Official Title
Identification of Thoracic CT Scan Biomarkers by Deep Learning for Evaluating the Prognosis of Patients With COVID-19 Disease
Brief Summary

The study hypothesis is that low-dose computed tomography (LDCT) coupled with artificialintelligence by deep learning would generate imaging biomarkers linked to the patient'sshort- 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 thoracicscanner) allowing both personalized medicine and better anticipation of the patient'sevolution in terms of care organization.

Unknown status
COVID-19

Diagnostic Test: Imaging by thoracic scanner

Low-dose computed tomography

Eligibility Criteria

Inclusion Criteria:

- 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

Exclusion Criteria:

• Patients opposing the retrospective use of their data

Eligibility Gender
All
Eligibility Age
Minimum: N/A ~ Maximum: N/A
Countries
France
Martinique
Locations

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

Julien Frandon, Principal Investigator
CHU Nimes

Centre Hospitalier Universitaire de Nīmes
NCT Number
Keywords
low dose CT scan
Biomarkers
Artificial Intelligence
MeSH Terms
COVID-19