An interpretable and trustworthy AI framework for large-scale longitudinal structure-pain association studies using data from the Osteoarthritis Initiative (OAI)

2026-06-03

AI Summary

This study addresses the lack of interpretability and uncertainty quantification in structure–pain association analyses within large-scale longitudinal osteoarthritis research. To this end, it integrates deep learning, conformal prediction, and latent class mixed models (LCMMs) for longitudinal data. The approach first leverages MRI to automatically predict MOAKS features while rigorously quantifying prediction uncertainty, enabling the selection of high-confidence samples for downstream analysis. This strategy substantially improves MOAKS prediction performance—for instance, increasing the Matthews correlation coefficient for bone marrow lesions from 0.69 to 0.91—and expands the analyzable cohort to 2,175 knees. The framework identifies two distinct knee pain progression trajectories and precisely quantifies the risk effects of bone marrow lesions, cartilage loss, and meniscal extrusion on rapid pain progression, thereby enabling highly reliable and interpretable large-scale association modeling.

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). 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.

Problem

• structure-pain association
• osteoarthritis
• longitudinal study
• MRI
• knee pain

Innovation

• conformal prediction
• interpretable AI
• deep learning
• longitudinal modeling
• MOAKS