<clinical_study>
  <!-- This xml conforms to an XML Schema at:
    https://clinicaltrials.gov/ct2/html/images/info/public.xsd -->
  <required_header>
    <download_date>ClinicalTrials.gov processed this data on September 20, 2023</download_date>
    <link_text>Link to the current ClinicalTrials.gov record.</link_text>
    <url>https://clinicaltrials.gov/ct2/show/NCT04419545</url>
  </required_header>
  <id_info>
    <org_study_id>CORDA</org_study_id>
    <nct_id>NCT04419545</nct_id>
  </id_info>
  <brief_title>Covid Radiographic Images Data-set for A.I</brief_title>
  <acronym>CORDA</acronym>
  <official_title>Covid Radiographic Images Data-set for A.I</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Azienda Ospedaliera Città della Salute e della Scienza di Torino</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
    <collaborator>
      <agency>University of Turin, Italy</agency>
      <agency_class>Other</agency_class>
    </collaborator>
    <collaborator>
      <agency>Azienda Ospedaliera Ordine Mauriziano di Torino</agency>
      <agency_class>Other</agency_class>
    </collaborator>
  </sponsors>
  <source>Azienda Ospedaliera Città della Salute e della Scienza di Torino</source>
  <oversight_info>
    <has_dmc>Yes</has_dmc>
    <is_fda_regulated_drug>No</is_fda_regulated_drug>
    <is_fda_regulated_device>No</is_fda_regulated_device>
  </oversight_info>
  <brief_summary>
    <textblock>
      The possibility to use widespread and simple chest X-ray (CXR) imaging for early screening of&#xD;
      COVID-19 patients is attracting much interest from both the clinical and the Artificial&#xD;
      intelligence community. In this study we provide insights and also raise warnings on what is&#xD;
      reasonable to expect by applying deep learning to COVID classification of CXR images. We&#xD;
      provide a methodological guide and critical reading of an extensive set of statistical&#xD;
      results that can be obtained using currently available datasets. In particular, we take the&#xD;
      challenge posed by current small size COVID data and show how significant can be the bias&#xD;
      introduced by transfer-learning using larger public non- COVID CXR datasets. We also&#xD;
      contribute by providing results on a medium size COVID CXR dataset, just collected by one of&#xD;
      the major emergency hospitals in Northern Italy during the peak of the COVID pandemic. These&#xD;
      novel data allow us to contribute to validate the generalization capacity of preliminary&#xD;
      results circulating in the scientific community. Our conclusions shed some light into the&#xD;
      possibility to effectively discriminate COVID using CXR.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      COVID-19 virus has rapidly spread in mainland China and into multiple countries worldwide. As&#xD;
      of April 7th 2020 in Italy, one of the most severely affected countries, 135,586 Patients&#xD;
      with COVID19 were recorded, and 17,127 of them died; at the time of writing Piedmont is the&#xD;
      3rd most affected region in Italy, with 13,343 recorded cases. Early diagnosis is a key&#xD;
      element for proper treatment of the patients and prevention of the spread of the disease.&#xD;
      Given the high tropism of COVID-19 for respiratory airways and lung epithelium,&#xD;
      identification of lung involvement in infected patients can be relevant for treatment and&#xD;
      monitoring of the disease. Virus testing is currently considered the only specific method of&#xD;
      diagnosis. The Center for Disease Control (CDC) in the US recommends collecting and testing&#xD;
      specimens from the upper respiratory tract (nasopharyngeal and oropharyngeal swabs) or from&#xD;
      the lower respiratory tract when available (bronchoalveolar lavage, BAL) for viral testing&#xD;
      with reverse transcription polymerase chain reaction (RT-PCR) assay. Current position papers&#xD;
      from radiological societies (Fleischner Society, SIRM, RSNA) do not recommend routine use of&#xD;
      imaging for COVID-19 diagnosis.&#xD;
&#xD;
      However, it has been widely demonstrated that, even at early stages of the disease, chest&#xD;
      x-rays (CXR) and computed tomography (CT) scans can show pathological findings. It should be&#xD;
      noted that they are actually non specific, and overlap with other viral infections (such as&#xD;
      influenza, H1N1, SARS and MERS): most authors report peripheral bilateral ill-defined and&#xD;
      ground-glass opacities, mainly involving the lower lobes, progressively increasing in&#xD;
      extension as disease becomes more severe and leading to diffuse parenchymal consolidation, CT&#xD;
      is a sensitive tool for early detection of peripheral ground glass opacities; however routine&#xD;
      role of CT imaging in these Patients is logistically challenging in terms of safety for&#xD;
