Automated Radiographic Measurements of Knee Osteoarthritis

Journal article (2023)

Authors

H. Rayegan University of Birjand
H. C. Nguyen University Medical Center Utrecht
HH Weinans Biomaterials & Tissue Biomechanics - Mechanical, Maritime and Materials Engineering , University Medical Center Utrecht
Harrie Weinans Biomaterials & Tissue Biomechanics - Mechanical, Maritime and Materials Engineering , University Medical Center Utrecht
H. Weinans Biomaterials & Tissue Biomechanics - Mechanical, Maritime and Materials Engineering , University Medical Center Utrecht
Willem Paul Gielis University Medical Center Utrecht
W. P. Gielis University Medical Center Utrecht
S. Y. Ahmadi Brooghani University of Birjand
R. J.H. Custers University Medical Center Utrecht
N. van Egmond University Medical Center Utrecht
C. Lindner The University of Manchester
V. Arbabi University Medical Center Utrecht, University of Birjand
Research Group
Biomaterials & Tissue Biomechanics (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2023 H. Rayegan, H. C. Nguyen, Harrie Weinans, W. P. Gielis, S. Y. Ahmadi Brooghani, R. J.H. Custers, N. van Egmond, C. Lindner, V. Arbabi
DOI: https://doi.org/10.1177/19476035231166126
More Info
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Published Date

2023

Language

English

Faculty

Mechanical, Maritime and Materials Engineering

Department

Biomechanical Engineering

Research Group

Biomaterials & Tissue Biomechanics

Abstract

Objective: Herewith, we report the development of Orthopedic Digital Image Analysis (ODIA) software that is developed to obtain quantitative measurements of knee osteoarthritis (OA) radiographs automatically. Manual segmentation and measurement of OA parameters currently hamper large-cohort analyses, and therefore, automated and reproducible methods are a valuable addition in OA research. This study aims to test the automated ODIA measurements and compare them with available manual Knee Imaging Digital Analysis (KIDA) measurements as comparison. Design: This study included data from the CHECK (Cohort Hip and Cohort Knee) initiative, a prospective multicentre cohort study in the Netherlands with 1,002 participants. Knee radiographs obtained at baseline of the CHECK cohort were included and mean medial/lateral joint space width (JSW), minimal JSW, joint line convergence angle (JLCA), eminence heights, and subchondral bone intensities were compared between ODIA and KIDA. Results: Of the potential 2,004 radiographs, 1,743 were included for analyses. Poor intraclass correlation coefficients (ICCs) were reported for the JLCA (0.422) and minimal JSW (0.299). The mean medial and lateral JSW, eminence height, and subchondral bone intensities reported a moderate to good ICC (0.7 or higher). Discrepancies in JLCA and minimal JSW between the 2 methods were mostly a problem in the lateral tibia plateau. Conclusions: The current ODIA tool provides important measurements of OA parameters in an automated manner from standard radiographs of the knee. Given the automated and computerized methodology that has very high reproducibility, ODIA is suitable for large epidemiological cohorts with various follow-up time points to investigate structural progression, such as CHECK or the Osteoarthritis Initiative (OAI).