Covid-19COVID 19-IANCT04418245
CT Biomarkers Identification by Artificial Intelligence for COVID-19 Prognosis
An observational study of Covid-19, run by Centre Hospitalier Universitaire de Nīmes at 6 sites in 2 countries.
Trial 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. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Covid-19
- What is being tested?
- Imaging by thoracic scanner
- How many people
- 0 participants
- Where
- 6 sitesin 2 countries
- Study dates
- 1 Mar 2020to Sep 2021
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: Patients hospitalized for Covid-19 confirmed by RT-PCR and undergoing CT scan
Who may be able to join
- 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
Who cannot join
- Patients opposing the retrospective use of their data
Trial locations and contacts
6 sites in 2 countries.
6 sites in 2 countries
France5 sites
CHU la Timone
Marseille
CHU Montpellier
Montpellier
CHU de Nimes
Nîmes
CHU Poitiers
Poitiers
CHU Strasbourg
Strasbourg
About this study
Official title: 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.
- Conditions
- Covid-19
- Being tested
- Imaging by thoracic scanner
- Keywords
- low dose CT scan, biomarkers, artificial intelligence
- Medical subject headings
- COVID-19
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Imaging by thoracic scannerDiagnostic test
Low-dose computed tomography
| Study group | Role | What they receive |
|---|---|---|
| Patients positive for SARS-CoV-2 | Not described Receives: Diagnostic Test: Imaging by thoracic scanner |
What is being measured
The primary outcome is the main question the study is designed to answer.
Vital status
Dead/alive
Measured: Day 8
Patient requiring more than 3 liters of oxygen to maintain a saturation >95% (intensive care unit or resuscitation department)
Yes/no
Measured: Day 8
Percentage of lung affected on CT
% ground glass and condensation calculated by deep learning
Measured: Day 0
Percentage of lung affected by ground glass opacity on scan
% calculated by deep learning
Measured: Day 0
Percentage of lung affected by condensation on scan
% calculated by deep learning
Measured: Day 0
Secondary outcomes (25)
Vital status
Dead/alive
Measured: Day 16
Vital status
Dead/alive
Measured: Day 30
Length of hospitalization
Days
Measured: Maximum 30 days
rehospitalization
Yes/no
Measured: Day 30
Duration of intubation
Days
Measured: Day 30
Percentage of lung affected on CT
% ground glass and condensation calculated by deep learning
Measured: Day 16
Percentage of lung affected by ground glass opacity on scan
% calculated by deep learning
Measured: Day 16
Percentage of lung affected by condensation on scan
% calculated by deep learning
Measured: Day 16
Software operating time
Speed of image loading and image processing depending of brand of scanner
Measured: End of study (August 2020)
C-reactive protein levels
mg/L
Measured: Admission Day 0
lactate dehydrogenase
U/L
Measured: Admission Day 0
lymphocytemia
g/L
Measured: Admission Day 0
D Dimers level
µg/L
Measured: Admission Day 0
Time until onset of symptoms
Days
Measured: Admission Day 0
Time between RT-PCR positive results and first scan
Hours
Measured: Admission Day 0
Age
Years
Measured: Admission Day 0
BMI> 30
Yes/no:
Measured: Admission Day 0
Medical history of cardiovascular disease
Yes/no: hypertension, coronary artery disease, congestive heart failure, cardiac arrhythmia
Measured: Admission Day 0
Diabetes
Yes/no
Measured: Admission Day 0
Medical history of respiratory disease
Yes/no: Chronic obstructive pulmonary disease, chronic respiratory failure
Measured: Admission Day 0
Medical history of immunosuppressed condition
Yes/no: steroid use, pre-existing immunological condition, current chemotherapy for cancer
Measured: Admission Day 0
Current or previous history of smoking
Yes/no:
Measured: Admission Day 0
Calculate a prognostic score from clinical, biological and CT parameters
Deep learning algorithm
Measured: Day 8
Calculate a prognostic score from clinical and biological parameters only
Deep learning algorithm
Measured: Day 8
Compare receiver operating curves of prognostic scores with and without CT parameters
Measured: Day 8
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted5 Jun 2020
- Study start1 Mar 2020 (estimated)
- Primary completionSep 2021 (estimated)
- Study completionSep 2021 (estimated)
Why it stopped: Difficulties in setting up the study
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Covid-19Studies of the same condition, open and under way.
