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CT Biomarkers Identification by Artificial Intelligence for COVID-19 Prognosis
COVID 19-IA
Identification of Thoracic CT Scan Biomarkers by Deep Learning for Evaluating the Prognosis of Patients With COVID-19 Disease
1 other identifier
observational
N/A
2 countries
6
Brief Summary
The study hypothesis is that low-dose computed tomography (LDCT) coupled with artificial intelligence by deep learning would generate imaging biomarkers linked to the patient's short- and medium-term prognosis. The purpose of this study is to rapidly make available an early decision-making tool (from the first hospital consultation of the patient with symptoms related to SARS-CoV-2) based on the integration of several biomarkers (clinical, biological, imaging by thoracic scanner) allowing both personalized medicine and better anticipation of the patient's evolution in terms of care organization.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
Started Mar 2020
6 active sites
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
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Study Timeline
Key milestones and dates
Study Start
First participant enrolled
March 1, 2020
CompletedFirst Submitted
Initial submission to the registry
June 4, 2020
CompletedFirst Posted
Study publicly available on registry
June 5, 2020
CompletedPrimary Completion
Last participant's last visit for primary outcome
September 1, 2021
CompletedStudy Completion
Last participant's last visit for all outcomes
September 1, 2021
CompletedMarch 10, 2025
March 1, 2025
1.5 years
June 4, 2020
March 7, 2025
Conditions
Keywords
Outcome Measures
Primary Outcomes (5)
Vital status
Dead/alive
Day 8
Patient requiring more than 3 liters of oxygen to maintain a saturation >95% (intensive care unit or resuscitation department)
Yes/no
Day 8
Percentage of lung affected on CT
% ground glass and condensation calculated by deep learning
Day 0
Percentage of lung affected by ground glass opacity on scan
% calculated by deep learning
Day 0
Percentage of lung affected by condensation on scan
% calculated by deep learning
Day 0
Secondary Outcomes (25)
Vital status
Day 16
Vital status
Day 30
Length of hospitalization
Maximum 30 days
rehospitalization
Day 30
Duration of intubation
Day 30
- +20 more secondary outcomes
Study Arms (1)
Patients positive for SARS-CoV-2
Interventions
Low-dose computed tomography
Eligibility Criteria
Patients hospitalized for Covid-19 confirmed by RT-PCR and undergoing CT scan
You may qualify if:
- Patients positive for SARS-CoV-2 according to RT-PCR test between 1st March and 31st May 2020
- Patients undergoing low dose CT scan to establish Covid-19 lung damage
- Available for at least 8 days follow-up
You may not qualify if:
- Patients opposing the retrospective use of their data
Contact the study team to confirm eligibility.
Sponsors & Collaborators
Study Sites (6)
CHU la Timone
Marseille, France
CHU Montpellier
Montpellier, France
CHU de Nimes
Nîmes, France
CHU Poitiers
Poitiers, France
CHU Strasbourg
Strasbourg, France
CHU Martinique
Fort-de-France, Martinique
MeSH Terms
Conditions
Condition Hierarchy (Ancestors)
Study Officials
- PRINCIPAL INVESTIGATOR
Julien Frandon
CHU Nimes
Study Design
- Study Type
- observational
- Observational Model
- CASE ONLY
- Time Perspective
- RETROSPECTIVE
- Sponsor Type
- OTHER
- Responsible Party
- SPONSOR
Study Record Dates
First Submitted
June 4, 2020
First Posted
June 5, 2020
Study Start
March 1, 2020
Primary Completion
September 1, 2021
Study Completion
September 1, 2021
Last Updated
March 10, 2025
Record last verified: 2025-03