CT Biomarkers Identification by Artificial Intelligence for COVID-19 Prognosis

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

WITHDRAWN

Study Classification

OBSERVATIONAL

Study Start Date

2020-03-01

Study Completion Date

2021-09-30

Brief Summary

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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.

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Detailed Description

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Conditions

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Covid-19

Study Design

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Observational Model Type

CASE_ONLY

Study Time Perspective

RETROSPECTIVE

Study Groups

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Patients positive for SARS-CoV-2

Imaging by thoracic scanner

Intervention Type DIAGNOSTIC_TEST

Low-dose computed tomography

Interventions

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Imaging by thoracic scanner

Low-dose computed tomography

Intervention Type DIAGNOSTIC_TEST

Eligibility Criteria

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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