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Osteoarthritis Cartilage. 2011 Aug;19(8):990-1002. doi: 10.1016/j.joca.2011.05.004. Epub 2011 May 23.

Evolution of semi-quantitative whole joint assessment of knee OA: MOAKS (MRI Osteoarthritis Knee Score).

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Rheumatology Department, Royal North Shore Hospital, Northern Clinical School, University of Sydney, Sydney, NSW 2065, Australia.

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  • Osteoarthritis Cartilage. 2011 Sep;19(9):1168.



In an effort to evolve semi-quantitative scoring methods based upon limitations identified in existing tools, integrating expert readers' experience with all available scoring tools and the published data comparing the different scoring systems, we iteratively developed the magnetic resonance imaging (MRI) Osteoarthritis Knee Score (MOAKS). The purpose of this report is to describe the instrument and its reliability.


The MOAKS instrument refines the scoring of bone marrow lesions (BMLs) (providing regional delineation and scoring across regions), cartilage (sub-regional assessment), and refines the elements of meniscal morphology (adding meniscal hypertrophy, partial maceration and progressive partial maceration) scoring. After a training and calibration session two expert readers read MRIs of 20 knees separately. In addition, one reader re-read the same 20 MRIs 4 weeks later presented in random order to assess intra-rater reliability. The analyses presented here are for both intra- and inter-rater reliability (calculated using the linear weighted kappa and overall percent agreement).


With the exception of inter-rater reliability for tibial cartilage area (kappa=0.36) and tibial osteophytes (kappa=0.49); and intra-rater reliability for tibial BML number of lesions (kappa=0.54), Hoffa-synovitis (kappa=0.42) all measures of reliability using kappa statistics were very good (0.61-0.8) or reached near-perfect agreement (0.81-1.0). Only intra-rater reliability for Hoffa-synovitis, and inter-rater reliability for tibial and patellar osteophytes showed overall percent agreement <75%.


MOAKS scoring shows very good to excellent reliability for the large majority of features assessed. Further iterative development and research will include assessment of its validation and responsiveness.

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