<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/NCT04442607</url>
  </required_header>
  <id_info>
    <org_study_id>S64243</org_study_id>
    <nct_id>NCT04442607</nct_id>
  </id_info>
  <brief_title>Clinical vAliDation of ARTificial Intelligence in POlyp Detection</brief_title>
  <acronym>CAD-ARTIPOD</acronym>
  <official_title>Clinical vAliDation of ARTificial Intelligence in POlyp Detection</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Universitaire Ziekenhuizen KU Leuven</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
    <collaborator>
      <agency>Nuovo Regina Margherita Hospital, Rome, Italy</agency>
      <agency_class>Other</agency_class>
    </collaborator>
    <collaborator>
      <agency>Krankenhaus Barmherzige Brüder, Regensburg, Germany</agency>
      <agency_class>Other</agency_class>
    </collaborator>
    <collaborator>
      <agency>Centre Hospitalier Universitaire de Nantes, Nantes, France</agency>
      <agency_class>Other</agency_class>
    </collaborator>
    <collaborator>
      <agency>Centrum Onkologii-Instytut im. Marii Skłodowskiej-Curie, Warschau, Poland</agency>
      <agency_class>Other</agency_class>
    </collaborator>
    <collaborator>
      <agency>Spire Portsmouth Hospital, Portsmouth, United Kingdom</agency>
      <agency_class>Other</agency_class>
    </collaborator>
    <collaborator>
      <agency>University Medical Center, Amsterdam, The Netherlands</agency>
      <agency_class>Other</agency_class>
    </collaborator>
    <collaborator>
      <agency>University Hospitals Ghent, Ghent, Belgium</agency>
      <agency_class>Other</agency_class>
    </collaborator>
  </sponsors>
  <source>Universitaire Ziekenhuizen KU Leuven</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>
      This study is an open label, unblinded, non-randomized interventional study, comparing the&#xD;
      investigational artificial intelligence tool with the current &quot;gold standard&quot;: Data&#xD;
      acquisition will be obtained during one scheduled colonoscopic procedure by a trained&#xD;
      endoscopist. During insertion, no action will be taken, colonoscopy is performed following&#xD;
      the standard of care. Once withdrawal is started, a second observer (not a trained&#xD;
      endoscopist but person trained in polyp recognition) will start the bedside Artificial&#xD;
      intelligence (AI) tool, connected to the endoscope's tower, for detection. This second&#xD;
      observer is trained in assessing endoscopic images to define the AI tool's outcome. Due to&#xD;
      the second observer watching the separate AI screen, the endoscopist is blinded of the AI&#xD;
      outcome. When a detection is made by the AI system that is not recognized by the endoscopist,&#xD;
      the endoscopist will be asked to relocate that same detection and to reassess the lesion and&#xD;
      the possible need of therapeutic action. All detections are separately counted and&#xD;
      categorized by the second observer. All polyp detections will be removed following standard&#xD;
      of care for histological assessment. The entire colonoscopic procedure is recorded via a&#xD;
      separate linked video-recorder.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      This is an investigator-initiated non-randomized prospective interventional trial to validate&#xD;
      the performance of a novel state-of-the-art computer-aided detection (CADe) tool for&#xD;
      colorectal polyp detection implemented as second observer during routine diagnostic&#xD;
      colonoscopy and to evaluate its feasibility in daily endoscopy. Consecutive patients referred&#xD;
      for a screening, surveillance or diagnostic colonoscopy will be included.&#xD;
&#xD;
      Patients will undergo a standard colonoscopy performed by a trained endoscopist. A second&#xD;
      observer, who is not a trained endoscopist, will follow the procedure on a bedside AI-tool to&#xD;
      count the number of detections made by the AI system and categorize the results into positive&#xD;
      or negative results as follows (1) true positive, (2) false negative or (3) false positive.&#xD;
      In case of a detection of the AI-system that was not seen by the endoscopist or unclear to&#xD;
      the second observer, the second observer will ask to re-evaluate the indicated region to&#xD;
      determine whether after second look the endoscopist has to take extra action. The entire&#xD;
      procedure will be recorded.&#xD;
&#xD;
      There are no additional risks specific to the use of the AI tool to be taken into account.&#xD;
      General risk of colonoscopy (i.e.: perforation, bleeding or post-polypectomy syndrome) could&#xD;
      occur with the same frequency as that of a colonoscopy without the use of this AI tool.&#xD;
&#xD;
      All patients will receive a standard of care protocol during their colonoscopy. The AI system&#xD;
      can only have a beneficial outcome for the patient, a better polyp detection, as it has shown&#xD;
      to be non-inferior in terms of accuracy when compared to high detecting endoscopist in our&#xD;
      pilot trial&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Completed</overall_status>
