaOncometrics, S.R.L., Craiova 200677, Romania.
Adina KamalDepartment of Internal Medicine, University of Medicine and Pharmacy of Craiova, Craiova 200349, Romania.
Madalin MamuleanuDepartment of Automatic Control and Electronics, University of Craiova, Craiova 200585, Romania.
Larisa D SandulescuDepartment of Gastroenterology, Research Center of Gastroenterology and Hepatology, University of Medicine and Pharmacy of Craiova, Craiova 200349, Romania.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Artificial IntelligenceLiverLiver NeoplasmsAlgorithmsContrast MediaDeep LearningHumansImage Interpretation, Computer-AssistedMachine LearningUltrasonographyContrast MediaArtificial intelligenceClinical decision support systemsContrast-enhanced ultrasoundDeep learningDiagnostic workflowFocal liver lesionsHepatocellular carcinomaImage interpretationLiver Imaging Reporting and Data SystemRadiomics

Identifiers

PMID41278165
PMCPMC12635717

What Socratic holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.