Imagine a digital brain map that helps doctors see how a stroke damaged a patient's mind. This tool uses AI to create a personalized recovery plan, almost like a GPS for healing. It connects hospital data with home-based gaming exercises to make sure patients get the right therapy at the right time.
Stroke rehabilitation is expensive and inefficient due to staff shortages and a lack of personalized treatment plans. This leads to high chronic care costs and suboptimal patient recovery.
An integrated AI health platform featuring a Bayesian Inference Engine, a whole-brain simulation model, and a Rehabilitation Gaming System for home use.
If you are a software provider dealing with low patient adherence to home exercises — this project developed a Rehabilitation Gaming System and AI decision-support that improves personalized treatment recommendations for patients at home.
If you are a clinic owner dealing with staff shortages and skill gaps — this project developed an AI health platform that automates clinical interpretation and optimizes intervention delivery to reduce the burden on clinicians.
If you are a manufacturer dealing with raw data that lacks clinical meaning — this project developed signal processing toolboxes and whole-brain simulation that turn EEG and fMRI data into actionable recovery predictions.
Based on available project data, specific pricing for the end-user is not listed, but the project is designed to align with Value-Based Healthcare to reduce the 60 billion euro annual cost of stroke in Europe.
Yes, the project aims for a TRL6 integrated platform, meaning it is designed for demonstration in a clinical environment across 10 different countries.
Based on available project data, the project focuses on developing legal and ethical guidelines for deployment, but specific licensing terms for the Bayesian Inference Engine or Knowledge Graph are not provided.
The platform integrates validated systems for data acquisition, cloud computing, and virtual research environments to solve interoperability issues.
The project period runs from 2022-12-01 to 2026-11-30, suggesting the validated platform will be ready toward the end of 2026.
The consortium is well-balanced for commercialization, featuring a 33% industry ratio with 6 industrial partners, including 3 SMEs. With 18 partners across 10 countries, the project has strong cross-border validation capabilities, combining academic research from 7 universities with practical application from industry and research centers.
Contact Universidad Miguel Hernandez de Elche regarding the AISN platform integration
Contact SciTransfer to explore licensing opportunities for the AI Decision-Support Module.
