Development, Evaluation, and Multicenter Clinical-Trial Application of an Artificial Intelligence-Assisted MRI Method for Quantitative Knee Cartilage Morphometry

RESUMO

Objective: To develop and evaluate an AI-assisted MRI method for quantitative knee cartilage morphometry in a multicenter phase III knee osteoarthritis trial. Methods: AI pre-segmentation used 3D full-resolution nnU-Net. Version 1.0 used separate femorotibial- and patellar-cartilage models, whereas version 2.0 used a unified three-class model trained on gold-standard annotations. Trial images then underwent two-reader correction and third-reader adjudication. Adjudicated masks were partitioned into medial/lateral femoral and tibial cartilage plus patellar cartilage. Cartilage volume was measured in physical coordinates, mean thickness by 3D ray tracing (3D-RT), and surface area with local thickness <1.5 mm by a 3D ray-based area method (3D-RBA). Evaluation included 1,189 phase III MRI examinations, reader agreement, 20 synthetic thinning models, and a 69-participant longitudinal comparison with 3D-PMA and three comparator thickness methods. Results: Overall pre-segmentation Dice was 0.964 +/- 0.030 (median 0.970), with 78.7% achieving Dice >=0.95. Inter-reader ICCs for cartilage volume were 0.959-0.995. In the 69-participant subset, total cartilage volume increased from 14,184.366 mm^3 at V0 to 15,359.345 mm^3 at V8; 3D-RBA and 3D-PMA decreased by 4.70% and 6.88%, and all four thickness measures were highest at V8. In 20 geometric experiments, MAPE was 5.73%, CCC 0.822, and Dice 0.956. The workflow was applied to 1,188 MRI examinations from 416 participants. From V0 to V8, the treatment group showed +3.45% total cartilage volume, +2.46% mean thickness, and -4.54% 3D-RBA, versus -2.08%, -1.32%, and +0.16% in controls. Conclusion: This workflow provided a reproducible MRI cartilage assessment framework for a multicenter KOA trial. Cross-method agreement and geometric validation supported 3D-RT and 3D-RBA for therapeutic efficacy evaluation.