Evidence map›Paper›PMID 40162114›Full record

ArticleActa radiologica open2025

A deep learning algorithm for radiographic measurements of the hip versus human CT measurements: An intermodality agreement study.

Christian Greve Jensen, Benjamin Schnack Rasmussen, Søren Overgaard, Claus Varnum, Martin Haagen Haubro, Janni Jensen

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Article in Acta radiologica open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Christian Greve JensenDepartment of Clinical Research, University of Southern Denmark Odense, Denmark.ORCID https://orcid.org/0000-0002-7076-8483
Benjamin Schnack RasmussenDepartment of Clinical Research, University of Southern Denmark Odense, Denmark.
Søren OvergaardDepartment of Orthopaedic Surgery and Traumatology, Copenhagen University Hospital, Copenhagen, Denmark.
Claus VarnumDepartment of Orthopedic Surgery, Lillebaelt Hospital, Vejle, University Hospital of Southern Denmark Vejle, Denmark.
Martin Haagen HaubroDepartment of Orthopedic Surgery and Traumatology, Odense University Hospital Odense, Denmark.ORCID https://orcid.org/0000-0002-6493-3980
Janni JensenResearch and Innovation Unit of Radiology, University of Southern Denmark Odense, Denmark.ORCID https://orcid.org/0000-0001-7036-6129

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Hip dysplasia (HD) is a prevalent cause of non-traumatic hip pain, which may result in osteoarthritis. Radiological measurements of HD exhibit variability based on reader and imaging modality, why it is important to know the agreement between different measurement methods. Purpose: To estimate agreement between measurements of lateral center edge angle (LCEA) and acetabular inclination angle (AIA) made, respectively, on Computed Tomography (CT) scans by humans and radiographs analyzed by an algorithm. To estimate impact of pelvic rotation on agreement between CT and radiographic measurements. Material and Methods: CT measurements were retrospectively extracted from 172 radiology reports. Radiographs were analyzed using an algorithm. Bland-Altman analysis assessed agreement between CT and radiographic measurements. Regression analyses estimated impact of pelvic rotation on inter-modality agreement. Results: Mean measured bias (95% confidence interval [CI]) between CT and radiographs for LCEA of right/left hip was 5.53° (95% CI: 4.81 to 6.24) and 5.13 (95% CI: 4.43 to 5.83), respectively. Corresponding values for right/left AIA were 1.08 (95% CI: 0.49 to 1.67) and -0.03 (95% CI: -0.60 to 0.05). Pelvic rotation affected right LCEA and AIA measurements, with a change in obturator foramen index of, respectively, 0.35 and 0.6 resulting in approximately 2° change in values. Conclusion: There was a significant difference in agreement of 5° between CT and radiographs for the LCEA bilaterally. The difference for the AIA was between 0 and 1°, probably of little clinical significance. Pelvic rotation slightly affected bias of the right LCEA, suggesting minimal clinical impact of a slightly rotated pelvis.

Indexed as

Artificial intelligenceCThip dysplasiax-ray

Identifiers

PMID40162114
PMCPMC11948554

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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.