用AI精准预测膝关节结构异常，揭示疼痛进展的关键风险因素
An interpretable and trustworthy AI framework for large-scale longitudinal structure-pain association studies using data from the Osteoarthritis Initiative (OAI)
- 结合深度学习与可解释建模，从MRI直接预测骨关节炎病变
- 预测准确率显著提升，关键结构异常与疼痛进展关联性强
- 适合医学研究者和临床决策支持系统开发者参考
目的：构建一个可解释且可信的AI框架，结合基于深度学习的MRI骨关节炎膝关节评分（MOAKS）预测与可解释统计建模，利用骨关节炎倡议计划（OAI）数据大规模研究结构-疼痛关系。方法：首先开发深度学习模型直接从膝关节MRI预测MOAKS特征，并引入置信区间校准提供预测不确定性量化；该策略可显式过滤输出，仅保留高置信度的膝关节级预测。其次，采用纵向潜在类别混合模型（LCMM）分析关键结构异常与四种互补膝痛测量值的关系。结果：在三种MRI定义的异常（骨髓病变BML、软骨丢失CART、半月板外移ME）中，框架显著提升了马修斯相关系数（MCC）等指标：BML从0.69升至0.91，CART从0.45升至0.80，ME从0.59升至0.89。基于高置信度预测，将样本量扩展至2,175个膝关节进行LCMM分析。识别出两种疼痛轨迹（快速进展型与稳定型），快速进展组的估计比值比（95%CI）分别为：BML为1.62（1.12–2.35），CART为1.83（1.24–2.70），ME为2.50（1.75–3.57）。结论：这些结果强调了上述结构异常作为骨关节炎疼痛与功能恶化的重要风险因子。
原文摘要 · Abstract (English)
Purpose: To develop an interpretable and trustworthy AI framework that combines deep learning based MRI Osteoarthritis Knee Score (MOAKS) prediction with interpretable statistical modeling to study structure-pain relationships at scale using data from the Osteoarthritis Initiative (OAI). Materials and Methods: We first developed a deep learning framework to predict MOAKS features directly from knee MRIs and incorporated conformal prediction to provide prediction uncertainty quantification. This uncertainty-aware strategy enables explicit filtering of model outputs, retaining only high-confidence MOAKS predictions at the knee level. Second, we applied a longitudinal latent class mixed model (LCMM) to examine associations between key structural abnormalities and four complementary knee pain measurements. Results: Among the three MRI-defined abnormalities (i.e., bone marrow lesions (BML), cartilage loss (CART), and meniscal extrusion (ME)), our framework substantially improved the Matthews correlation coefficient (MCC) and some other metrics. For example, MCC increased from 0.69 to 0.91 for BML, from 0.45 to 0.80 for CART, and from 0.59 to 0.89 for ME. Using these high-confidence predictions, we expanded the sample size to 2,175 knees for the LCMM analysis. Two distinct pain trajectories were identified (rapid and stable pain progression). The estimated odds ratios (95% CI) for the rapid progression group were 1.62 (1.12-2.35) for BML, 1.83 (1.24-2.70) for CART loss, and 2.50 (1.75-3.57) for ME. Conclusion: These results highlight the importance of these structural abnormalities as risk factors for pain and functional progression in osteoarthritis.
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