An interpretable and trustworthy AI framework for large-scale longitudinal structure-pain association studies using data from the Osteoarthritis Initiative (OAI)
Quick Answer
This paper shows that An AI framework combining deep learning and interpretable modeling was developed to analyze structure-pain associations in osteoarthritis using OAI data.
Quick Take
It achieved significant improvements in predicting MRI-defined abnormalities, with Matthews correlation coefficients rising from 0.69 to 0.91 for bone marrow lesions. The study identified two pain trajectories, highlighting structural abnormalities as critical risk factors for pain progression.
Key Points
- Developed a deep learning framework for predicting MOAKS from knee MRIs.
- Incorporated conformal prediction for uncertainty quantification in model outputs.
- Matthews correlation coefficients improved: BML from 0.69 to 0.91, CART from 0.45 to 0.80.
- Identified two pain trajectories: rapid and stable progression.
- Structural abnormalities are critical risk factors for osteoarthritis pain.
Paper Resources
Article Content
From source RSS / original summaryarXiv:2606. 05357v1 Announce Type: new 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.
Want this in your inbox every morning?
Daily brief at your local 8am — bilingual EN/中文, free.
More from arXiv cs.AI
See more →HOBA: Hierarchical On-Policy Bidding Agents for Adaptive Online Advertising
HOBA (Hierarchical On-policy Bidding Agents) is a novel hierarchical reinforcement learning framework that enhances online advertising bidding systems by improving adaptability and reducing hyperparameter tuning costs. It utilizes a for hyperparameter inference, a SARSA agent for expert model selection, and a dynamic expert pool for bid execution, achieving a +3.6% increase in target cost during large-scale deployment and outperforming state-of-the-art baselines on AuctionNet.