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Updated: May 15, 2026

Automated Joint Space Detection Improves Bone Segmentation Accuracy
Published on: November 28, 2025
Correlations between radiographic assessments and MRI features of knee osteoarthritis--a cross-sectional study
H Gudbergsen1, L S Lohmander, G Jones
1The Parker Institute, Department of Rheumatology, Copenhagen University Hospital at Frederiksberg, Denmark. drhg@dadlnet.dk
Objectives:
To assess correlations between Kellgren & Lawrence (KL) gradings, minimum joint space width (mJSW) measurements and the Boston Leeds Osteoarthritis Knee Score (BLOKS) within a cohort of obese patients with knee osteoarthritis (KOA).
Methods:
192 Participants were recruited from an outpatient clinic (ClinicalTrials.gov: NCT00655941). Inclusion criteria were age ≥50 years, body mass index (BMI) ≥30 kg/m(2) plus symptomatic and verified KOA. 1.5 T magnetic resonance imaging (MRI) scans were assessed using BLOKS and bi-plane radiography by mJSW and KL. Statistics used were Spearman rank correlation coefficients.
Results:
The average patient was 63 years of age, female and had a BMI of 37. KL gradings correlated to cartilage damage, bone marrow lesions and meniscus pathology (r = 0.15-0.76) and similar results were found for the relationship between BLOKS and mJSW. BLOKS assessed knee joint pathology co-segregated with compartment and grade specific KL (P < 0.0001). BLOKS variables were statistically significant correlated, particularly in the medial tibiofemoral compartment (r = 0.42-0.80). Adjusting for age, gender and BMI did not alter these associations.
Conclusion:
Extensive pathological damage is present even in mild radiographic KOA and BLOKS gradings and KL scores increase together. Analyses of compartment specific KL scores revealed differences in their relationship to the assessed MRI variables. Our study displays the segregation of MRI gradings with respect to location and level of radiographic scores, reveals a high inter-dependency of MRI-assessed structures, and describes some redundancy of specific BLOKS variables.
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