The project is based on long-term basic and applied research carried out by the Biophysics of Bone and Cartilage research group (http://luotain.uef.fi/) at the Department of Applied Physics, UEF, as well as on utilising these research findings in computational modelling.
Personalized treatment planning of osteoarthritis
Osteoarthritis is a common joint disease, affecting approximately 100 million people in the US and Europe. The societal burden of the disease is great: it is estimated that the direct and indirect costs of osteoarthritis amount to around 1–2.5% of GDP in Western countries. In the US alone, the overall costs of osteoarthritis exceed 300 billion dollars every year. Hence, the possibility to prevent the disease would result in great societal savings as well in better health for individuals.
Currently, it is impossible to predict the progression of osteoarthritis, as the disease tends to progress very differently among people. Moreover, the treatment planning is highly subjective and largely based on the national Current Care Guidelines. Yet, we know that the same course of treatment doesn’t necessarily work for everyone. Due to this, a great demand has emerged for novel methods predicting the progression of osteoarthritis on an individual-level.
In this project, we seek to lay both technological and commercial foundations for a new method that enables simple and personalized treatment planning for patients with osteoarthritis, as well as for those suffering from, e.g., joint damage.
Our method seeks to make it possible to predict the progression of osteoarthritis by using a personalized computational model that utilizes information from clinical imaging data (MRI and CT) . Later, this method can be used to simulate the effects of different treatments on the progression of osteoarthritis, and to find optimal treatment alternatives. The objective of this type of treatment planning is to prevent osteoarthritis or to slow down its progression.