An interpretable and trustworthy AI framework for large-scale longitudinal structure-pain association studies using data from the Osteoarthritis Initiative (OAI)
This study develops an AI framework combining deep learning-based MRI Osteoarthritis Knee Score (MOAKS) prediction with interpretable statistical modeling to study structure-pain relationships at scale using OAI data. Conformal prediction enables uncertainty quantification, filtering only high-confidence predictions, which substantially improves performance for bone marrow lesions (BML), cartilage loss (CART), and meniscal extrusion (ME) (MCC from 0.69 to 0.91, 0.45 to 0.80, 0.59 to 0.89 respectively). Using 2,175 knees, longitudinal latent class mixed modeling identifies rapid and stable pain trajectories, with odds ratios for rapid progression of 1.62 (BML), 1.83 (CART), and 2.50 (ME), highlighting these abnormalities as key risk factors for osteoarthritis pain and functional progression.
[2606.05357] An interpretable and trustworthy AI framework for large-scale longitudinal structure-pain association studies using data from the Osteoarthritis Initiative (OAI)
[Submitted on 3 Jun 2026]
Title:An interpretable and trustworthy AI framework for large-scale longitudinal structure-pain association studies using data from the Osteoarthritis Initiative (OAI)
View a PDF of the paper titled An interpretable and trustworthy AI framework for large-scale longitudinal structure-pain association studies using data from the Osteoarthritis Initiative (OAI), by Jincheng Yu and 7 other authors
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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.
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2606.05357 [cs.AI]
(or arXiv:2606.05357v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.05357
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Jincheng Yu [view email] [v1] Wed, 3 Jun 2026 18:59:38 UTC (771 KB)
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