Project description
AI patches to predict real-time disease progression
COVID-19 is associated with so many different symptoms – from a sore throat and the loss of taste to more serious ones like lung failure. Is there a way to predict how serious the disease will be when it first manifests? The EU-funded DIGIPREDICT project is developing a system based on AI that can predict whether COVID-19 patients will develop severe cardiovascular complications and, in the longer term, detect the likely onset of inflammatory disease. Specifically, it proposes the first of its kind Digital Twin that will consist of a smart patch with integrated technology for collecting a range of medical data. This will be able to monitor how well the treatment is working.
Objective
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.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
- medical and health sciencesbasic medicinephysiologypathophysiology
- engineering and technologyother engineering and technologiesmicrotechnologyorgan on a chip
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Keywords
Programme(s)
Call for proposal
(opens in new window) H2020-FETPROACT-2018-2020
See other projects for this callSub call
H2020-FETPROACT-2020-2
Funding Scheme
RIA - Research and Innovation actionCoordinator
1015 Lausanne
Switzerland