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Completed RESEARCH GRANT Europe PMC

Edge AI-deployed DIGItal Twins for PREDICTing disease progression and need for early intervention in infectious and cardiovascular diseases beyond COVID-19

€59.78M EUR

Funder European Commission
Recipient Organization Ecole Polytechnique Federale de Lausanne
Country Based in EU
Start Date Jan 01, 2021
End Date Dec 31, 2024
Duration 1,460 days
Number of Grantees 11
Roles Participant; Coordinator; Award Holder
Data Source Europe PMC
Grant ID 101017915
Grant Description

The interplay between viral infection, host response, development of (hyper)inflammation and cardiovascular injury in COVID-19 is currently poorly understood which makes it difficult to predict which patients remain with mild symptoms only and which patients rapidly develop multi organ failure.

The solution offered by DIGIPREDICT is an Edge Artificial Intelligence (AI) based, high-tech personalized computational and physical Digital Twin vehicle representing patient-specific (patho)physiology, with embedded disease progression prediction capability, focusing on COVID-19 and beyond.

DIGIPREDICT proposes the first of its kind Digital Twin, designed, developed and calibrated on i) patient measurements of various Digital Biomarkers and their interaction, ii) Organ-On-Chips (OoCs) as physical counterpart using patient blood for personalized screening and iii) integration of those physiological readouts using AI at Edge technologies.

The final goal is to identify and validate patient-specific dynamic digital fingerprints of complex disease state and prediction of the progression as a basis for assistive tools for medical doctors and patients.

Using and improving state-of-the-art OoCs and Digital Biomarkers (for physiology and biomarkers in interstitial fluid) we will measure detailed response to viral infection.

By closely monitoring the response with wearable multi-modal Edge AI patches, we aim to predict in near real-time the progression of the disease, support early clinical decision and to propose patient-specific therapy using existing drugs.

We will combine scientific and technical excellence in a highly multi- and inter-disciplinary project, bringing together medical, biological, electronical, computer, signal processing and social science communities around Europe to setup Digital Twin at Edge.

We will enable an Edge-to-Cloud vision, significantly advancing current state of the art and setting up a new European community for researching and applying Digital Twins.

All Grantees

Stichting Imec Nederland; Sciprom Sarl; Interuniversitair Micro-Electronica Centrum; Universiteit Twente; Universitaet Bern; Ecole Polytechnique Federale de Lausanne; Eidgenoessische Technische Hochschule Zuerich; Charite - Universitaetsmedizin Berlin; E.P.O.S. Iasis Research and Development Ltd; Ascilion Ab

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