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

Grant number: 101017915

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Key facts

  • Disease

    COVID-19
  • Start & end year

    2021
    2024
  • Known Financial Commitments (USD)

    $7,353,363.12
  • Funder

    European Commission
  • Principal Investigator

    IONESCU Adrian
  • Research Location

    Switzerland
  • Lead Research Institution

    ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE
  • Research Priority Alignment

    N/A
  • Research Category

    Pathogen: natural history, transmission and diagnostics

  • Research Subcategory

    Diagnostics

  • Special Interest Tags

    Digital Health

  • Study Type

    Clinical

  • Clinical Trial Details

    Not applicable

  • Broad Policy Alignment

    Pending

  • Age Group

    Unspecified

  • Vulnerable Population

    Unspecified

  • Occupations of Interest

    Unspecified

Abstract

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.

Publicationslinked via Europe PMC

Last Updated:43 minutes ago

View all publications at Europe PMC

Use of a Silicon Microneedle Chip-Based Device for the Extraction and Subsequent Analysis of Dermal Interstitial Fluid in Heart Failure Patients.

pH Quantification in Human Dermal Interstitial Fluid Using Ultra-Thin SOI Silicon Nanowire ISFETs and a High-Sensitivity Constant-Current Approach.

Prognostic models in COVID-19 infection that predict severity: a systematic review.