- ICH GCP
- US Clinical Trials Registry
- Clinical Trial NCT04419545
Covid Radiographic Images Data-set for A.I (CORDA)
Study Overview
Status
Conditions
Intervention / Treatment
Detailed Description
COVID-19 virus has rapidly spread in mainland China and into multiple countries worldwide. As of April 7th 2020 in Italy, one of the most severely affected countries, 135,586 Patients with COVID19 were recorded, and 17,127 of them died; at the time of writing Piedmont is the 3rd most affected region in Italy, with 13,343 recorded cases. Early diagnosis is a key element for proper treatment of the patients and prevention of the spread of the disease. Given the high tropism of COVID-19 for respiratory airways and lung epithelium, identification of lung involvement in infected patients can be relevant for treatment and monitoring of the disease. Virus testing is currently considered the only specific method of diagnosis. The Center for Disease Control (CDC) in the US recommends collecting and testing specimens from the upper respiratory tract (nasopharyngeal and oropharyngeal swabs) or from the lower respiratory tract when available (bronchoalveolar lavage, BAL) for viral testing with reverse transcription polymerase chain reaction (RT-PCR) assay. Current position papers from radiological societies (Fleischner Society, SIRM, RSNA) do not recommend routine use of imaging for COVID-19 diagnosis.
However, it has been widely demonstrated that, even at early stages of the disease, chest x-rays (CXR) and computed tomography (CT) scans can show pathological findings. It should be noted that they are actually non specific, and overlap with other viral infections (such as influenza, H1N1, SARS and MERS): most authors report peripheral bilateral ill-defined and ground-glass opacities, mainly involving the lower lobes, progressively increasing in extension as disease becomes more severe and leading to diffuse parenchymal consolidation, CT is a sensitive tool for early detection of peripheral ground glass opacities; however routine role of CT imaging in these Patients is logistically challenging in terms of safety for health professionals and other patients, and can overwhelm available resources. Chest X-ray can be a useful tool, especially in emergency settings: it can help exclude other possible lung "noxa", allow a first rough valuation of the extent of lung involvement and most importantly can be obtained at patients bed using portable devices, limiting possible exposure in health care workers and other patients. Furthermore, CXR can be repeated over time to monitor the evolution of lung disease.
Methodology:
we describe the deeplearning approach based on quite standard pipeline, namely chest image pre-processing and lung segmentation followed by classification model obtained with transfer learning. As we will see in this section, data pre-processing is fundamental to remove any bias present in the data. In particular, we will show that it is easy for a deep model to recognize these biases which drive the learning process. Given the small size of COVID datasets, a key role is played by the larger datasets used for pre-training. Therefore, we first discuss which datasets can be used for our goals.
Study Type
Enrollment (Anticipated)
Contacts and Locations
Study Contact
- Name: Marco Grosso, M.Sc.
- Phone Number: 00390116331330
- Email: mgrosso2@gmail.com
Study Locations
-
-
Turin
-
Torino, Turin, Italy, 10126
- Recruiting
- Azienda Ospedaliero Universitaria Citta Della Salute E Della Scienza
-
Contact:
- Marco Grosso, M.Sc.
- Phone Number: 00390116331330
- Email: mgrosso2@gmail.com
-
Principal Investigator:
- Giorgio Limerutti, M.D.
-
Sub-Investigator:
- Paolo Fonio, M.D.
-
Sub-Investigator:
- Marco Grosso, M.Sc.
-
Sub-Investigator:
- Stefano Tibaldi, M.Sc.
-
Sub-Investigator:
- Simona Capello, M.D.
-
Sub-Investigator:
- Patrizia Sardo, M.D.
-
Sub-Investigator:
- Claudio Berzovini, M.D.
-
Sub-Investigator:
- Francesca Santinelli, M.Sc.
-
Sub-Investigator:
- Marco Calandri, M.D.
-
Sub-Investigator:
- Andrea Ferraris, M.D.
-
-
Participation Criteria
Eligibility Criteria
Ages Eligible for Study
- ADULT
- OLDER_ADULT
- CHILD
Accepts Healthy Volunteers
Genders Eligible for Study
Sampling Method
Study Population
Description
Inclusion Criteria: chest x ray performed during emergency department or hospital stay
-
Exclusion Criteria:
- None
Study Plan
How is the study designed?
Design Details
Cohorts and Interventions
Group / Cohort |
Intervention / Treatment |
---|---|
interstitial pneumonia cases
Chest x-ray diagnosis
|
we feed neural network with chest x-ray radiography images for teaching the network for automatic diagnosis of interstitial pneumonia
|
Negative controls
Chest x-ray Negative for pneumonia
|
we feed neural network with chest x-ray radiography images for teaching the network for automatic diagnosis of interstitial pneumonia
|
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
---|---|---|
sensibility and specificity of neural network diagnosis
Time Frame: at day 0
|
sensibility and specificity of neural network diagnosis compared with human diagnosis
|
at day 0
|
Collaborators and Investigators
Investigators
- Principal Investigator: Giorgio Limerutti, M.D., Radiology Unit A.O.U. Città della Salute e della Scienza
Study record dates
Study Major Dates
Study Start (ACTUAL)
Primary Completion (ANTICIPATED)
Study Completion (ANTICIPATED)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (ACTUAL)
Study Record Updates
Last Update Posted (ACTUAL)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Other Study ID Numbers
- CORDA
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.
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