Home > Publication > A deep learning algorithm for radiographic measurements of the hip in adults—A reliability and agreement study

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A deep learning algorithm for radiographic measurements of the hip in adults—A reliability and agreement study

Authors:

Jensen, J.; Graumann, O.; Overgaard, S.; Gerke, O.; Lundemann, M.; Haubro, M.H.; Varnum, C.; Bak, L.; Rasmussen, J.; Olsen, L.B.; et al.

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Objective

The study assesses the reliability of RBhip, an AI solution designed to read pelvic anterior-posterior (AP) radiographs and estimates the agreement between RBhip and human readers for measuring lateral center edge angle of Wiberg (LCEA) and Acetabular index angle (AIA).

Open access

This research is open access

This original research can be read in Diagnostics in volume 12, issue 11, published October 2022.

Funding and conflicts of interests

This research was funded by the EIT Health Digital Sandbox Programme 2020, grant number DS20-12449. M.L. is the Director of Clinical Operations Radiobotics, Copenhagen, Denmark. M.L. contributed to the study design and final approval of the manuscript. M.L. had no access to study data and did not take part in data analysis. The other authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

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