AI automated grid placement in the OMERACT knee inflammation MRI scoring system (KIMRISS) for bone marrow lesion assessment: A multi-reader exercise.

OBJECTIVE: To validate the reliability and feasibility of AI-automated grid placement for bone marrow lesion (BML) scoring in the Knee Inflammation MRI Scoring System (KIMRISS) using the OMERACT Filter. METHODS: Eleven experts evaluated 40 MRI cases using manual and automated grid placement. Grids were compared both directly using spatial similarity metrics and indirectly using agreement metrics calculated on resulting KIMRISS BML scores. Feasibility was assessed using the System Usability Scale (SUS). RESULTS: In most regions, automatically- and manually-placed grids demonstrated strong spatial similarity (e.g., mean femur Dice Coefficient = 0.78) and KIMRISS BML score agreement (mean intraclass correlation coefficients of 0.86 and 0.89 for baseline and change scores, respectively). SUS scores for automated grid placement were moderate (mean = 66.1). CONCLUSION: Automated grid placement is a reliable and feasible improvement to KIMRISS that could improve the ease and reproducibility of quantifying osteoarthritis in clinical trials.