Objective: To investigate the associations between whole joint cartilage and meniscal morphology on MRI and radiographic joint space width (JSW) measures and in knee osteoarthritis (KOA), to determine whether bicompartmental measures demonstrate stronger associations than unicompartmental ones, and to evaluate their correlations with Kellgren and Lawrence grading. Design: A cross-sectional analysis of baseline radiographs and MRIs from 262 KOA participants in the prospective, multicenter IMI-APPROACH cohort was conducted. Radiographic measures included minimum joint space width (mJSW), fixed location JSW (JSW(x)), mean JSW, and joint line convergence angle (JLCA), assessed using fully automated software. JSW was evaluated both unicompartmentally and bicompartmentally. Cartilage morphology, full-thickness cartilage loss, meniscal extrusion, tears, and maceration were assessed using the semi-quantitative MRI Osteoarthritis Knee Score to summarize whole-joint cartilage and meniscal morphology. Associations of radiographic measures with MRI outcomes were assessed using multivariable linear regression; Spearman correlations with Kellgren and Lawrence (KL) were also evaluated. Results: MRI-defined meniscal maceration was associated with unicompartmental and bicompartmental JSW measures. Full-thickness cartilage loss was associated with unicompartmental (95% CI [-0.16;-0.02]) and bicompartmental mJSW (95% CI [-0.14;-0.02]), and JLCA (95% CI [0.04;0.22]). Models explained 32–39% of variance for unicompartmental and 23–45% for bicompartmental measures (R²). Bicompartmental measures showed stronger correlations with KL grading than unicompartmental measures (95% CI: –0.31 to –0.02). Conclusions: Associations between whole-joint cartilage and meniscal degeneration are similar for uni- and bicompartmental JSW, with bicompartmental JSW showing stronger correlations with KL grades. These findings support including both compartments in radiographic assessment to improve structural evaluation in KOA.

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).

Background and purpose — Being able to predict the hip–knee–ankle angle (HKAA) from standard knee radiographs allows studies on malalignment in cohorts lacking full-limb radiography. We aimed to develop an automated image analysis pipeline to measure the femoro-tibial angle (FTA) from standard knee radiographs and test various FTA definitions to predict the HKAA. Patients and methods — We included 110 pairs of standard knee and full-limb radiographs. Automatic search algorithms found anatomic landmarks on standard knee radiographs. Based on these landmarks, the FTA was automatically calculated according to 9 different definitions (6 described in the literature and 3 newly developed). Pearson and intra-class correlation coefficient [ICC]) were determined between the FTA and HKAA as measured on full-limb radiographs. Subsequently, the top 4 FTA definitions were used to predict the HKAA in a 5-fold cross-validation setting. Results — Across all pairs of images, the Pearson correlations between FTA and HKAA ranged between 0.83 and 0.90. The ICC values from 0.83 to 0.90. In the cross-validation experiments to predict the HKAA, these values decreased only minimally. The mean absolute error for the best method to predict the HKAA from standard knee radiographs was 1.8° (SD 1.3). Interpretation — We showed that the HKAA can be automatically predicted from standard knee radiographs with fair accuracy and high correlation compared with the true HKAA. Therefore, this method enables research of the relationship between malalignment and knee pathology in large (epidemiological) studies lacking full-limb radiography.