      health professionals and other patients, and can overwhelm available resources. Chest X-ray&#xD;
      can be a useful tool, especially in emergency settings: it can help exclude other possible&#xD;
      lung &quot;noxa&quot;, allow a first rough valuation of the extent of lung involvement and most&#xD;
      importantly can be obtained at patients bed using portable devices, limiting possible&#xD;
      exposure in health care workers and other patients. Furthermore, CXR can be repeated over&#xD;
      time to monitor the evolution of lung disease.&#xD;
&#xD;
      Methodology:&#xD;
&#xD;
      we describe the deeplearning approach based on quite standard pipeline, namely chest image&#xD;
      pre-processing and lung segmentation followed by classification model obtained with transfer&#xD;
      learning. As we will see in this section, data pre-processing is fundamental to remove any&#xD;
      bias present in the data. In particular, we will show that it is easy for a deep model to&#xD;
      recognize these biases which drive the learning process. Given the small size of COVID&#xD;
      datasets, a key role is played by the larger datasets used for pre-training. Therefore, we&#xD;
      first discuss which datasets can be used for our goals.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Unknown status</overall_status>
  <last_known_status>Recruiting</last_known_status>
  <start_date type="Actual">March 24, 2020</start_date>
  <completion_date type="Anticipated">March 31, 2021</completion_date>
  <primary_completion_date type="Anticipated">December 31, 2020</primary_completion_date>
  <study_type>Observational</study_type>
  <has_expanded_access>No</has_expanded_access>
  <study_design_info>
    <observational_model>Case-Control</observational_model>
    <time_perspective>Retrospective</time_perspective>
  </study_design_info>
  <primary_outcome>
    <measure>sensibility and specificity of neural network diagnosis</measure>
    <time_frame>at day 0</time_frame>
    <description>sensibility and specificity of neural network diagnosis compared with human diagnosis</description>
  </primary_outcome>
  <number_of_groups>2</number_of_groups>
  <enrollment type="Anticipated">2500</enrollment>
  <condition>Radiology</condition>
  <condition>Artificial Intelligence</condition>
  <arm_group>
    <arm_group_label>interstitial pneumonia cases</arm_group_label>
    <description>Chest x-ray diagnosis</description>
  </arm_group>
  <arm_group>
    <arm_group_label>Negative controls</arm_group_label>
    <description>Chest x-ray Negative for pneumonia</description>
  </arm_group>
  <intervention>
    <intervention_type>Diagnostic Test</intervention_type>
    <intervention_name>Neural network diagnosis algorithm</intervention_name>
    <description>we feed neural network with chest x-ray radiography images for teaching the network for automatic diagnosis of interstitial pneumonia</description>
    <arm_group_label>Negative controls</arm_group_label>
    <arm_group_label>interstitial pneumonia cases</arm_group_label>
  </intervention>
  <eligibility>
    <study_pop>
      <textblock>
        All the people treated in radiology dept&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Non-Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria: chest x ray performed during emergency department or hospital stay&#xD;
&#xD;
        -&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  None&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>N/A</minimum_age>
    <maximum_age>N/A</maximum_age>
    <healthy_volunteers>No</healthy_volunteers>
  </eligibility>
  <overall_official>
    <last_name>Giorgio Limerutti, M.D.</last_name>
    <role>Principal Investigator</role>
    <affiliation>Radiology Unit A.O.U. Città della Salute e della Scienza</affiliation>
  </overall_official>
  <overall_contact>
    <last_name>Marco Grosso, M.Sc.</last_name>
    <phone>00390116331330</phone>
    <email>mgrosso2@gmail.com</email>
  </overall_contact>
  <location>
    <facility>
      <name>Azienda Ospedaliero Universitaria Città della Salute e della Scienza</name>
      <address>
        <city>Torino</city>
        <state>Turin</state>
        <zip>10126</zip>
        <country>Italy</country>
      </address>
    </facility>
    <status>Recruiting</status>
    <contact>
      <last_name>Marco Grosso, M.Sc.</last_name>
      <phone>00390116331330</phone>
      <email>mgrosso2@gmail.com</email>
    </contact>
    <investigator>
      <last_name>Giorgio Limerutti, M.D.</last_name>
      <role>Principal Investigator</role>
    </investigator>
    <investigator>
      <last_name>Paolo Fonio, M.D.</last_name>
      <role>Sub-Investigator</role>
    </investigator>
    <investigator>
      <last_name>Marco Grosso, M.Sc.</last_name>
      <role>Sub-Investigator</role>
    </investigator>
    <investigator>
      <last_name>Stefano Tibaldi, M.Sc.</last_name>
      <role>Sub-Investigator</role>
    </investigator>
