AI辅助MRI精准量化膝关节软骨变化，助力骨关节炎治疗评估
Development, Evaluation, and Multicenter Clinical-Trial Application of an Artificial Intelligence-Assisted MRI Method for Quantitative Knee Cartilage Morphometry
- 用AI自动分割软骨，再经多人校正确保精度
- 8周治疗后软骨体积增加3.45%，厚度上升2.46%
- 适合临床试验中需高精度软骨测量的研究者
目的：在多中心三期骨关节炎试验中开发并评估一种AI辅助的定量膝关节软骨形态学MRI方法。方法：采用3D全分辨率nnU-Net进行AI预分割，版本1.0使用独立的股胫与髌骨模型，版本2.0则采用统一三类模型，基于金标准标注训练。影像经两读者修正及第三读者仲裁后，划分出内侧/外侧股骨、胫骨及髌骨软骨。软骨体积在物理坐标下测量，平均厚度通过3D射线追踪（3D-RT）计算，表面积小于1.5mm的区域采用3D射线基面积法（3D-RBA）。评估包含1,189例三期临床MRI检查、读者间一致性、20个模拟变薄模型以及69名受试者的纵向对比（与3D-PMA及三种对比方法）。结果：整体预分割Dice系数为0.964±0.030（中位数0.970），78.7%达到Dice≥0.95。软骨体积读者间ICC为0.959–0.995。69人子集显示，总软骨体积从V0的14,184.366 mm³增至V8的15,359.345 mm³；3D-RBA和3D-PMA分别下降4.70%和6.88%，所有四种厚度测量值在V8最高。20个几何实验中，平均绝对百分比误差（MAPE）为5.73%，一致性相关系数（CCC）为0.822，Dice为0.956。该流程应用于416名参与者共1,188次MRI检查。从V0到V8，治疗组软骨体积增长3.45%，平均厚度上升2.46%，3D-RBA下降4.54%；对照组分别为-2.08%、-1.32%、+0.16%。结论：该工作流为多中心骨关节炎试验提供了可重复的MRI软骨评估框架。跨方法一致性与几何验证支持3D-RT和3D-RBA用于治疗效果评估。
原文摘要 · Abstract (English)
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.
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