Print Email Facebook Twitter Automated Radiographic Measurements of Knee Osteoarthritis Title Automated Radiographic Measurements of Knee Osteoarthritis Author Rayegan, H. (University of Birjand) Nguyen, H. C. (University Medical Center Utrecht) Weinans, Harrie (TU Delft Biomaterials & Tissue Biomechanics; University Medical Center Utrecht) Gielis, W. P. (University Medical Center Utrecht) Ahmadi Brooghani, S. Y. (University of Birjand) Custers, R. J.H. (University Medical Center Utrecht) van Egmond, N. (University Medical Center Utrecht) Lindner, C. (The University of Manchester) Arbabi, V. (University of Birjand; University Medical Center Utrecht) Date 2023 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). Subject automationkneemeasurementsosteoarthritisradiological imaging To reference this document use: http://resolver.tudelft.nl/uuid:5ae9de6e-fb6f-436b-95ef-9188d8f4ee3d DOI https://doi.org/10.1177/19476035231166126 ISSN 1947-6035 Source Cartilage, 14 (4), 413-423 Part of collection Institutional Repository Document type journal article Rights © 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 Files PDF rayegan_et_al_2023_automa ... hritis.pdf 1.32 MB Close viewer /islandora/object/uuid:5ae9de6e-fb6f-436b-95ef-9188d8f4ee3d/datastream/OBJ/view