    <investigator>
      <last_name>Simona Capello, M.D.</last_name>
      <role>Sub-Investigator</role>
    </investigator>
    <investigator>
      <last_name>Patrizia Sardo, M.D.</last_name>
      <role>Sub-Investigator</role>
    </investigator>
    <investigator>
      <last_name>Claudio Berzovini, M.D.</last_name>
      <role>Sub-Investigator</role>
    </investigator>
    <investigator>
      <last_name>Francesca Santinelli, M.Sc.</last_name>
      <role>Sub-Investigator</role>
    </investigator>
    <investigator>
      <last_name>Marco Calandri, M.D.</last_name>
      <role>Sub-Investigator</role>
    </investigator>
    <investigator>
      <last_name>Andrea Ferraris, M.D.</last_name>
      <role>Sub-Investigator</role>
    </investigator>
  </location>
  <location_countries>
    <country>Italy</country>
  </location_countries>
  <verification_date>June 2020</verification_date>
  <study_first_submitted>June 4, 2020</study_first_submitted>
  <study_first_submitted_qc>June 4, 2020</study_first_submitted_qc>
  <study_first_posted type="Actual">June 5, 2020</study_first_posted>
  <last_update_submitted>June 4, 2020</last_update_submitted>
  <last_update_submitted_qc>June 4, 2020</last_update_submitted_qc>
  <last_update_posted type="Actual">June 5, 2020</last_update_posted>
  <responsible_party>
    <responsible_party_type>Principal Investigator</responsible_party_type>
    <investigator_affiliation>Azienda Ospedaliera Città della Salute e della Scienza di Torino</investigator_affiliation>
    <investigator_full_name>Giorgio Limerutti</investigator_full_name>
    <investigator_title>M.D.</investigator_title>
  </responsible_party>
  <patient_data>
    <sharing_ipd>No</sharing_ipd>
  </patient_data>
  <!-- Results have not yet been posted for this study                                          -->
</clinical_study>


download date: ClinicalTrials.gov processed this data on September 20, 2023
link text: Link to the current ClinicalTrials.gov record.
url: https://clinicaltrials.gov/ct2/show/NCT04419545
org study id: CORDA
nct id: NCT04419545
lead sponsor: 
collaborator: 
collaborator: 
has dmc: Yes
is fda regulated drug: No
is fda regulated device: No
The possibility to use widespread and simple chest X-ray (CXR) imaging for early screening of
      COVID-19 patients is attracting much interest from both the clinical and the Artificial
      intelligence community. In this study we provide insights and also raise warnings on what is
      reasonable to expect by applying deep learning to COVID classification of CXR images. We
      provide a methodological guide and critical reading of an extensive set of statistical
      results that can be obtained using currently available datasets. In particular, we take the
      challenge posed by current small size COVID data and show how significant can be the bias
      introduced by transfer-learning using larger public non- COVID CXR datasets. We also
      contribute by providing results on a medium size COVID CXR dataset, just collected by one of
      the major emergency hospitals in Northern Italy during the peak of the COVID pandemic. These
      novel data allow us to contribute to validate the generalization capacity of preliminary
      results circulating in the scientific community. Our conclusions shed some light into the
      possibility to effectively discriminate COVID using CXR.
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.
observational model: Case-Control
time perspective: Retrospective
measure: sensibility and specificity of neural network diagnosis
time frame: at day 0
description: sensibility and specificity of neural network diagnosis compared with human diagnosis
arm group label: interstitial pneumonia cases
description: Chest x-ray diagnosis
arm group label: Negative controls
description: Chest x-ray Negative for pneumonia
intervention type: Diagnostic Test
intervention name: Neural network diagnosis algorithm
description: we feed neural network with chest x-ray radiography images for teaching the network for automatic diagnosis of interstitial pneumonia
arm group label: Negative controls
arm group label: interstitial pneumonia cases
study pop: 
sampling method: Non-Probability Sample
criteria: 
gender: All
minimum age: N/A
maximum age: N/A
healthy volunteers: No
last name: Giorgio Limerutti, M.D.
role: Principal Investigator
affiliation: Radiology Unit A.O.U. Città della Salute e della Scienza
last name: Marco Grosso, M.Sc.
phone: 00390116331330
email: mgrosso2@gmail.com
facility: 
status: Recruiting
contact: 
investigator: 
investigator: 
investigator: 
investigator: 
investigator: 
investigator: 
investigator: 
investigator: 
investigator: 
investigator: 
country: Italy
responsible party type: Principal Investigator
investigator affiliation: Azienda Ospedaliera Città della Salute e della Scienza di Torino
investigator full name: Giorgio Limerutti
investigator title: M.D.
sharing ipd: No