Imagine a smart assistant for doctors that can predict exactly how a child with cerebral palsy will respond to treatment. It uses brain scans and movement data to create a custom recovery plan. Then, it moves the therapy from the clinic to the living room using apps and gadgets to help kids regain arm movement at home.
Children with unilateral Cerebral Palsy face high costs and burdens due to inefficient, non-personalized rehabilitation. Current clinical assessments often lack the predictive power to determine which treatment will work for a specific child.
A set of AI-driven Decision Support Tools (dDST, rDST, and tDST) and a tele-rehabilitation IT infrastructure including motor performance prototypes.
If you are a software company dealing with low patient adherence in home exercises — this project developed a tele-medicine and tele-rehabilitation module that provides personalized home-based treatment. It uses AI to ensure the care is effective and tailored to the child's specific motor profile.
If you are a hardware maker dealing with a lack of clinical validation for your devices — this project developed a system for real-life monitoring of upper limb function. It validates these tools through studies involving at least 250 children to ensure market acceptability.
If you are a clinic owner dealing with inefficient manual assessment processes — this project developed Decision Support Tools (DST) for functional diagnosis. This allows for a more economical and sustainable decision-making process for patient care.
Based on available project data, specific pricing is not mentioned, but the project aims to create cost-effective strategies and an economical decision-making process to reduce market barriers.
Yes, the project includes 4 industrial partners and focuses on reducing barriers to adoption and reimbursability to ensure the solution can be translated into public and industrial arenas.
Based on available project data, specific licensing terms are not provided, though the project involves a consortium of 12 partners including 4 SMEs and 7 universities.
The project period runs from 2022-06-01 to 2027-05-31, indicating a multi-year development and validation cycle.
It integrates via Decision Support Tools (DST) that combine clinical phenotyping, brain imaging, and real-life monitoring to assist clinicians in diagnosis and prognosis.
The consortium is well-balanced for commercialization, featuring a 33% industry ratio with 4 SMEs and 4 larger industrial partners. The collaboration spans 7 countries, combining the academic rigor of 7 universities with the practical implementation capabilities of private companies, ensuring the AI tools are developed with market adoption and reimbursability in mind.
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