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

Author:

Andersen Anne Mathilde1ORCID,Rasmussen Benjamin S. B.2345ORCID,Graumann Ole23,Overgaard Søren67ORCID,Lundemann Michael8,Haubro Martin Haagen9,Varnum Claus91011,Rasmussen Janne4,Jensen Janni2312

Affiliation:

1. Faculty of Health Sciences, Medicine, University of Southern Denmark, 5230 Odense, Denmark

2. Department of Radiology, Odense University Hospital, 5000 Odense, Denmark

3. Research and Innovation Unit of Radiology, University of Southern Denmark, 5230 Odense, Denmark

4. Department of Radiology, Odense University Hospital, 5700 Svendborg, Denmark

5. CAI-X, Centre for Clinical Artificial Intelligence, Odense University Hospital, University of Southern Denmark, 5230 Odense, Denmark

6. Department of Orthopaedic Surgery and Traumatology, Copenhagen University Hospital, Bispebjerg, 2100 Copenhagen, Denmark

7. Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, 1165 Copenhagen, Denmark

8. Radiobotics, 1263 Copenhagen, Denmark

9. Department of Orthopedic Surgery and Traumatology, Odense University Hospital, 5000 Odense, Denmark

10. Department of Orthopedic Surgery, Lillebaelt Hospital—Vejle, University Hospital of Southern Denmark, 7100 Vejle, Denmark

11. Department of Regional Health Research, University of Southern Denmark, 5230 Odense, Denmark

12. Open Patient Data Explorative Network (OPEN), Odense University Hospital, 5000 Odense, Denmark

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.

Funder

EIT Health Digital Sandbox Programme 2020

Publisher

MDPI AG

Subject

Management Science and Operations Research,Mechanical Engineering,Energy Engineering and Power Technology

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