xml: <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/NCT06032819</url>
    </required_header>
    <id_info>
      <org_study_id>SYSKY-2023-865-01</org_study_id>
      <nct_id>NCT06032819</nct_id>
    </id_info>
    <brief_title>Differentiating Between Brain Hemorrhage and Contrast</brief_title>
    <official_title>Artificial Intelligence for Differentiating Between Brain Hemorrhage and Contrast Extravasation After Mechanical Revascularization in Acute Ischemic Stroke</official_title>
    <sponsors>
      <lead_sponsor>
        <agency>Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University</agency>
        <agency_class>Other</agency_class>
      </lead_sponsor>
      <collaborator>
        <agency>Hong Kong University of Science and Technology</agency>
        <agency_class>Other</agency_class>
      </collaborator>
      <collaborator>
        <agency>First Affiliated Hospital, Sun Yat-Sen University</agency>
        <agency_class>Other</agency_class>
      </collaborator>
      <collaborator>
        <agency>The Seventh Affiliated Hospital of Sun Yat-sen University</agency>
        <agency_class>Other</agency_class>
      </collaborator>
      <collaborator>
        <agency>Peking University Shenzhen Hospital</agency>
        <agency_class>Other</agency_class>
      </collaborator>
      <collaborator>
        <agency>The Third Affiliated Hospital of Guangzhou Medical University</agency>
        <agency_class>Other</agency_class>
      </collaborator>
      <collaborator>
        <agency>The Third Affiliated Hospital of Southern Medical University</agency>
        <agency_class>Other</agency_class>
      </collaborator>
      <collaborator>
        <agency>Shenshan Medical Center, Memorial Hospital of Sun Yat-sen University</agency>
        <agency_class>Other</agency_class>
      </collaborator>
      <collaborator>
        <agency>Shantou Central Hospital</agency>
        <agency_class>Other</agency_class>
      </collaborator>
      <collaborator>
        <agency>Dongguan People's Hospital</agency>
        <agency_class>Other</agency_class>
      </collaborator>
      <collaborator>
        <agency>The First People's Hospital of Qinzhou</agency>
        <agency_class>Other</agency_class>
      </collaborator>
      <collaborator>
        <agency>Guangdong 999 Brain Hospital</agency>
        <agency_class>Other</agency_class>
      </collaborator>
    </sponsors>
    <source>Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University</source>
    <oversight_info>
      <has_dmc>No</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 goal of this observational study is to use artificial intelligence to differentiate&#xD;
        cerebral hemorrhage from contrast agent extravasation after mechanical revascularization in&#xD;
        ischemic stroke.&#xD;
  &#xD;
        The main question it aims to answer is: Whether artificial intelligence can help&#xD;
        differentiate brain hemorrhage from contrast agent extravasation.&#xD;
  &#xD;
        Patients with intracranial high-density lesions on CT scans within 24h after mechanical&#xD;
        revascularization will be included. Expected to enroll 500 patients. The type of high-density&#xD;
        lesion is determined according to dual-energy CT images or follow-up images. Patients will be&#xD;
        divided into training group, validation and testing groups by stratified random sampling&#xD;
        (6:2:2). After the images and the image labels are obtained, deep learning artificial&#xD;
        intelligence will be used to learn the image characteristics and establish a diagnostic&#xD;
        model, and the model performance and generalization ability will be evaluated.&#xD;
      </textblock>
    </brief_summary>
    <overall_status>Not yet recruiting</overall_status>
    <start_date type="Anticipated">September 2023</start_date>
    <completion_date type="Anticipated">December 2024</completion_date>
    <primary_completion_date type="Anticipated">July 2024</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>Develop a deep learning model to differentiate brain hemorrhage from contrast agent extravasation, and evaluate the model performance and generalization ability</measure>
      <time_frame>2024-12</time_frame>
      <description>The accuracy, sensitivity, specificity, precision, and recall of the model will be calculated, and confusion matrix will be display.</description>
    </primary_outcome>
    <number_of_groups>2</number_of_groups>
    <enrollment type="Anticipated">500</enrollment>
    <condition>Ischemic Stroke</condition>
    <arm_group>
      <arm_group_label>hemorrhage</arm_group_label>
      <description>The result of the Intracranial hyper-density on CT images is determined by dual-energy CT or follow-up images: hyper-density can be seen on the virtual non-contrasted image of dual-energy CT, or high density persist longer than 48 hours.</description>
    </arm_group>
    <arm_group>
      <arm_group_label>simple contrast extravasation</arm_group_label>
      <description>There is no Intracranial hyper-density on the virtual non-contrast images of dual-energy CT, or the follow-up CT show that the hyper-density is absorbed within 48 hours.</description>
    </arm_group>
    <eligibility>
      <study_pop>
        <textblock>
          From December 2017 to June 2023, patients with acute ischemic stroke or intracranial&#xD;
          arterial stenosis who underwent mechanical vascularization at 10 centers will be&#xD;
          retrospectively enrolled.&#xD;
        </textblock>
      </study_pop>
      <sampling_method>Non-Probability Sample</sampling_method>
      <criteria>
        <textblock>
          Inclusion Criteria:&#xD;
  &#xD;
          (1) patients underwent non-enhanced head CT after mechanical vascularization; (2) initial&#xD;
          post-operative non-enhanced head CT was performed within 24 h after mechanical&#xD;
          vascularization; and (3) intracranial hyper-intensity, which was defined as an objectively&#xD;
          higher density than the surrounding grey or white matter in the parenchyma or higher&#xD;
          density than cerebrospinal fluid in ventricles and cisterns, could be seen on the initial&#xD;
          non-enhanced head CT after mechanical vascularization.&#xD;
  &#xD;
          Exclusion Criteria:&#xD;
  &#xD;
          (1) the follow-up time of non-enhanced head CT after mechanical vascularization was less&#xD;
          than 24 h; (2) artifacts (e.g. metal artifacts or motion artifacts) affected the&#xD;
          hyper-intensity in CT images; and (3) patients underwent craniotomy after mechanical&#xD;
          vascularization, which made it difficult to identify the area of hyper-intensity.&#xD;
        </textblock>
      </criteria>
      <gender>All</gender>
      <minimum_age>18 Years</minimum_age>
      <maximum_age>100 Years</maximum_age>
      <healthy_volunteers>No</healthy_volunteers>
    </eligibility>
    <overall_contact>
      <last_name>Meiwei Chen</last_name>
      <phone>+86 18898534109</phone>
      <email>chenmw7@mail.sysu.edu.cn</email>
    </overall_contact>
    <verification_date>September 2023</verification_date>
    <study_first_submitted>September 4, 2023</study_first_submitted>
    <study_first_submitted_qc>September 4, 2023</study_first_submitted_qc>
    <study_first_posted type="Actual">September 13, 2023</study_first_posted>
    <last_update_submitted>September 4, 2023</last_update_submitted>
    <last_update_submitted_qc>September 4, 2023</last_update_submitted_qc>
    <last_update_posted type="Actual">September 13, 2023</last_update_posted>
    <responsible_party>
      <responsible_party_type>Sponsor</responsible_party_type>
    </responsible_party>
    <keyword>Hemorrhage</keyword>
    <keyword>Contrast Media Extravasation</keyword>
    <keyword>Artificial Intelligence</keyword>
    <keyword>Mechanical Revascularisation</keyword>
    <condition_browse>
      <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
      <mesh_term>Ischemic Stroke</mesh_term>
      <mesh_term>Intracranial Hemorrhages</mesh_term>
      <mesh_term>Hemorrhage</mesh_term>
    </condition_browse>
    <patient_data>
      <sharing_ipd>Undecided</sharing_ipd>
    </patient_data>
    <!-- Results have not yet been posted for this study                                          -->
  </clinical_study>
  
