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Published in: International Orthopaedics 4/2023

Open Access 17-02-2023 | Osteoarthrosis | Original Paper

Can an artificial intelligence powered software reliably assess pelvic radiographs?

Authors: Gilbert M Schwarz, Sebastian Simon, Jennyfer A Mitterer, Stephanie Huber, Bernhard JH Frank, Alexander Aichmair, Martin Dominkus, Jochen G Hofstaetter

Published in: International Orthopaedics | Issue 4/2023

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Despite advances of three-dimensional imaging pelvic radiographs remain the cornerstone in the evaluation of the hip joint. However, large inter- and intra-rater variabilities were reported due to subjective landmark setting. Artificial intelligence (AI)–powered software applications could improve the reproducibility of pelvic radiograph evaluation by providing standardized measurements. The aim of this study was to evaluate the reliability and agreement of a newly developed AI algorithm for the evaluation of pelvic radiographs.


Three-hundred pelvic radiographs from 280 patients with different degrees of acetabular coverage and osteoarthritis (Tönnis Grade 0 to 3) were evaluated. Reliability and agreement between manual measurements and the outputs of the AI software were assessed for the lateral-center-edge (LCE) angle, neck-shaft angle, sharp angle, acetabular index, as well as the femoral head extrusion index.


The AI software provided reliable results in 94.3% (283/300). The ICC values ranged between 0.73 for the Acetabular Index to 0.80 for the LCE Angle. Agreement between readers and AI outputs, given by the standard error of measurement (SEM), was good for hips with normal coverage (LCE-SEM: 3.4°) and no osteoarthritis (LCE-SEM: 3.3°) and worse for hips with undercoverage (LCE-SEM: 5.2°) or severe osteoarthritis (LCE-SEM: 5.1°).


AI-powered applications are a reliable alternative to manual evaluation of pelvic radiographs. While being accurate for patients with normal acetabular coverage and mild signs of osteoarthritis, it needs improvement in the evaluation of patients with hip dysplasia and severe osteoarthritis.
Can an artificial intelligence powered software reliably assess pelvic radiographs?
Gilbert M Schwarz
Sebastian Simon
Jennyfer A Mitterer
Stephanie Huber
Bernhard JH Frank
Alexander Aichmair
Martin Dominkus
Jochen G Hofstaetter
Publication date
Springer Berlin Heidelberg
Published in
International Orthopaedics / Issue 4/2023
Print ISSN: 0341-2695
Electronic ISSN: 1432-5195

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