Evidence map›Paper›PMID 39312896›Full record

SynthesisDigestion2025

Artificial Intelligence for Contrast-Enhanced Ultrasound of the Liver: A Systematic Review.

James A Brooks, Michael Kallenbach, Iuliana-Pompilia Radu, Annalisa Berzigotti, Christoph F Dietrich, Jakob N Kather, Tom Luedde, Tobias P Seraphin

Abstract readSystematic Review
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in Digestion, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. Review
  5. Article
  6. Review
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

8 authors.

James A BrooksDepartment of Gastroenterology, Hepatology and Infectious Diseases, University Hospital Dusseldorf, Medical Faculty at Heinrich-Heine-University, Dusseldorf, Germany.
Michael KallenbachDepartment of Gastroenterology, Hepatology and Infectious Diseases, University Hospital Dusseldorf, Medical Faculty at Heinrich-Heine-University, Dusseldorf, Germany.
Iuliana-Pompilia RaduDepartment for Visceral Surgery and Medicine, Inselspital, University of Bern, Bern, Switzerland.
Annalisa BerzigottiDepartment for Visceral Surgery and Medicine, Inselspital, University of Bern, Bern, Switzerland.
Christoph F DietrichDepartment Allgemeine Innere Medizin (DAIM), Kliniken Hirslanden Beau Site, Salem and Permanence, Bern, Switzerland.
Jakob N KatherElse Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, Technical University Dresden, Dresden, Germany.
Tom LueddeDepartment of Gastroenterology, Hepatology and Infectious Diseases, University Hospital Dusseldorf, Medical Faculty at Heinrich-Heine-University, Dusseldorf, Germany.
Tobias P SeraphinDepartment of Gastroenterology, Hepatology and Infectious Diseases, University Hospital Dusseldorf, Medical Faculty at Heinrich-Heine-University, Dusseldorf, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionThe research field of artificial intelligence (AI) in medicine and especially in gastroenterology is rapidly progressing with the first AI tools entering routine clinical practice, for example, in colorectal cancer screening. Contrast-enhanced ultrasound (CEUS) is a highly reliable, low-risk, and low-cost diagnostic modality for the examination of the liver. However, doctors need many years of training and experience to master this technique and, despite all efforts to standardize CEUS, it is often believed to contain significant interrater variability. As has been shown for endoscopy, AI holds promise to support examiners at all training levels in their decision-making and efficiency.

methodsIn this systematic review, we analyzed and compared original research studies applying AI methods to CEUS examinations of the liver published between January 2010 and February 2024. We performed a structured literature search on PubMed, Web of Science, and IEEE. Two independent reviewers screened the articles and subsequently extracted relevant methodological features, e.g., cohort size, validation process, machine learning algorithm used, and indicative performance measures from the included articles.

resultsWe included 41 studies with most applying AI methods for classification tasks related to focal liver lesions. These included distinguishing benign versus malignant or classifying the entity itself, while a few studies tried to classify tumor grading, microvascular invasion status, or response to transcatheter arterial chemoembolization directly from CEUS. Some articles tried to segment or detect focal liver lesions, while others aimed to predict survival and recurrence after ablation. The majority (25/41) of studies used hand-picked and/or annotated images as data input to their models. We observed mostly good to high reported model performances with accuracies ranging between 58.6% and 98.9%, while noticing a general lack of external validation.

conclusionEven though multiple proof-of-concept studies for the application of AI methods to CEUS examinations of the liver exist and report high performance, more prospective, externally validated, and multicenter research is needed to bring such algorithms from desk to bedside.

Indexed as

Artificial IntelligenceContrast MediaLiverLiver NeoplasmsHumansUltrasonographyContrast MediaArtificial intelligenceContrast-enhanced ultrasoundHepatologyLiverSonography

Identifiers

PMID39312896
PMCPMC12129427

What OpenQuestion holds

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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 OpenQuestion graph.