Evidence map›Paper›PMID 40722715›Full record

ReviewBiomedicines2025

Knee Osteoarthritis Diagnosis: Future and Perspectives.

Henri Favreau, Kirsley Chennen, Sylvain Feruglio, Elise Perennes, Nicolas Anton, Thierry Vandamme, Nadia Jessel, Olivier Poch, Guillaume Conzatti

Abstract readReview
In one paragraph

Review in Biomedicines, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Review
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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

9 authors.

Henri FavreauUniversité de Strasbourg, INSERM, Regenerative Nanomedicine (RNM) UMR 1260, CRBS, 1 Rue Eugène Boeckel, 67000 Strasbourg, France.
Kirsley ChennenLaboratory of Medical Genetics, INSERM U1112, CRBS, 1 Rue Eugène Boeckel, 67000 Strasbourg, France.ORCID 0000-0001-9268-6748
Sylvain FeruglioSorbonne Université, Laboratoire d'Informatique de Paris 6 (LIP6), CNRS UMR7606, 4 Place Jussieu, CEDEX 05, 75252 Paris, France.ORCID 0000-0002-6444-2671
Elise PerennesUniversité de Strasbourg, INSERM, Regenerative Nanomedicine (RNM) UMR 1260, CRBS, 1 Rue Eugène Boeckel, 67000 Strasbourg, France.
Nicolas AntonUniversité de Strasbourg, INSERM, Regenerative Nanomedicine (RNM) UMR 1260, CRBS, 1 Rue Eugène Boeckel, 67000 Strasbourg, France.ORCID 0000-0002-7047-9657
Thierry VandammeUniversité de Strasbourg, INSERM, Regenerative Nanomedicine (RNM) UMR 1260, CRBS, 1 Rue Eugène Boeckel, 67000 Strasbourg, France.
Nadia JesselUniversité de Strasbourg, INSERM, Regenerative Nanomedicine (RNM) UMR 1260, CRBS, 1 Rue Eugène Boeckel, 67000 Strasbourg, France.
Olivier PochUniversité de Strasbourg, Complex Systems and Translational Bioinformatics (CSTB), ICube Laboratory, CNRS UMR 7357, CRBS, 1 Rue Eugène Boeckel, 67000 Strasbourg, France.
Guillaume ConzattiUniversité de Strasbourg, INSERM, Regenerative Nanomedicine (RNM) UMR 1260, CRBS, 1 Rue Eugène Boeckel, 67000 Strasbourg, France.ORCID 0000-0002-8002-5894

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The risk of developing symptomatic knee osteoarthritis (KOA) during a lifetime, i.e., pain, aching, or stiffness in a joint associated with radiographic KOA, was estimated in 2008 to be around 40% in men and 47% in women. The clinical and scientific communities lack an efficient diagnostic method to effectively monitor, evaluate, and predict the evolution of KOA before and during the therapeutic protocol. In this review, we summarize the main methods that are used or seem promising for the diagnosis of osteoarthritis, with a specific focus on non- or low-invasive methods. As standard diagnostic tools, arthroscopy, magnetic resonance imaging (MRI), and X-ray radiography provide spatial and direct visualization of the joint. However, discrepancies between findings and patient feelings often occur, indicating a lack of correlation between current imaging methods and clinical symptoms. Alternative strategies are in development, including the analysis of biochemical markers or acoustic emission recordings. These methods have undergone deep development and propose, with non- or minimally invasive procedures, to obtain data on tissue condition. However, they present some drawbacks, such as possible interference or the lack of direct visualization of the tissue. Other original methods show strong potential in the field of KOA monitoring, such as electrical bioimpedance or near-infrared spectrometry. These methods could permit us to obtain cheap, portable, and non-invasive data on joint tissue health, while they still need strong implementation to be validated. Also, the use of Artificial Intelligence (AI) in the diagnosis seems essential to effectively develop and validate predictive models for KOA evolution, provided that a large and robust database is available. This would offer a powerful tool for researchers and clinicians to improve therapeutic strategies while permitting an anticipated adaptation of the clinical protocols, moving toward reliable and personalized medicine.

Indexed as

artificial intelligencediagnosismedical deviceosteoarthritis

Identifiers

PMID40722715
PMCPMC12292290

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.