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
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Key facts
Disease
COVID-19Start & end year
20202020Known Financial Commitments (USD)
$96,653.97Funder
Bundesministerium für Bildung und Forschung [German Federal Ministry of Education and Research] (BMBF)Principal Investigator
PendingResearch Location
GermanyLead Research Institution
University Heidelberg, Mint Medical GmbH, Deutsches Krebsforschungszentrum (DKFZ)Research Priority Alignment
N/A
Research Category
Pathogen: natural history, transmission and diagnostics
Research Subcategory
Diagnostics
Special Interest Tags
Innovation
Study Type
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.