The doctoral dissertation in the field of Applied Physics will be examined at the Faculty of Science, Forestry and Technology, Kuopio Campus and online.
What is the topic of your doctoral research? Why is it important to study the topic?
Knee osteoarthritis (OA) is an illness and disease that affects millions worldwide. Knee OA is an irreversible condition that diminishes the integrity of the tissues in the joint, leading to pain, and utterly affecting the mobility and quality of life of individuals. Accurately predicting the development of knee OA is critical for effectively preventing its ultimate incapacitating symptoms. In this regard, finite element modeling has demonstrated potential for this predictive purpose. However, generating and simulating fully personalized knee joint models is time-demanding, prohibiting its application to numerous individuals and fast-paced clinical environments.
What are the key findings or observations of your doctoral research?
The studies in this thesis investigated the potential of exploiting computational modeling and simulation to predict the development of radiographic knee OA in numerous subjects. The findings in this thesis suggest that the same biomechanical conclusions can be achieved either using either commercial or open-source finite element software, supporting the use of open tools in research. Furthermore, using artificial neural network estimations of the personalized force in the knee works better for the stratification of subjects that will develop severe knee OA from subjects that remain healthy, compared to using generic peak forces in the joint.
How can the results of your doctoral research be utilised in practice?
As a result, combining physics- and data-driven methods is promising for complementing clinical evaluations for individualized knee OA prevention. We proposed a pipeline that uses simple anatomic, demographic, and biomechanical information for the generation and simulation of individualized knee joint models. The model results will allow patients and clinicians to visualize the effects of intervention strategies before OA starts. By this method, we avoided the major time-related drawbacks of finite element methods to be scalable to multiple subjects. We skipped the manual segmentation of the complex tissues in the knee from medical images and the cumbersome motion analysis that is usually required for estimating the forces in the joint.
What are the key research methods and materials used in your doctoral research?
We started with a literature review to point out the current trends in biomechanical studies of the knee aiming for expediting the process of knee modeling by finite element analysis. We then continued developing, and making available, a knee joint model using open-source finite element software. Subsequently, we further developed a template-based approach to personalize knee joint finite element models using simple anatomical measurements. We added to the template-based method the subject-specific predictions of joint contact forces in the knee by artificial neural networks, which use simple demographic and biomechanical information for this purpose.
The doctoral dissertation of Alexander Paz Carvajal, BSc, entitled Novel methodologies for generation and simulation of knee joint finite element models: toward a template-based and neural network-assisted model for rapid prediction of knee osteoarthritis will be examined at the Faculty of Science, Forestry and Technology, Kuopio Campus. The opponent will be Associate Professor Clare Fitzpatrick, Boise State University, and the custos will be Adjunct Professor Mika E. Mononen, University of Eastern Finland. Language of the public defence is English.
For more information, please contact:
Alexander Paz Carvajal, alexander.paz@uef.fi
