Evidence map›Paper›PMID 36541701›Full record

ArticleCartilage2023

Toward New Assessment of Knee Cartilage Degeneration.

Romain Aubonnet, Jorgelina Ramos, Marco Recenti, Deborah Jacob, Federica Ciliberti, Lorena Guerrini, Magnus K Gislason, Olafur Sigurjonsson, Mariella Tsirilaki, Halldór Jónsson and 1 more

Open access · hybridAbstract read
In one paragraph

Article in Cartilage, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
3.2field-weighted citation impact, top 7% of its field
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

5 citing papers in PubMed, 19 citations in OpenAlex.

  1. Article
  2. Review
  3. The Role ofBiomolecules · 2025
    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

11 authors at 3 institutions in 1 country.

Romain AubonnetInstitute of Biomedical and Neural Engineering, Reykjavik University, Reykjavik, Iceland.ORCID 0000-0002-5395-775X
Jorgelina RamosInstitute of Biomedical and Neural Engineering, Reykjavik University, Reykjavik, Iceland.
Marco RecentiInstitute of Biomedical and Neural Engineering, Reykjavik University, Reykjavik, Iceland.
Deborah JacobInstitute of Biomedical and Neural Engineering, Reykjavik University, Reykjavik, Iceland.
Federica CilibertiInstitute of Biomedical and Neural Engineering, Reykjavik University, Reykjavik, Iceland.
Lorena GuerriniInstitute of Biomedical and Neural Engineering, Reykjavik University, Reykjavik, Iceland.
Magnus K GislasonInstitute of Biomedical and Neural Engineering, Reykjavik University, Reykjavik, Iceland.
Olafur SigurjonssonInstitute of Biomedical and Neural Engineering, Reykjavik University, Reykjavik, Iceland.
Mariella TsirilakiLandspitali, University Hospital of Iceland, Reykjavik, Iceland.
Halldór JónssonLandspitali, University Hospital of Iceland, Reykjavik, Iceland.
Paolo GargiuloInstitute of Biomedical and Neural Engineering, Reykjavik University, Reykjavik, Iceland.
Reykjavík University · ISNational University Hospital of Iceland · ISUniversity of Iceland · IS

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveAssessment of human joint cartilage is a crucial tool to detect and diagnose pathological conditions. This exploratory study developed a workflow for 3D modeling of cartilage and bone based on multimodal imaging. New evaluation metrics were created and, a unique set of data was gathered from healthy controls and patients with clinically evaluated degeneration or trauma.

designWe present a novel methodology to evaluate knee bone and cartilage based on features extracted from magnetic resonance imaging (MRI) and computed tomography (CT) data. We developed patient specific 3D models of the tibial, femoral, and patellar bones and cartilages. Forty-seven subjects with a history of degenerative disease, traumatic events, or no symptoms or trauma (control group) were recruited in this study. Ninety-six different measurements were extracted from each knee, 78 2D and 18 3D measurements. We compare the sensitivity of different metrics to classify the cartilage condition and evaluate degeneration.

resultsSelected features extracted show significant difference between the 3 groups. We created a cumulative index of bone properties that demonstrated the importance of bone condition to assess cartilage quality, obtaining the greatest sensitivity on femur within medial and femoropatellar compartments. We were able to classify degeneration with a maximum recall value of 95.9 where feature importance analysis showed a significant contribution of the 3D parameters.

conclusionThe present work demonstrates the potential for improving sensitivity in cartilage assessment. Indeed, current trends in cartilage research point toward improving treatments and therefore our contribution is a first step toward sensitive and personalized evaluation of cartilage condition.

Indexed as

Cartilage, ArticularCartilage DiseasesHumansKneeKnee JointPatella3D modelsclassificationimage segmentationknee cartilageosteoarthritis

Identifiers

PMID36541701
PMCPMC10601563
OpenAlexW4312065877

What OpenQuestion 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 OpenQuestion graph.