Evidence map›Paper›PMID 39562432›Full record

SynthesisJournal of ultrasound2025

FibroScan's evolution: a critical 20-year review.

Nisar Ahmed, Ayushi Kumari, R Srinivasa Murty

Abstract readSystematic Review
In one paragraph

Synthesis in Journal of ultrasound, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

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

3 authors.

Nisar AhmedAditya Pharmacy Collage, Surampalem, Andhra Pradesh, India. nisarmehdinager786@gmail.com.ORCID http://orcid.org/0009-0002-9296-8512
Ayushi KumariAditya Pharmacy Collage, Surampalem, Andhra Pradesh, India.
R Srinivasa MurtyAIIMS, New Delhi, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

FibroScan, initially designed for assessing cheese maturity, has evolved into a crucial medical tool for liver fibrosis diagnosis. This systematic review explores its development history, functionality, and pros and cons compared to traditional liver biopsy. Precision in various clinical settings is scrutinised, emphasising FibroScan's accuracy in conditions like NAFLD and viral-induced liver disease. The article also delves into its potential in paediatrics, its relevance in monitoring COVID-19-related liver complications, and its role in predicting hepatocellular carcinoma risk, Technical aspects, including transducers, imaging integration, and portability, are examined. Various methods for evaluating liver fibrosis are discussed, highlighting FibroScan's suitability for advanced stages, contrasting with the gold standard of liver biopsy for early stages. The impact of FibroScan on long-term liver conditions is emphasised, focusing on early detection, progression monitoring, reduced invasive biopsies, and hepatocellular carcinoma risk prediction. This systematic review underscores FibroScan's transformative potential in liver disease treatment and predicts ongoing research to enhance early detection, disease monitoring, and explore new clinical applications. Anticipated advances include FibroScan-guided liver biopsy, artificial intelligence data analysis, and point-of-care device development, promising a further revolution in liver disease management. The article concludes with optimistic prospects for FibroScan's future.

Indexed as

Elasticity Imaging TechniquesLiver CirrhosisCarcinoma, HepatocellularCOVID-19HumansLiverLiver NeoplasmsSARS-CoV-2Artificial intelligenceChronic liver diseasesClinical applicationsDiagnostic accuracyFibroScanLiver biopsyLiver fibrosisNon-invasiveTransient elastography

Identifiers

PMID39562432
PMCPMC12145338

What Socratic holds

Textmetadata
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.