Periodic Reporting for period 1 - ALGOA (Novel algorithm for treatment planning of patients with osteoarthritis)
Okres sprawozdawczy: 2018-01-01 do 2019-06-30
Introduction
Osteoarthritis affects approximately 100 million people in the US and EU area. This causes enormous economic costs for societies. The most cost-effective and helpful treatment for the knee osteoarthritis would be prevention. However, it is currently not possible, and current methods for rehabilitation and treatment of osteoarthritis rely on general guidelines. Furthermore, prevention is possible only if the disease progression can be predicted and personalized interventions can be provided for patients.
In our recent ERC starting grant project, we developed an algorithm that was able to predict the progression of osteoarthritis. Provided that this kind of algorithm was in clinical use, different interventions could be tested and preventive, personalized treatments could be started before clinically significant symptoms were observed.
In the ERC proof-of-concept project, we further validated the aforementioned algorithm and carried out pre-commercialization actions.
Results
Summary of the most important results:
A) Technological proof-of-concept
We further developed the algorithm and tested it with various subject groups. In particular, the algorithm was fully able to predict osteoarthritis progression for overweight subjects and categorize them to the experimentally defined groups (healthy: KL0, early osteoarthritis: KL2, severe osteoarthritis: KL3) based on the 4-year follow-up data. The model also predicted well locations susceptible to osteoarthritis progression in patient with anterior cruciate injury and reconstruction, as characterized experimentally by MRI.
We built a user-friendly interface for the algorithm, in which most of the model generation phases are now automatized. In its current form, the developed tool produces personalized predictions for the onset and progression of knee osteoarthritis within two hours.
B) Pre-commercialization actions
We made an invention disclosure and performed a market survey to understand existing and future market sizes for the developed approach. The market survey concluded that our innovation has a very strong commercial potential and multiple possible ways to go into the market. It suggested 3 possible business models for the future. Hospitals, insurance companies and medical device (MRI/CT) manufacturers could be potential partners.
Conclusion
The developed tool was able to simulate personalized onset and progression of osteoarthritis for different patient groups. By applying the tool for simulation of interventions, in the future, we should be able to give a medical doctor a valuable method to make correct and cost-effective treatment decisions. As this kind of method is not commercially available, there are good possibilities for market entry to make profitable business in the future. We have now 3 possible business models and have already attracted more funding for further development of the method and especially for business development.
Socioeconomic impact
It’s too early to say what is going to be the socioeconomic impact of our innovation. Simulation of different interventions of patients could be used to plan the best possible personalized treatments, delaying or preventing the development of osteoarthritis. This should lead to longer working careers and lesser absences from work, improving productivity. Furthermore, since subjects should be able to perform longer normal physical activities, the personalized treatment should prevent disability and social isolation, which have been shown to increase life expectancy.
Osteoarthritis affects approximately 100 million people in the US and EU area. This causes enormous economic costs for societies. The most cost-effective and helpful treatment for the knee osteoarthritis would be prevention. However, it is currently not possible, and current methods for rehabilitation and treatment of osteoarthritis rely on general guidelines. Furthermore, prevention is possible only if the disease progression can be predicted and personalized interventions can be provided for patients.
In our recent ERC starting grant project, we developed an algorithm that was able to predict the progression of osteoarthritis. Provided that this kind of algorithm was in clinical use, different interventions could be tested and preventive, personalized treatments could be started before clinically significant symptoms were observed.
In the ERC proof-of-concept project, we further validated the aforementioned algorithm and carried out pre-commercialization actions.
Results
Summary of the most important results:
A) Technological proof-of-concept
We further developed the algorithm and tested it with various subject groups. In particular, the algorithm was fully able to predict osteoarthritis progression for overweight subjects and categorize them to the experimentally defined groups (healthy: KL0, early osteoarthritis: KL2, severe osteoarthritis: KL3) based on the 4-year follow-up data. The model also predicted well locations susceptible to osteoarthritis progression in patient with anterior cruciate injury and reconstruction, as characterized experimentally by MRI.
We built a user-friendly interface for the algorithm, in which most of the model generation phases are now automatized. In its current form, the developed tool produces personalized predictions for the onset and progression of knee osteoarthritis within two hours.
B) Pre-commercialization actions
We made an invention disclosure and performed a market survey to understand existing and future market sizes for the developed approach. The market survey concluded that our innovation has a very strong commercial potential and multiple possible ways to go into the market. It suggested 3 possible business models for the future. Hospitals, insurance companies and medical device (MRI/CT) manufacturers could be potential partners.
Conclusion
The developed tool was able to simulate personalized onset and progression of osteoarthritis for different patient groups. By applying the tool for simulation of interventions, in the future, we should be able to give a medical doctor a valuable method to make correct and cost-effective treatment decisions. As this kind of method is not commercially available, there are good possibilities for market entry to make profitable business in the future. We have now 3 possible business models and have already attracted more funding for further development of the method and especially for business development.
Socioeconomic impact
It’s too early to say what is going to be the socioeconomic impact of our innovation. Simulation of different interventions of patients could be used to plan the best possible personalized treatments, delaying or preventing the development of osteoarthritis. This should lead to longer working careers and lesser absences from work, improving productivity. Furthermore, since subjects should be able to perform longer normal physical activities, the personalized treatment should prevent disability and social isolation, which have been shown to increase life expectancy.