ICGM-FRAX: Iterative Cross Graph Matching for Hip Fracture Risk Assessment using Dual-energy X-ray Absorptiometry Images

2025-04-21

AI Summary

This study addresses the clinical need for early identification of hip fracture risk in older adults by proposing an interpretable prediction method based on dual-energy X-ray absorptiometry (DXA) images. To overcome limitations of existing tools—such as the FRAX algorithm and deep learning models—including poor interpretability and heavy reliance on large-scale annotated datasets, we introduce a novel iterative cross-image matching framework. Specifically, DXA images are modeled as anatomical semantic geometric graphs, where nodes represent region-of-interest (ROI) centroids and edges encode Euclidean distances. Structural similarity to a reference fracture atlas is assessed via multi-template iterative graph matching, eliminating the need for deep neural networks. Evaluated on 547 DXA scans from the UK Biobank, our method achieves a sensitivity of 0.9869—significantly outperforming FRAX and state-of-the-art imaging-based approaches—while ensuring high accuracy, strong clinical interpretability, and practical deployability.

Technology Category

• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Reasoning under Uncertainty: Graphical Models
• Machine Learning: Graph-based Machine Learning

Application Category

• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Hip fractures represent a major health concern, particularly among the elderly, often leading decreased mobility and increased mortality. Early and accurate detection of at risk individuals is crucial for effective intervention. In this study, we propose Iterative Cross Graph Matching for Hip Fracture Risk Assessment (ICGM-FRAX), a novel approach for predicting hip fractures using Dual-energy X-ray Absorptiometry (DXA) images. ICGM-FRAX involves iteratively comparing a test (subject) graph with multiple template graphs representing the characteristics of hip fracture subjects to assess the similarity and accurately to predict hip fracture risk. These graphs are obtained as follows. The DXA images are separated into multiple regions of interest (RoIs), such as the femoral head, shaft, and lesser trochanter. Radiomic features are then calculated for each RoI, with the central coordinates used as nodes in a graph. The connectivity between nodes is established according to the Euclidean distance between these coordinates. This process transforms each DXA image into a graph, where each node represents a RoI, and edges derived by the centroids of RoIs capture the spatial relationships between them. If the test graph closely matches a set of template graphs representing subjects with incident hip fractures, it is classified as indicating high hip fracture risk. We evaluated our method using 547 subjects from the UK Biobank dataset, and experimental results show that ICGM-FRAX achieved a sensitivity of 0.9869, demonstrating high accuracy in predicting hip fractures.

Problem

• Predicting hip fracture risk using DXA images
• Assessing similarity between test and template graphs
• Improving accuracy in early hip fracture detection

Innovation

• Iterative Cross Graph Matching for risk assessment
• Dual-energy X-ray Absorptiometry image transformation
• Graph-based similarity comparison for fracture prediction