Instant identification of biomarkers of COVID 19 by applying AI on structured reporting of Chest CT integrating clinical information

  • Funded by Bundesministerium für Bildung und Forschung [German Federal Ministry of Education and Research] (BMBF)
  • Total publications:0 publications

Grant number: 01KI2054A

Grant search

Key facts

  • Disease

    COVID-19
  • Start & end year

    2020
    2020
  • Known Financial Commitments (USD)

    $96,653.97
  • Funder

    Bundesministerium für Bildung und Forschung [German Federal Ministry of Education and Research] (BMBF)
  • Principle Investigator

    Pending
  • Research Location

    Germany, Europe
  • Lead Research Institution

    University Heidelberg, Mint Medical GmbH, Deutsches Krebsforschungszentrum (DKFZ)
  • Research Category

    Pathogen: natural history, transmission and diagnostics

  • Research Subcategory

    Diagnostics

  • Special Interest Tags

    Gender

  • Study Subject

    Clinical

  • Clinical Trial Details

    Not applicable

  • Broad Policy Alignment

    Pending

  • Age Group

    Unspecified

  • Vulnerable Population

    Unspecified

  • Occupations of Interest

    Unspecified

Abstract

clinical trial - Current evidence suggests that chest CT imaging may be an extremely valuable tool in the diagnosis, epidemiology, and therapy response control of COVID-19 cases. It offers high sensitivity, short turnaround times and wide availability, and may thus complement RT-PCR tests, especially in situations of unclear clinical presentation, such as a negative RT-PCR despite strong anamnestic evidence for COVID-19. More importantly, it may offer opportunities to directly assess the stage of progression of the disease as observed directly from the affected lung tissue, and may thus be a method of choice for therapy response assessment in upcoming trials of new therapeutic agents. However, in order to develop it into a suitable tool for these purposes, a reproducible, standardized, and quantitative approach to image diagnostics is required. This proposal aims to develop such a standardized diagnostic and staging procedure for COVID-19 cases, using an AI-supported approach to select clinical attributes that serve as optimal predictors of the presence and stage of the disease.