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/NCT05963724</url>
    </required_header>
    <id_info>
      <org_study_id>1922564-2</org_study_id>
      <nct_id>NCT05963724</nct_id>
    </id_info>
    <brief_title>Real-Time CAD for Colonic Neoplasia: A RCT</brief_title>
    <acronym>CAD</acronym>
    <official_title>Efficacy of Real-Time Computer Aided-Detected of Colonic Neoplasia in an Underserved Population, A Randomized Controlled Trial</official_title>
    <sponsors>
      <lead_sponsor>
        <agency>Riverside University Health System Medical Center</agency>
        <agency_class>Other</agency_class>
      </lead_sponsor>
    </sponsors>
    <source>Riverside University Health System Medical Center</source>
    <oversight_info>
      <has_dmc>No</has_dmc>
      <is_fda_regulated_drug>No</is_fda_regulated_drug>
      <is_fda_regulated_device>Yes</is_fda_regulated_device>
      <is_us_export>No</is_us_export>
    </oversight_info>
    <brief_summary>
      <textblock>
        This study assesses the sensitivity and added benefits of computer-aided detection compared&#xD;
        to standard care (white-light) in detecting colon polyps in patients undergoing colonoscopy.&#xD;
      </textblock>
    </brief_summary>
    <detailed_description>
      <textblock>
        Failure in polyp detection leads to colon cancer after colonoscopy. Artificial intelligence&#xD;
        systems allow real-time computer-aided detection of polyps with high-accuracy. This study&#xD;
        will compare GI-Genius, a real-time CAD system to standard colonoscopy in terms of how many&#xD;
        colonoscopies detect an adenoma.&#xD;
      </textblock>
    </detailed_description>
    <overall_status>Completed</overall_status>
    <start_date type="Actual">September 1, 2022</start_date>
    <completion_date type="Actual">May 27, 2023</completion_date>
    <primary_completion_date type="Actual">March 31, 2023</primary_completion_date>
    <phase>N/A</phase>
    <study_type>Interventional</study_type>
    <has_expanded_access>No</has_expanded_access>
    <study_design_info>
      <allocation>Randomized</allocation>
      <intervention_model>Parallel Assignment</intervention_model>
      <primary_purpose>Screening</primary_purpose>
      <masking>Double (Participant, Outcomes Assessor)</masking>
    </study_design_info>
    <primary_outcome>
      <measure>Adenoma Detection Rate</measure>
      <time_frame>1 year</time_frame>
    </primary_outcome>
    <secondary_outcome>
      <measure>Adenomas Per Colon</measure>
      <time_frame>1 year</time_frame>
    </secondary_outcome>
    <secondary_outcome>
      <measure>Sessile Serrated Lesion Detection Rate</measure>
      <time_frame>1 year</time_frame>
    </secondary_outcome>
    <secondary_outcome>
      <measure>Sessile Serrated Lesions Per Colon</measure>
      <time_frame>1 year</time_frame>
    </secondary_outcome>
    <secondary_outcome>
      <measure>False Neoplasia Rate</measure>
      <time_frame>1 year</time_frame>
    </secondary_outcome>
    <secondary_outcome>
      <measure>Withdrawal Time</measure>
      <time_frame>1 year</time_frame>
    </secondary_outcome>
    <number_of_arms>2</number_of_arms>
    <enrollment type="Actual">1100</enrollment>
    <condition>Colorectal Cancer</condition>
    <condition>Colorectal Neoplasms</condition>
    <arm_group>
      <arm_group_label>Real-Time Computer Aided Detection</arm_group_label>
      <arm_group_type>Experimental</arm_group_type>
    </arm_group>
    <arm_group>
      <arm_group_label>Standard Colonoscopy</arm_group_label>
      <arm_group_type>Active Comparator</arm_group_type>
    </arm_group>
    <intervention>
      <intervention_type>Device</intervention_type>
      <intervention_name>Real-Time Computer Aided Detection</intervention_name>
      <description>This intervention involves using computer-aided detection using a real-time system (GI-Genius, Medtronic)</description>
      <arm_group_label>Real-Time Computer Aided Detection</arm_group_label>
    </intervention>
    <intervention>
      <intervention_type>Procedure</intervention_type>
      <intervention_name>Stanford Colonoscopy</intervention_name>
      <description>This involves white-light colonoscopy</description>
      <arm_group_label>Standard Colonoscopy</arm_group_label>
    </intervention>
    <eligibility>
      <criteria>
        <textblock>
          Inclusion Criteria:&#xD;
  &#xD;
            -  Undergoing colonoscopy at RUHS&#xD;
  &#xD;
            -  Age &gt; 45 years&#xD;
