Evidence map›Paper›PMID 39143129›Full record

ArticleScientific reports2024

An approach leveraging radiomics and model checking for the automatic early diagnosis of adhesive capsulitis.

Giulia Varriano, Vittoria Nardone, Maria Chiara Brunese, Michela Bruno, Antonella Santone, Luca Brunese, Marcello Zappia

Abstract read
In one paragraph

Article in Scientific reports, 2024. 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. Observational
  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

7 authors.

Giulia VarrianoDepartment of Medicine and Surgery "V. Tiberio", University of Molise, 86100, Campobasso, Italy. giulia.varriano@unimol.it.
Vittoria NardoneDepartment of Medicine and Surgery "V. Tiberio", University of Molise, 86100, Campobasso, Italy.
Maria Chiara BruneseDepartment of Medicine and Surgery "V. Tiberio", University of Molise, 86100, Campobasso, Italy. mariachiarabrunese@gmail.com.
Michela BrunoDepartment of Medicine and Surgery "V. Tiberio", University of Molise, 86100, Campobasso, Italy.
Antonella SantoneDepartment of Medicine and Surgery "V. Tiberio", University of Molise, 86100, Campobasso, Italy.
Luca BruneseDepartment of Medicine and Surgery "V. Tiberio", University of Molise, 86100, Campobasso, Italy.
Marcello ZappiaDepartment of Medicine and Surgery "V. Tiberio", University of Molise, 86100, Campobasso, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Adhesive Capsulitis of the shoulder is a painful pathology limiting shoulder movements, commonly known as "Frozen Shoulder". Since this pathology limits movement, it is important to make an early diagnosis. Diagnosing capsulitis relies on clinical assessment, although diagnostic imaging, such as Magnetic Resonance Imaging, can provide predictive or supportive information for specific characteristic signs. However, its diagnosis is not so simple nor so immediate, indeed it remains a difficult topic for many general radiologists and expert musculoskeletal radiologists. This study aims to investigate whether it is possible to use disease signs within a medical image to automatically diagnose Adhesive Capsulitis. To this purpose, we propose an automatic Model Checking-based approach to quickly diagnose the Adhesive Capsulitis taking as input the radiomic feature values from the medical images. Furthermore, we compare the performance achieved by our method with diagnostic results obtained by professional radiologists with different levels of experience. To the best of our knowledge, this is the first method for the automatic diagnosis of Adhesive Capsulitis of the Shoulder.

Indexed as

BursitisEarly DiagnosisMagnetic Resonance ImagingAgedFemaleHumansMaleMiddle AgedRadiomicsShoulder JointFormal methodsFrozen shoulderMedical imagingModel checkingRadiomics

Identifiers

PMID39143129
PMCPMC11324739

What Socratic holds

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LicenceCC BY
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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 Socratic graph.