Imagine having a digital twin of your metabolism that predicts if you're heading toward diabetes before it actually happens. Instead of just guessing based on a few blood tests, this tool combines real-time data from wearables with a deep understanding of how the body works. It helps people flip the switch back to health through simple lifestyle changes before medication becomes necessary.
Millions of adults have prediabetes, a reversible condition that often goes undiagnosed until it becomes Type-2 Diabetes. Current diagnostic methods lack real-time, personalized prediction to trigger timely lifestyle interventions.
A prototype prediction tool consisting of a web-based application for doctors and a mobile app for patients that integrates wearable sensor data with physics-informed machine learning.
If you are a health-tech app developer dealing with low user retention in wellness apps — this project developed a prototype tool that uses physics-informed machine learning to provide real-time prediabetic risk predictions. This allows users to see the direct impact of diet and activity on their specific risk levels.
If you are a wearable sensor manufacturer dealing with a lack of clinical utility for raw data — this project developed an algorithm that integrates glucose monitoring and bioimpedance data into a medical prediction tool. This transforms simple hardware into a diagnostic aid for reversing prediabetes.
If you are a private clinic network dealing with the 36% of European adults with undiagnosed diabetes — this project developed a web-based application for doctors to monitor patient data in real-time. This enables a proactive business model focused on prevention and reversal rather than chronic treatment.
Based on available project data, no specific pricing or cost structure has been disclosed; the project is currently in the prototype development phase.
The project aims to implement the algorithm in a web-based application and mobile app, suggesting a scalable digital architecture for wide deployment.
Based on available project data, specific licensing terms are not mentioned, though the project emphasizes transparency and reproducibility of the algorithms.
The system is designed to collect data from wearable sensors including glucose monitors, bioimpedance, heart rate, and accelerometers.
The project period runs from 2023-01-01 to 2025-12-31, indicating the prototype will be finalized by the end of 2025.
The consortium is highly balanced for commercialization, featuring 12 partners with a strong industry presence (42% industry ratio). With 5 industry players and 4 SMEs involved across 6 countries, the project is well-positioned to move from academic research to a commercial product, supported by a mix of 3 universities and 3 research institutes.
Contact SPINDOX LABS SRL in Italy for licensing and prototype access.
Contact us to connect with the PRAESIIDIUM consortium for early pilot integration.