  &#xD;
            -  No contraindications to colonoscopy&#xD;
  &#xD;
          Exclusion Criteria:&#xD;
  &#xD;
            -  Prior history of subtotal colectomy&#xD;
        </textblock>
      </criteria>
      <gender>All</gender>
      <minimum_age>30 Years</minimum_age>
      <maximum_age>100 Years</maximum_age>
      <healthy_volunteers>No</healthy_volunteers>
    </eligibility>
    <location>
      <facility>
        <name>Riverside University Health System</name>
        <address>
          <city>Moreno Valley</city>
          <state>California</state>
          <zip>92555</zip>
          <country>United States</country>
        </address>
      </facility>
    </location>
    <location_countries>
      <country>United States</country>
    </location_countries>
    <reference>
      <citation>Thiruvengadam NR, Cote GA, Gupta S, Rodrigues M, Schneider Y, Arain MA, Solaimani P, Serrao S, Kochman ML, Saumoy M. An Evaluation of Critical Factors for the Cost-Effectiveness of Real-Time Computer-Aided Detection: Sensitivity and Threshold Analyses Using a Microsimulation Model. Gastroenterology. 2023 May;164(6):906-920. doi: 10.1053/j.gastro.2023.01.027. Epub 2023 Feb 2.</citation>
      <PMID>36736437</PMID>
    </reference>
    <verification_date>July 2023</verification_date>
    <study_first_submitted>July 19, 2023</study_first_submitted>
    <study_first_submitted_qc>July 19, 2023</study_first_submitted_qc>
    <study_first_posted type="Actual">July 27, 2023</study_first_posted>
    <last_update_submitted>July 19, 2023</last_update_submitted>
    <last_update_submitted_qc>July 19, 2023</last_update_submitted_qc>
    <last_update_posted type="Actual">July 27, 2023</last_update_posted>
    <responsible_party>
      <responsible_party_type>Sponsor</responsible_party_type>
    </responsible_party>
    <keyword>artificial intelligence</keyword>
    <condition_browse>
      <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
      <mesh_term>Colorectal Neoplasms</mesh_term>
    </condition_browse>
    <patient_data>
      <sharing_ipd>No</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/NCT05963724
  org study id: 1922564-2
  nct id: NCT05963724
  lead sponsor: 
  has dmc: No
  is fda regulated drug: No
  is fda regulated device: Yes
  is us export: No
  This study assesses the sensitivity and added benefits of computer-aided detection compared
        to standard care (white-light) in detecting colon polyps in patients undergoing colonoscopy.
  Failure in polyp detection leads to colon cancer after colonoscopy. Artificial intelligence
        systems allow real-time computer-aided detection of polyps with high-accuracy. This study
        will compare GI-Genius, a real-time CAD system to standard colonoscopy in terms of how many
        colonoscopies detect an adenoma.
  allocation: Randomized
  intervention model: Parallel Assignment
  primary purpose: Screening
  masking: Double (Participant, Outcomes Assessor)
  measure: Adenoma Detection Rate
  time frame: 1 year
  measure: Adenomas Per Colon
  time frame: 1 year
  measure: Sessile Serrated Lesion Detection Rate
  time frame: 1 year
  measure: Sessile Serrated Lesions Per Colon
  time frame: 1 year
  measure: False Neoplasia Rate
  time frame: 1 year
  measure: Withdrawal Time
  time frame: 1 year
  arm group label: Real-Time Computer Aided Detection
  arm group type: Experimental
  arm group label: Standard Colonoscopy
  arm group type: Active Comparator
  intervention type: Device
  intervention name: Real-Time Computer Aided Detection
  description: This intervention involves using computer-aided detection using a real-time system (GI-Genius, Medtronic)
  arm group label: Real-Time Computer Aided Detection
  intervention type: Procedure
  intervention name: Stanford Colonoscopy
  description: This involves white-light colonoscopy
  arm group label: Standard Colonoscopy
  criteria: 
  gender: All
  minimum age: 30 Years
  maximum age: 100 Years
  healthy volunteers: No
  facility: 
  country: United States
  citation: Thiruvengadam NR, Cote GA, Gupta S, Rodrigues M, Schneider Y, Arain MA, Solaimani P, Serrao S, Kochman ML, Saumoy M. An Evaluation of Critical Factors for the Cost-Effectiveness of Real-Time Computer-Aided Detection: Sensitivity and Threshold Analyses Using a Microsimulation Model. Gastroenterology. 2023 May;164(6):906-920. doi: 10.1053/j.gastro.2023.01.027. Epub 2023 Feb 2.
  PMID: 36736437
  responsible party type: Sponsor
  mesh term: Colorectal Neoplasms
  sharing ipd: No