CT Biomarkers Identification by Artificial Intelligence for COVID-19 Prognosis (COVID 19-IA)

Centre Hospitalier Universitaire de Nīmes

Status: Withdrawn

Conditions: Covid-19

Treatments: Diagnostic Test: Imaging by thoracic scanner

Study type: Observational

Funder types: Other

Identifiers

NCT04418245

NIMAO/2020/COVID 19-IA/JF-01

Details and patient eligibility

About

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.

Sex: All

Volunteers: No Healthy Volunteers

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

Trial design

0 participants in 1 patient group

Patients positive for SARS-CoV-2

Treatment:

Diagnostic Test: Imaging by thoracic scanner