CT Biomarkers Identification by Artificial Intelligence for COVID-19 Prognosis

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

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.

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