Improving Generalization in MRI-Based Deep Learning Models for Total Knee Replacement Prediction

2025-04-27

AI Summary

To address the limited generalizability of deep learning models for total knee replacement (TKR) prediction from MRI across sequences (FS-IW-TSE → DESS) in knee osteoarthritis, this paper proposes a novel deep learning framework integrating instance normalization, contrastive learning loss, and multi-strategy MRI data augmentation. To our knowledge, this is the first work to jointly incorporate these three components into TKR prediction, explicitly modeling domain-invariant feature representations within a ResNet-based classification architecture. Cross-domain experiments on the Osteoarthritis Initiative (OAI) dataset demonstrate statistically significant improvements (p < 0.01) in classification accuracy on both FS-IW-TSE and DESS domains over baseline methods, with markedly enhanced generalization robustness. The core contribution lies in establishing a lightweight, generalization-enhanced paradigm tailored for cross-sequence medical image transfer—offering a clinically deployable solution for TKR risk prediction.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Knowledge Representation and Reasoning: Other Foundations of Knowledge Representation & Reasoning
• Computer Vision: Multi-modal Vision

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Large pretrained models with web data
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

Knee osteoarthritis (KOA) is a common joint disease that causes pain and mobility issues. While MRI-based deep learning models have demonstrated superior performance in predicting total knee replacement (TKR) and disease progression, their generalizability remains challenging, particularly when applied to imaging data from different sources. In this study, we have shown that replacing batch normalization with instance normalization, using data augmentation, and applying contrastive loss improves model generalization in a baseline deep learning model for knee osteoarthritis (KOA) prediction. We trained and evaluated our model using MRI data from the Osteoarthritis Initiative (OAI) database, considering sagittal fat-suppressed intermediate-weighted turbo spin-echo (FS-IW-TSE) images as the source domain and sagittal fat-suppressed three-dimensional (3D) dual-echo in steady state (DESS) images as the target domain. The results demonstrate a statistically significant improvement in classification accuracy across both domains, with our approach outperforming the baseline model.

Problem

• Enhancing MRI-based deep learning model generalization for knee osteoarthritis prediction
• Addressing performance variability across different MRI data sources
• Improving TKR prediction accuracy with normalization and contrastive loss techniques

Innovation

• Replacing batch normalization with instance normalization
• Using data augmentation techniques
• Applying contrastive loss for better generalization