  
text: 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/NCT06032819
  org study id: SYSKY-2023-865-01
  nct id: NCT06032819
  lead sponsor: 
  collaborator: 
  collaborator: 
  collaborator: 
  collaborator: 
  collaborator: 
  collaborator: 
  collaborator: 
  collaborator: 
  collaborator: 
  collaborator: 
  collaborator: 
  has dmc: No
  is fda regulated drug: No
  is fda regulated device: No
  The goal of this observational study is to use artificial intelligence to differentiate
        cerebral hemorrhage from contrast agent extravasation after mechanical revascularization in
        ischemic stroke.
  
        The main question it aims to answer is: Whether artificial intelligence can help
        differentiate brain hemorrhage from contrast agent extravasation.
  
        Patients with intracranial high-density lesions on CT scans within 24h after mechanical
        revascularization will be included. Expected to enroll 500 patients. The type of high-density
        lesion is determined according to dual-energy CT images or follow-up images. Patients will be
        divided into training group, validation and testing groups by stratified random sampling
        (6:2:2). After the images and the image labels are obtained, deep learning artificial
        intelligence will be used to learn the image characteristics and establish a diagnostic
        model, and the model performance and generalization ability will be evaluated.
  observational model: Case-Control
  time perspective: Retrospective
  measure: Develop a deep learning model to differentiate brain hemorrhage from contrast agent extravasation, and evaluate the model performance and generalization ability
  time frame: 2024-12
  description: The accuracy, sensitivity, specificity, precision, and recall of the model will be calculated, and confusion matrix will be display.
  arm group label: hemorrhage
  description: The result of the Intracranial hyper-density on CT images is determined by dual-energy CT or follow-up images: hyper-density can be seen on the virtual non-contrasted image of dual-energy CT, or high density persist longer than 48 hours.
  arm group label: simple contrast extravasation
  description: There is no Intracranial hyper-density on the virtual non-contrast images of dual-energy CT, or the follow-up CT show that the hyper-density is absorbed within 48 hours.
  study pop: 
  sampling method: Non-Probability Sample
  criteria: 
  gender: All
  minimum age: 18 Years
  maximum age: 100 Years
  healthy volunteers: No
  last name: Meiwei Chen
  phone: +86 18898534109
  email: chenmw7@mail.sysu.edu.cn
  responsible party type: Sponsor
  mesh term: Ischemic Stroke
  mesh term: Intracranial Hemorrhages
  mesh term: Hemorrhage
  sharing ipd: Undecided