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Minimal Hip Joint Space Width Measured on X-rays by an Artificial Intelligence Algorithm—A Study of Reliability and Agreement

  • Anne Mathilde Andersen
  • , Benjamin S.B. Rasmussen
  • , Ole Graumann
  • , Søren Overgaard
  • , Michael Lundemann
  • , Martin Haagen Haubro
  • , Claus Varnum
  • , Janne Rasmussen
  • , Janni Jensen*
  • *Corresponding author for this work
9 Citations (Scopus)

Abstract

Minimal joint space width (mJSW) is a radiographic measurement used in the diagnosis of hip osteoarthritis. A large variance when measuring mJSW highlights the need for a supporting diagnostic tool. This study aimed to estimate the reliability of a deep learning algorithm designed to measure the mJSW in pelvic radiographs and to estimate agreement between the algorithm and orthopedic surgeons, radiologists, and a reporting radiographer. The algorithm was highly consistent when measuring mJSW with a mean difference at 0.00. Human readers, however, were subject to variance with a repeatability coefficient of up to 1.31. Statistically, although not clinically significant, differences were found between the algorithm’s and all readers’ measurements with mean measured differences ranging from −0.78 to −0.36 mm. In conclusion, the algorithm was highly reliable, and the mean measured difference between the human readers combined and the algorithm was low, i.e., −0.5 mm bilaterally. Given the consistency of the algorithm, it may be a useful tool for monitoring hip osteoarthritis.

Original languageEnglish
JournalBioMedInformatics
Volume3
Issue number3
Pages (from-to)714-723
Number of pages10
DOIs
Publication statusPublished - Sept 2023

Keywords

  • artificial intelligence
  • deep learning
  • minimal joint space width
  • osteoarthritis
  • radiology
  • X-ray

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