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

• Published in: BioMedInformatics Vol. 3; no. 3; pp. 714 - 723
• Main Authors: Andersen, Anne Mathilde, Rasmussen, Benjamin S. B., Graumann, Ole, Overgaard, Søren, Lundemann, Michael, Haubro, Martin Haagen, Varnum, Claus, Rasmussen, Janne, Jensen, Janni
• Format: Journal Article
• Language: English
• Published: MDPI AG 01.09.2023
• Subjects: artificial intelligence; deep learning; minimal joint space width; osteoarthritis; radiology; X-ray
• ISSN: 2673-7426; 2673-7426
• DOI: 10.3390/biomedinformatics3030046

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.