An interpretable and trustworthy AI framework for large-scale longitudinal structure-pain association studies using data from the Osteoarthritis Initiative (OAI)
An interpretable and trustworthy AI framework for large-scale longitudinal structure-pain association studies using data from the Osteoarthritis Initiative (OAI)
用于大规模纵向结构-疼痛关联研究的可解释且可信的 AI 框架：基于骨关节炎倡议 (OAI) 数据
Abstract: 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).
摘要： 目的：开发一个可解释且可信的 AI 框架，该框架结合了基于深度学习的 MRI 骨关节炎膝关节评分 (MOAKS) 预测与可解释的统计建模，旨在利用骨关节炎倡议 (OAI) 的数据进行大规模的结构-疼痛关系研究。
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.
材料与方法： 我们首先开发了一个深度学习框架，直接从膝关节 MRI 中预测 MOAKS 特征，并引入了共形预测 (conformal prediction) 来提供预测不确定性量化。这种具有不确定性感知能力的策略能够对模型输出进行显式过滤，仅保留膝关节层面高置信度的 MOAKS 预测结果。其次，我们应用了纵向潜在类别混合模型 (LCMM) 来研究关键结构异常与四种互补的膝关节疼痛测量指标之间的关联。
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.
结果： 在三种 MRI 定义的异常（即骨髓病变 (BML)、软骨丢失 (CART) 和半月板挤压 (ME)）中，我们的框架显著提高了马修斯相关系数 (MCC) 及其他一些指标。例如，BML 的 MCC 从 0.69 提高到 0.91，CART 从 0.45 提高到 0.80，ME 从 0.59 提高到 0.89。利用这些高置信度的预测结果，我们将 LCMM 分析的样本量扩大到了 2,175 个膝关节。研究识别出了两种截然不同的疼痛轨迹（快速进展和稳定进展）。快速进展组的估计优势比 (95% CI) 分别为：BML 为 1.62 (1.12-2.35)，软骨丢失为 1.83 (1.24-2.70)，ME 为 2.50 (1.75-3.57)。
Conclusion: These results highlight the importance of these structural abnormalities as risk factors for pain and functional progression in osteoarthritis.
结论： 这些结果强调了上述结构异常作为骨关节炎疼痛和功能进展风险因素的重要性。