CovidResearchTrials by Shray Alag


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Imaging by thoracic scannerWiki

Developed by Shray Alag
Clinical Trial MeSH HPO Drug Gene SNP Protein Mutation


Correlated Drug Terms (1)


Name (Synonyms) Correlation
drug348 Conestat alfa Wiki 1.00

Correlated MeSH Terms (4)


Name (Synonyms) Correlation
D003141 Communicable Diseases NIH 0.10
D007239 Infection NIH 0.07
D045169 Severe Acute Respiratory Syndrome NIH 0.06
D018352 Coronavirus Infections NIH 0.05

Correlated HPO Terms (0)


Name (Synonyms) Correlation

There is one clinical trial.

Clinical Trials


1 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.

NCT04418245 Covid-19 Diagnostic Test: Imaging by thoracic scanner

Primary Outcomes

Description: Dead/alive

Measure: Vital status

Time: Day 8

Description: Yes/no

Measure: Patient requiring more than 3 liters of oxygen to maintain a saturation >95% (intensive care unit or resuscitation department)

Time: Day 8

Description: % ground glass and condensation calculated by deep learning

Measure: Percentage of lung affected on CT

Time: Day 0

Description: % calculated by deep learning

Measure: Percentage of lung affected by ground glass opacity on scan

Time: Day 0

Description: % calculated by deep learning

Measure: Percentage of lung affected by condensation on scan

Time: Day 0

Secondary Outcomes

Description: Dead/alive

Measure: Vital status

Time: Day 16

Description: Dead/alive

Measure: Vital status

Time: Day 30

Description: Days

Measure: Length of hospitalization

Time: Maximum 30 days

Description: Yes/no

Measure: rehospitalization

Time: Day 30

Description: Days

Measure: Duration of intubation

Time: Day 30

Description: % ground glass and condensation calculated by deep learning

Measure: Percentage of lung affected on CT

Time: Day 16

Description: % calculated by deep learning

Measure: Percentage of lung affected by ground glass opacity on scan

Time: Day 16

Description: % calculated by deep learning

Measure: Percentage of lung affected by condensation on scan

Time: Day 16

Description: Speed of image loading and image processing depending of brand of scanner

Measure: Software operating time

Time: End of study (August 2020)

Description: mg/L

Measure: C-reactive protein levels

Time: Admission Day 0

Description: U/L

Measure: lactate dehydrogenase

Time: Admission Day 0

Description: g/L

Measure: lymphocytemia

Time: Admission Day 0

Description: µg/L

Measure: D Dimers level

Time: Admission Day 0

Description: Days

Measure: Time until onset of symptoms

Time: Admission Day 0

Description: Hours

Measure: Time between RT-PCR positive results and first scan

Time: Admission Day 0

Description: Years

Measure: Age

Time: Admission Day 0

Description: Yes/no:

Measure: BMI> 30

Time: Admission Day 0

Description: Yes/no: hypertension, coronary artery disease, congestive heart failure, cardiac arrhythmia

Measure: Medical history of cardiovascular disease

Time: Admission Day 0

Description: Yes/no

Measure: Diabetes

Time: Admission Day 0

Description: Yes/no: Chronic obstructive pulmonary disease, chronic respiratory failure

Measure: Medical history of respiratory disease

Time: Admission Day 0

Description: Yes/no: steroid use, pre-existing immunological condition, current chemotherapy for cancer

Measure: Medical history of immunosuppressed condition

Time: Admission Day 0

Description: Yes/no:

Measure: Current or previous history of smoking

Time: Admission Day 0

Description: Deep learning algorithm

Measure: Calculate a prognostic score from clinical, biological and CT parameters

Time: Day 8

Description: Deep learning algorithm

Measure: Calculate a prognostic score from clinical and biological parameters only

Time: Day 8

Measure: Compare receiver operating curves of prognostic scores with and without CT parameters

Time: Day 8


No related HPO nodes (Using clinical trials)