BioTrib-AVN: The effect of avascular necrosis of the hip on articular function in natural and artificial joints
Lead Research Organisation:
University of Leeds
Department Name: Mechanical Engineering
Abstract
The Postdoctoral Fellowship provides the opportunity for the applicant to undertake research and research training in one of Europe's foremost medical engineering research groups at the University of Leeds. The fellowship seeks to enhance the modelling approaches already developed by the applicant in bone remodelling with the advanced cartilage models being generated in Leeds to provide a key tool to mitigate the rising incidence of avascular necrosis (AVN) in Asia. AVN is a destructive disease, mainly found in the younger population, arising due to the disruption of blood supply to the femur head. It is the most common diagnosis (40%) for patients undergoing total hip replacement (THR) in Asia. Some patients may have the risk of collapse of the femoral head, depending upon the size of the lesion. Studies have observed a reduction in strength and stiffness in the pre-collapse stage of the AVN, which reduces the structural competence of the bone as well as the adjacent articular cartilage that covers the femoral head. Any disruption of the articular cartilage that arises from abnormal loading due to damage in the underlying bone has severe consequences for the overall joint function and ultimately pain and disability. The proposed research will investigate the link between AVN induced bone deterioration and joint function - biotribology - using a dynamic computational model that incorporates the evolution of the AVN from a bone function perspective. In the first instance, this will be an examination of the relationship between the cartilage function and bone properties in early stage AVN there are more conservative treatments. Such interventions are in urgent need of scientific underpinning; an aim that this model could achieve in the medium term. The second project, which will be delivered whilst on placement, will focus on late stage interventions for AVN, that is surface replacement where the design will be considered in terms of bone remodelling.
Publications
| Description | This award has facilitated three key developments in the computational biotribology of human joints, with one specific to Avascular Necrosis. 1. Characterisation of poroelastic material parameters for articular cartilage based on combined experimental indentation and computational modelling. 2. Representative simulation of the friction and lubrication of human joints in motion using three-dimensional point contact geometry. 3. Analysis of avascular necrosis on functional human hip joints with the capability to assess disease state versus tribological performance in terms of friction. |
| Exploitation Route | Characterisation of poroelastic models for cartilage can be used by researchers to effectively simulate material properties an analyse a range of biomedical applications. This includes designing cartilage repair materials with similar properties and tribological performance, investigating the mechanical performance of degraded cartilage through conditions such as arthritis, and exploring the behaviour of other bodily tissues which also exhibit poroelastic behaviour. Models of healthy joint operation will allow researchers to understand geometric and gait cycle variations on the tribological performance. This will allow engineers and clinicians to understand the functionality which joints maintain when under load which will facilitate improved joint replacement design through matching of conditions, and clinical diagnosis when deviation from the effective baseline can be readily identified. Predictive models of avascular necrosis progression will allow clinicians to assess different stages of the disease and the affect this has mechanically and tribologically in terms of load and friction on the potential to save further degradation. These tools have the potential to enhance diagnosis through analysis of the size, location, and shape of lesions on whether rehabilitation is likely. Model training is necessary across a wide range of patient data in order to achieve this. |
| Sectors | Digital/Communication/Information Technologies (including Software) Healthcare Pharmaceuticals and Medical Biotechnology |
| Description | A collaboration across multiple researchers in the same academic group materialised which led to an investigation of poroelastic material parameters for cartilage, this required experimental and computational researchers to work together to produce the necessary workflow and datasets required. Led to a paper under review on this process, new pathways for validation realised. Use of neural networks to combine experimental data and computational data of joint simulator load cells was realised with Simulation Solutions to develop a new method for calibration. This enabled an efficient and less resource intensive process for industry to use when setting up simulator test protocols, led to a technical research paper under review. Collaboration with clinical experts has allowed patient data on necrotic tissues to be combined with computational model developments to investigate a range of AVN cases. This has allowed clinical experts to understand and disseminate the use of models for supporting challenging diagnosis and decision making on interventional surgery. Led to a jointly written publication under review and the potential of follow on funding aimed at supporting clinical trials and further model development under this area. |
| First Year Of Impact | 2024 |
| Sector | Digital/Communication/Information Technologies (including Software),Healthcare,Pharmaceuticals and Medical Biotechnology |
| Impact Types | Societal |
| Title | Porohyperelastic Hip Joint Cartilage Contact Model for Assessing Avascular Necrosis Impact on Biotribology |
