Morphology-Aware KOA Classification: Integrating Graph Priors with Vision Models

2025-10-20

AI Summary

Accurate radiographic grading of knee osteoarthritis (KOA) remains challenging due to subtle morphological features that standard deep learning models fail to capture effectively. To address this, we propose a multimodal framework integrating anatomical priors with visual representations: first, the Segment Anything Model generates anatomical segmentation maps of the knee joint to construct geometric priors; second, a graph neural network encodes the structural topology of these maps, and cross-modal alignment between graph embeddings and visual features is achieved via mutual information maximization. Evaluated on the Osteoarthritis Initiative (OAI) dataset, our method achieves 79.6% classification accuracy—surpassing unimodal baselines by 10.2% and outperforming current state-of-the-art methods by 8.3% in accuracy and 11.1% in F1 score. The framework significantly enhances modeling of pathology-relevant anatomical variations, demonstrating improved interpretability and robustness in capturing disease-specific structural changes.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Knee osteoarthritis (KOA) diagnosis from radiographs remains challenging due to the subtle morphological details that standard deep learning models struggle to capture effectively. We propose a novel multimodal framework that combines anatomical structure with radiographic features by integrating a morphological graph representation - derived from Segment Anything Model (SAM) segmentations - with a vision encoder. Our approach enforces alignment between geometry-informed graph embeddings and radiographic features through mutual information maximization, significantly improving KOA classification accuracy. By constructing graphs from anatomical features, we introduce explicit morphological priors that mirror clinical assessment criteria, enriching the feature space and enhancing the model's inductive bias. Experiments on the Osteoarthritis Initiative dataset demonstrate that our approach surpasses single-modality baselines by up to 10% in accuracy (reaching nearly 80%), while outperforming existing state-of-the-art methods by 8% in accuracy and 11% in F1 score. These results underscore the critical importance of incorporating anatomical structure into radiographic analysis for accurate KOA severity grading.

Problem

• Improving knee osteoarthritis classification by integrating anatomical graphs with vision models
• Addressing subtle morphological details missed by standard deep learning approaches
• Enhancing radiographic analysis through geometry-informed feature alignment and clinical priors

Innovation

• Integrates graph priors with vision models
• Enforces alignment through mutual information maximization
• Constructs graphs from anatomical features