- More trials run by Centre Hospitalier Universitaire de NīmesThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case only
- Time perspective
- Retrospective
- Enrolment
- 0 participants (actual)
Sponsor and investigators
- Lead sponsor
- Centre Hospitalier Universitaire de Nīmes
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Julien Frandon, Principal investigator, CHU Nimes
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04418245
- Sponsor's protocol ID
- NIMAO/2020/COVID 19-IA/JF-01
- Acronym
- COVID 19-IA
- Status last verified
- Mar 2025
- First submitted
- 4 Jun 2020
- Last updated
- 10 Mar 2025
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 10 Mar 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Identification of Thoracic CT Scan Biomarkers by Deep Learning for Evaluating the Prognosis of Patients With COVID-19 Disease
- ClinicalTrials.gov ID
- NCT04418245
- Acronym / sponsor ID
- COVID 19-IA / NIMAO/2020/COVID 19-IA/JF-01
- Conditions
- Covid-19
- Keywords
- low dose CT scan, biomarkers, artificial intelligence
- Study type
- Observational
- Recruitment status
- WithdrawnReason stopped: Difficulties in setting up the study
- Enrolment
- 0 participants (actual)
- Lead sponsor
- Centre Hospitalier Universitaire de NīmesUniversity, hospital or other
- Study start
- 1 Mar 2020 (estimated)
- Primary completion
- Sep 2021 (estimated)
- Study completion
- Sep 2021 (estimated)
- First posted
- 5 Jun 2020 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Case only
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Patients positive for SARS-CoV-2 | – | Diagnostic Test: Imaging by thoracic scanner |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Patients hospitalized for Covid-19 confirmed by RT-PCR and undergoing CT scan
Key inclusion criteria (3 listed)
- 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
Key exclusion criteria (1 listed)
- Patients opposing the retrospective use of their data
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Imaging by thoracic scannerLow-dose computed tomography | Diagnostic test | Patients positive for SARS-CoV-2 |
Primary outcome
Vital status
Time frame: Day 8
Dead/alive
Patient requiring more than 3 liters of oxygen to maintain a saturation >95% (intensive care unit or resuscitation department)
Time frame: Day 8
Yes/no
Percentage of lung affected on CT
Time frame: Day 0
% ground glass and condensation calculated by deep learning
Percentage of lung affected by ground glass opacity on scan
Time frame: Day 0
% calculated by deep learning
Percentage of lung affected by condensation on scan
Time frame: Day 0
% calculated by deep learning
25 secondary outcomes
Vital status
Time frame: Day 16
Vital status
Time frame: Day 30
Length of hospitalization
Time frame: Maximum 30 days
rehospitalization
Time frame: Day 30
Duration of intubation
Time frame: Day 30
Percentage of lung affected on CT
Time frame: Day 16
Percentage of lung affected by ground glass opacity on scan
Time frame: Day 16
Percentage of lung affected by condensation on scan
Time frame: Day 16
Software operating time
Time frame: End of study (August 2020)
C-reactive protein levels
Time frame: Admission Day 0
lactate dehydrogenase
Time frame: Admission Day 0
lymphocytemia
Time frame: Admission Day 0
D Dimers level
Time frame: Admission Day 0
Time until onset of symptoms
Time frame: Admission Day 0
Time between RT-PCR positive results and first scan
Time frame: Admission Day 0
Age
Time frame: Admission Day 0
BMI> 30
Time frame: Admission Day 0
Medical history of cardiovascular disease
Time frame: Admission Day 0
Diabetes
Time frame: Admission Day 0
Medical history of respiratory disease
Time frame: Admission Day 0
Medical history of immunosuppressed condition
Time frame: Admission Day 0
Current or previous history of smoking
Time frame: Admission Day 0
Calculate a prognostic score from clinical, biological and CT parameters
Time frame: Day 8
Calculate a prognostic score from clinical and biological parameters only
Time frame: Day 8
Compare receiver operating curves of prognostic scores with and without CT parameters
Time frame: Day 8
Results and safety
The study was withdrawn before enrolling participants, so there are no results.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 10 Mar 2025.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Julien Frandon principal investigator
CHU Nimes
6 sites in 2 countries: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Covid-19, run by Centre Hospitalier Universitaire de Nīmes at 6 sites in 2 countries.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: withdrawn, as of the sponsor's last update on 10 Mar 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 6 sites in France, Martinique. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study was withdrawn before enrolling participants, so there are no results.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Vital status (time frame: Day 8). The study lists 5 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 10 Mar 2025.