| Description | This research tool is an advanced in vitro computational porohyperelastic model developed to simulate cartilage contact mechanics in the hip joint under physiological and pathological conditions. Unlike traditional biphasic models, which assume incompressibility of both solid and fluid phases, this model incorporates compressibility in both phases using principles of continuum mechanics. It couples fluid pressure and solid deformation to predict stress distribution, fluid flow, lubrication and load-bearing behavior in cartilage. The model is parameterized using material properties such as solid phase modulus, permeability, and compressibility coefficients, validated against empirical data. It specifically evaluates how avascular necrosis (AVN) alters cartilage biotribology by simulating, subsequent tissue degradation, and changes in mechanical properties. |
| Type Of Material | Model of mechanisms or symptoms - human |
| Year Produced | 2023 |
| Provided To Others? | No |
| Impact | Enhanced Predictive Capability: The model addresses limitations of biphasic models (e.g., reliance on empirical fitting) by providing a physics-based framework for complex loading scenarios. This improves accuracy in predicting long-term wear, fluid redistribution, and stress concentrations in diseased cartilage, aiding in early intervention strategies. Reduced Reliance on Animal Models: By enabling high-fidelity in vitro simulation of AVN progression and its biomechanical consequences, the tool reduces the need for animal studies to investigate cartilage degeneration or test therapeutic interventions. Clinical Relevance: The model identifies critical thresholds of permeability and stiffness associated with AVN-driven cartilage failure, offering insights into disease progression and potential biomarkers for clinical diagnosis. Framework for Personalized Medicine: The adaptable parameters allow customization to patient-specific data (e.g., imaging-derived material properties), supporting personalized treatment planning for hip preservation surgeries or regenerative therapies. The methodology can be extended to other joints (e.g., knee, shoulder) or pathologies (e.g., osteoarthritis), advancing research in musculoskeletal biomechanics and biotribology. |
| Title | FE Hip Joint Models for Cartilage Biotribology Analysis in Avascular Necrosis |
| Description | This research dataset comprises three computational models, that integrate porohyperelastic theory, thin film theory and are parameterized using empirical data from cartilage samples, to study cartilage biotribology in the hip joint under AVN: 2D Finite Element (FE) Model: Simulates cartilage-cartilage contact mechanics in a simplified 2D cross-section of the hip joint, focusing on stress distribution, fluid flow, hydration effects and dynamic loading. 3D FE Model with plane of symmetry: Captures complex 3D geometry of the hip joint to analyze radial and circumferential strain, fluid pressure gradients, and load redistribution during gait cycles. Cartilage Plug Indentation Model: Validates computational predictions against in vitro indentation experiments using cartilage plugs, correlating poroelastic parameters (e.g., aggregate modulus H?, permeability k) with mechanical responses under confined compression. |
| Type Of Material | Computer model/algorithm |
| Year Produced | 2023 |
| Provided To Others? | No |
| Impact | Improved Understanding of AVN Biotribology: The models enable the simulation of avascular necrosis, by modelling of material property changes. Validation Framework: The cartilage plug indentation model bridges computational and experimental data, enabling rigorous validation of poroelastic parameters and reducing reliance on iterative empirical fitting. Reduced Animal Use (3Rs): By simulating AVN progression and cartilage degradation in silico, these models minimize the need for animal studies to investigate hip joint biomechanics or test therapeutic interventions. Enhanced Predictive Capabilities. The models go beyond limitations of traditional biphasic approaches |
| Description | Collaboration with LIRMM |
| Organisation | University of Leeds |
| Department | Leeds Institute of Rheumatic and Musculoskeletal Medicine |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | Development of computational models of AVN degraded hips. Necrotic tissue shape, size, and location driven through MRI images provided by clinicians. |
| Collaborator Contribution | MRI images of patients with AVN in their hips were anonymised, various stages of the disease progression were considered. Data provided to researchers to generate computational models to assess AVN progression. |
| Impact | Multi-disciplinary collaboration with fundamental biotribology developed by researchers and clinical expertise and application provided by collaborators. The outcome is a model capable of assessing the mechanical performance of hips with various stages of AVN progression, clinicians able to link necrotic tissue size, location, and shape to understand whether rehabilitation with or without intervention is possible. Ongoing research required toward publication with data collections and stochastic analysis required to draw accurate conclusions of the model capability continuing. |
| Start Year | 2024 |
| Description | Collaboration with SimSol |
| Organisation | Simulation Solutions |
| Country | United Kingdom |
| Sector | Private |
| PI Contribution | Development of models to design and optimise load cell placements within joint simulator rigs, in addition development of an automated framework for load cell calibration using neural networks. |
| Collaborator Contribution | Manufacture and experimental testing of load cells within joint simulators to provide validation of model framework. Experimental data for sensor calibration and relevant testing for validation of neural network predictions. |
| Impact | Internal report on the use of computational models for the design and optimisation of load cell placement within joint simulator, secondment #1 deliverable 2023. Furthered to short technical paper on calibration of load cells using neural network trained on combined experimental and computational model predictions, to be submitted for publication. |
| Start Year | 2023 |