Evidence map›Paper›PMID 38352173›Full record

ArticleOrthopaedic journal of sports medicine2024

Reliability of Manual Measurements Versus Semiautomated Software for Glenoid Bone Loss Quantification in Patients With Anterior Shoulder Instability.

Katrin Karpinski, Doruk Akguen, Henry Gebauer, Alp Paksoy, Mattia Lupetti, Viktoria Markova, Oliver Zettinig, Philipp Moroder

Abstract read
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Article in Orthopaedic journal of sports medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. 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

8 authors.

Katrin KarpinskiCentrum für Muskuloskeletale Chirurgie, Charité-Universitätsmedizin Berlin, Berlin, Germany.
Doruk AkguenCentrum für Muskuloskeletale Chirurgie, Charité-Universitätsmedizin Berlin, Berlin, Germany.
Henry GebauerCentrum für Muskuloskeletale Chirurgie, Charité-Universitätsmedizin Berlin, Berlin, Germany.
Alp PaksoyCentrum für Muskuloskeletale Chirurgie, Charité-Universitätsmedizin Berlin, Berlin, Germany.
Mattia LupettiImFusion, Munich, Germany.
Viktoria MarkovaImFusion, Munich, Germany.
Oliver ZettinigImFusion, Munich, Germany.
Philipp MoroderSchulthess Klinik, Zürich, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The presence of glenoid bone defects is indicative in the choice of treatment for patients with anterior shoulder instability. In contrast to traditional linear- and area-based measurements, techniques such as the consideration of glenoid concavity have been proposed and validated. Purpose: To compare the reliability of linear (1-dimensional [1D]), area (2-dimensional [2D]), and concavity (3-dimensional [3D]) measurements to quantify glenoid bone loss performed manually and to analyze how automated measurements affect reliability. Study Design: Cohort study (diagnosis); Level of evidence, 3. Methods: Computed tomography images of 100 patients treated for anterior shoulder instability with differently sized glenoid defects were evaluated independently by 2 orthopaedic surgeons manually using conventional software (OsiriX; Pixmeo) as well as automatically with a dedicated prototype software program (ImFusion Suite; ImFusion). Parameters obtained included 1D (defect diameter, best-fit circle diameter), 2D (defect area, best-fit circle area), and 3D (bony shoulder stability ratio) measurements. Mean values and reliability as expressed by the intraclass correlation coefficient [ICC]) were compared between the manual and automated measurements. Results: When manually obtained, the measurements showed almost perfect agreement for 1D parameters (ICC = 0.83), substantial agreement for 2D parameters (ICC = 0.79), and moderate agreement for the 3D parameter (ICC = 0.48). When measurements were aided by automated software, the agreement between raters was almost perfect for all parameters (ICC = 0.90 for 1D, 2D, and 3D). There was a significant difference in mean values between manually versus automatically obtained measurements for 1D, 2D, and 3D parameters ( Conclusion: While more advanced measurement techniques that take glenoid concavity into account are more accurate in determining the biomechanical relevance of glenoid bone loss, our study showed that the reliability of manually performed, more complex measurements was moderate.

Indexed as

BSSRglenoid bone lossglenoid concavityglenoid defectshoulder instabilityshoulder segmentation software

Identifiers

PMID38352173
PMCPMC10863483

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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.