Imagine having a digital copy of your own heart and brain that doctors can use to test treatments before they actually give them to you. This technology predicts how a person with an irregular heartbeat might suffer a stroke and how they will recover. It's like a flight simulator for medicine, allowing doctors to find the best path to health without guessing.
Healthcare systems struggle with the high cost and disability rates of AF-related strokes because treatments are not personalized. Current methods fail to predict individual risks and recovery paths accurately.
Virtual twin AI models and interoperable decision-support tools for the AF-related stroke pathway.
If you are a software company dealing with generic health apps — this project developed virtual twin AI models that provide individual risk and outcome predictions. This allows for the creation of precision decision-support tools for clinicians.
If you are a pharma company dealing with expensive clinical trials — this project developed in-silico simulated trials. This can accelerate research by testing drug impacts on virtual heart and brain models before human testing.
If you are a clinic dealing with unpredictable patient recovery rates — this project developed models to optimize rehabilitation strategies. This helps in tailoring physiotherapy to individual needs to reduce long-term disability.
Based on available project data, specific pricing or cost structures for the tools are not provided.
The project includes 5 industry partners and aims for swift adoption through commercial partners, suggesting a focus on industrial scalability.
Based on available project data, the specific licensing terms are not mentioned, though the project involves a consortium of 20 partners.
The project is developing interoperable decision support tools and a secure data integration platform to facilitate clinical adoption.
The project runs from 2024-01-01 to 2028-12-31, indicating that final validated tools will be available toward the end of 2028.
The consortium is heavily weighted toward academic research with 11 universities and 2 research institutes, but maintains a strong commercial focus with a 25% industry ratio (5 companies). With 20 partners across 10 countries, the project has the geographic reach and technical diversity to validate the AI models across different European healthcare systems.
Contact LUNDS UNIVERSITET in Sweden
Contact us to connect with the TARGET consortium for early-stage licensing discussions.
