Evidence map›Paper›PMID 40299404›Full record

ArticleBiomedicines2025

Digital Pathology Tailored for Assessment of Liver Biopsies.

Alina-Iuliana Onoiu, David Parada Domínguez, Jorge Joven

Abstract read
In one paragraph

Article in Biomedicines, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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.

Alina-Iuliana OnoiuUnitat de Recerca Biomèdica, Hospital Universitari Sant Joan, Universitat Rovira i Virgili, 43204 Reus, Spain.ORCID 0009-0006-9483-498X
David Parada DomínguezUnitat de Recerca Biomèdica, Hospital Universitari Sant Joan, Universitat Rovira i Virgili, 43204 Reus, Spain.ORCID 0000-0002-8894-2312
Jorge JovenUnitat de Recerca Biomèdica, Hospital Universitari Sant Joan, Universitat Rovira i Virgili, 43204 Reus, Spain.ORCID 0000-0003-2749-4541

Funding

Instituto de Salud Carlos III PI21/00510, PI24/01146
6 · The paper itself

Abstract

Improved image quality, better scanners, innovative software technologies, enhanced computational power, superior network connectivity, and the ease of virtual image reproduction and distribution are driving the potential use of digital pathology for diagnosis and education. Although relatively common in clinical oncology, its application in liver pathology is under development. Digital pathology and improving subjective histologic scoring systems could be essential in managing obesity-associated steatotic liver disease. The increasing use of digital pathology in analyzing liver specimens is particularly intriguing as it may offer a more detailed view of liver biology and eliminate the incomplete measurement of treatment responses in clinical trials. The objective and automated quantification of histological results may help establish standardized diagnosis, treatment, and assessment protocols, providing a foundation for personalized patient care. Our experience with artificial intelligence (AI)-based software enhances reproducibility and accuracy, enabling continuous scoring and detecting subtle changes that indicate disease progression or regression. Ongoing validation highlights the need for collaboration between pathologists and AI developers. Concurrently, automated image analysis can address issues related to the historical failure of clinical trials stemming from challenges in histologic assessment. We discuss how these novel tools can be incorporated into liver research and complement post-diagnosis scenarios where quantification is necessary, thus clarifying the evolving role of digital pathology in the field.

Indexed as

algorithmsdeep learningliverobesityvirtual imageswhole tissue slide

Identifiers

PMID40299404
PMCPMC12024806

What Socratic holds

Textmetadata
LicenceCC BY
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.