Evidence map›Paper›PMID 35204370›Full record

ArticleDiagnostics (Basel, Switzerland)2022

CT- and MRI-Based 3D Reconstruction of Knee Joint to Assess Cartilage and Bone.

Federica Kiyomi Ciliberti, Lorena Guerrini, Arnar Evgeni Gunnarsson, Marco Recenti, Deborah Jacob, Vincenzo Cangiano, Yonatan Afework Tesfahunegn, Anna Sigríður Islind, Francesco Tortorella, Mariella Tsirilaki and 3 more

Open access · goldAbstract read
In one paragraph

Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed
8.2field-weighted citation impact, top 2% 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

22 citing papers in PubMed, 51 citations in OpenAlex.

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  8. Automated personalization of biomechanical knee model.International journal of computer assisted radiology and surgery · 2024
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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

13 authors at 4 institutions in 2 countries.

Federica Kiyomi CilibertiInstitute of Biomedical and Neural Engineering, Reykjavik University, 101 Reykjavik, Iceland.ORCID 0000-0002-8378-9478
Lorena GuerriniInstitute of Biomedical and Neural Engineering, Reykjavik University, 101 Reykjavik, Iceland.ORCID 0000-0002-6308-8981
Arnar Evgeni GunnarssonInstitute of Biomedical and Neural Engineering, Reykjavik University, 101 Reykjavik, Iceland.
Marco RecentiInstitute of Biomedical and Neural Engineering, Reykjavik University, 101 Reykjavik, Iceland.
Deborah JacobInstitute of Biomedical and Neural Engineering, Reykjavik University, 101 Reykjavik, Iceland.ORCID 0000-0003-3507-8003
Vincenzo CangianoInstitute of Biomedical and Neural Engineering, Reykjavik University, 101 Reykjavik, Iceland.ORCID 0000-0001-5187-9164
Yonatan Afework TesfahunegnDepartment of Engineering, Reykjavik University, 101 Reykjavik, Iceland.
Anna Sigríður IslindDepartment of Computer Science, Reykjavik University, 101 Reykjavik, Iceland.ORCID 0000-0002-4563-0001
Francesco TortorellaDepartment of Electrical, Information Engineering and Applied Mathematics, University of Salerno, 84084 Salerno, Italy.ORCID 0000-0002-5033-9323
Mariella TsirilakiDepartment of Radiology, Landspitali, University Hospital of Iceland, 101 Reykjavik, Iceland.
Halldór JónssonDepartment of Orthopaedics, Landspitali, University Hospital of Iceland, 101 Reykjavik, Iceland.ORCID 0000-0001-8406-9328
Paolo GargiuloInstitute of Biomedical and Neural Engineering, Reykjavik University, 101 Reykjavik, Iceland.ORCID 0000-0002-5049-4817
Romain AubonnetInstitute of Biomedical and Neural Engineering, Reykjavik University, 101 Reykjavik, Iceland.ORCID 0000-0002-5395-775X
Reykjavík University · ISNational University Hospital of Iceland · ISUniversity of Iceland · ISUniversity of Salerno · IT

Funding

Community Research and Development Information Service 814558
6 · The paper itself

Abstract

For the observation of human joint cartilage, X-ray, computed tomography (CT) or magnetic resonance imaging (MRI) are the main diagnostic tools to evaluate pathologies or traumas. The current work introduces a set of novel measurements and 3D features based on MRI and CT data of the knee joint, used to reconstruct bone and cartilages and to assess cartilage condition from a new perspective. Forty-seven subjects presenting a degenerative disease, a traumatic injury or no symptoms or trauma were recruited in this study and scanned using CT and MRI. Using medical imaging software, the bone and cartilage of the knee joint were segmented and 3D reconstructed. Several features such as cartilage density, volume and surface were extracted. Moreover, an investigation was carried out on the distribution of cartilage thickness and curvature analysis to identify new markers of cartilage condition. All the extracted features were used with advanced statistics tools and machine learning to test the ability of our model to predict cartilage conditions. This work is a first step towards the development of a new gold standard of cartilage assessment based on 3D measurements.

Indexed as

3D modelingimage segmentationknee jointmachine learningmedical imaging

Identifiers

PMID35204370
PMCPMC8870751
OpenAlexW4207047654

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.