Related Experiment Video
Updated: Aug 12, 2025
Software-Assisted Quantitative Measurement of Osteoarthritic Subchondral Bone Thickness
Published on: March 18, 2022
Hip osteoarthritis: A novel network analysis of subchondral trabecular bone structures
Mohsen Dorraki1,2,3,4, Dzenita Muratovic5, Anahita Fouladzadeh6
1South Australian Health and Medical Research Institute (SAHMRI), Adelaide, SA 5000, Australia.
Insights
Network analysis of hip osteoarthritis (HOA) trabecular bone reveals distinct structural differences. This approach significantly improves diagnostic accuracy compared to traditional imaging, offering new insights into bone degeneration.
Area of Science:
- Biomedical Engineering
- Orthopedics
- Graph Theory
Background:
- Hip osteoarthritis (HOA) involves progressive destruction of hip joint bone and cartilage.
- Understanding HOA pathogenesis and early diagnosis are crucial for effective treatment development.
Purpose of the Study:
- To introduce a novel network analysis methodology for microcomputed tomography (micro-CT) images of human trabecular bone.
- To explore and identify differences in trabecular bone microstructure between healthy and HOA-affected femoral heads.
Main Methods:
- Automated extraction of trabecular bone networks from micro-CT images.
- Analysis of network properties, including edges, vertices, graph components, clustering coefficient, and characteristic path length.
- Development and evaluation of a deep learning model using both raw micro-CT images and extracted network data for HOA classification.
Main Results:
- Significant differences in trabecular bone network properties were found, particularly in the proximal femoral head.
- HOA networks exhibited altered small-world properties (decreased clustering, increased path length) and compressed structures (reduced edge length).
- A deep learning model utilizing extracted network data achieved 96.5% accuracy in classifying HOA, significantly outperforming models using only micro-CT images (74.6% accuracy).
Conclusions:
- Network analysis provides a novel perspective on bone microstructure in hip osteoarthritis.
- This graph theory-based approach reveals distinct topological differences in trabecular bone affected by HOA.
- The high accuracy of the network-based deep learning model suggests its potential for improved early diagnosis of hip osteoarthritis.
Abstract:
Hip osteoarthritis (HOA) is a degenerative joint disease that leads to the progressive destruction of subchondral bone and cartilage at the hip joint. Development of effective treatments for HOA remains an open problem, primarily due to the lack of knowledge of its pathogenesis and a typically late-stage diagnosis. We describe a novel network analysis methodology for microcomputed tomography (micro-CT) images of human trabecular bone. We explored differences between the trabecular bone microstructure of femoral heads with and without HOA. Large-scale automated extraction of the network formed by trabecular bone revealed significant network properties not previously reported for bone. Profound differences were discovered, particularly in the proximal third of the femoral head, where HOA networks demonstrated elevated numbers of edges, vertices, and graph components. When further differentiating healthy joint and HOA networks, the latter showed fewer small-world network properties, due to decreased clustering coefficient and increased characteristic path length. Furthermore, we found that HOA networks had reduced length of edges, indicating the formation of compressed trabecular structures. In order to assess our network approach, we developed a deep learning model for classifying HOA and control cases, and we fed it with two separate inputs: (i) micro-CT images of the trabecular bone, and (ii) the network extracted from them. The model with plain micro-CT images achieves 74.6% overall accuracy while the trained model with extracted networks attains 96.5% accuracy. We anticipate our findings to be a starting point for a novel description of bone microstructure in HOA, by considering the phenomenon from a graph theory viewpoint.
