Evidence map›Paper›PMID 39891185›Full record

ArticleDiagnostic pathology2025

Pre-screening of endomysial microvessel density by fast random forest image processing machine learning algorithm accelerates recognition of a modified vascular network in idiopathic inflammatory myopathies.

Alessandro Massaro, Gerardo Cazzato, Giuseppe Ingravallo, Nadia Casatta, Carmelo Lupo, Angelo Vacca, Florenzo Iannone, Francesco Girolamo

Abstract read
In one paragraph

Article in Diagnostic pathology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–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

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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.

Alessandro MassaroDepartment of Engineering, LUM University "Giuseppe Degennaro", Casamassima, Italy.
Gerardo CazzatoSection of Molecular Pathology, Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari "Aldo Moro", Bari, 70124, Italy. gerardo.cazzato@uniba.it.
Giuseppe IngravalloSection of Molecular Pathology, Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari "Aldo Moro", Bari, 70124, Italy.
Nadia CasattaDiapath SpA, Martinengo, Italy.
Carmelo LupoDiapath SpA, Martinengo, Italy.
Angelo VaccaGuido Baccelli Unit of Internal Medicine, Department of Precision and Regenerative Medicine and Jonian Area-(DiMePRe-J), University of Bari Aldo Moro, Bari, Italy.
Florenzo IannoneSection of Rheumathology, Department of Precision Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari Aldo Moro, Bari, Italy.
Francesco GirolamoUnit of Human Anatomy and Histology, Department of Translational Biomedicine and Neuroscience "DiBraiN", University of Bari, Bari, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Biomarkers for discrimination among different subgroups of idiopathic inflammatory myopathies (IIM) are difficult to identify and may involve multiple laboratory tests and time-consuming procedures. We assessed the potential for artificial intelligence (AI) to extract features such as density of endomysial microvessels based on automatic analysis of the CD31

Indexed as

Image Interpretation, Computer-AssistedImage Processing, Computer-AssistedMachine LearningMicrovascular DensityMicrovesselsMuscle, SkeletalMyositisAdultAlgorithmsCapillariesFemaleHumansMaleMiddle AgedPlatelet Endothelial Cell Adhesion Molecule-1Random ForestPlatelet Endothelial Cell Adhesion Molecule-1FRF image processingMyositisNecrotizing myopathy, CD31Neovascularization

Identifiers

PMID39891185
PMCPMC11783852

What OpenQuestion holds

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Registered trials

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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.