  <start_date type="Actual">October 13, 2020</start_date>
  <completion_date type="Actual">November 29, 2022</completion_date>
  <primary_completion_date type="Actual">October 28, 2022</primary_completion_date>
  <phase>N/A</phase>
  <study_type>Interventional</study_type>
  <has_expanded_access>No</has_expanded_access>
  <study_design_info>
    <allocation>N/A</allocation>
    <intervention_model>Single Group Assignment</intervention_model>
    <intervention_model_description>This is an investigator-initiated non-randomized prospective interventional trial to validate the performance of a novel state-of-the-art computer-aided detection (CADe) tool for colorectal polyp detection implemented as second observer during routine diagnostic colonoscopy and to evaluate its feasibility in daily endoscopy.</intervention_model_description>
    <primary_purpose>Diagnostic</primary_purpose>
    <masking>None (Open Label)</masking>
  </study_design_info>
  <primary_outcome>
    <measure>Total polyp detection during single pass colonoscopy by the artificial intelligence tool in comparison to polyp detection by the endoscopist with endoscopic diagnosis as a gold standard</measure>
    <time_frame>1.5 year</time_frame>
  </primary_outcome>
  <secondary_outcome>
    <measure>Total polyp detection during single pass colonoscopy by the artificial intelligence tool in comparison to polyp detection by the endoscopist with histological diagnosis as a gold standard.</measure>
    <time_frame>1.5 year</time_frame>
  </secondary_outcome>
  <secondary_outcome>
    <measure>The number of extra detected polyps by artificial intelligence with the endoscopic diagnosis as a gold standard.</measure>
    <time_frame>1.5 year</time_frame>
  </secondary_outcome>
  <secondary_outcome>
    <measure>The number of extra detected polyps by artificial intelligence with the histological diagnosis as a gold standard</measure>
    <time_frame>1.5 year</time_frame>
  </secondary_outcome>
  <secondary_outcome>
    <measure>The endoscopist's polyp miss rate defined as the additional detection of polyps during colonoscopy</measure>
    <time_frame>1.5 year</time_frame>
  </secondary_outcome>
  <secondary_outcome>
    <measure>The false positive rate during clean withdrawal.</measure>
    <time_frame>1.5 year</time_frame>
  </secondary_outcome>
  <other_outcome>
    <measure>Correlation between the Boston Bowel Preparation Score and the number of false positive detections during colonoscopy</measure>
    <time_frame>1.5 year</time_frame>
  </other_outcome>
  <other_outcome>
    <measure>Correlation between the endoscopist's historical adenoma detection rate and the number of extra detections and false negative detections by the artificial intelligence system.</measure>
    <time_frame>1.5 year</time_frame>
  </other_outcome>
  <other_outcome>
    <measure>Correlation between the polyp size and number of false negatives and additional detections</measure>
    <time_frame>1.5 year</time_frame>
  </other_outcome>
  <other_outcome>
    <measure>Correlation between the Paris classification and the number of false negatives and additional detections.</measure>
    <time_frame>1.5 year</time_frame>
  </other_outcome>
  <other_outcome>
    <measure>Correlation between the total number of polyps per colonoscopy and additional detections.</measure>
    <time_frame>1.5 year</time_frame>
  </other_outcome>
  <other_outcome>
    <measure>Correlation between the experience of the endoscopist and additional detections</measure>
    <time_frame>1.5 year</time_frame>
  </other_outcome>
  <number_of_arms>1</number_of_arms>
  <enrollment type="Actual">856</enrollment>
  <condition>Polyp of Colon</condition>
  <arm_group>
    <arm_group_label>AI arm</arm_group_label>
    <arm_group_type>Experimental</arm_group_type>
    <description>Only one arm in this study. Every patient who is eligible for this study and is included, after informed consent, will receive a standard colonoscopy combined with real-time AI video analysis</description>
  </arm_group>
  <intervention>
    <intervention_type>Device</intervention_type>
    <intervention_name>artificial intelligence image processing</intervention_name>
    <description>Patients will undergo a standard colonoscopy performed by a trained endoscopist. A second observer, who is not a trained endoscopist, will follow the procedure on a bedside AI-tool to count the number of detections made by the AI system and categorize the results into positive or negative results as follows (1) true positive, (2) false negative or (3) false positive.</description>
    <arm_group_label>AI arm</arm_group_label>
  </intervention>
  <eligibility>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  Age ≥40 years&#xD;
&#xD;
          -  Referral for screening, surveillance or diagnostic colonoscopy&#xD;
&#xD;
          -  Able to give informed consent by the patient or by a legal representative&#xD;
&#xD;
        Exclusion criteria for study inclusion&#xD;
&#xD;
          -  &lt;40 years old&#xD;
&#xD;
          -  Referral for a therapeutic colonoscopy&#xD;
&#xD;
          -  Known Lynch syndrome or Familial Adenomatous Polyposis syndrome&#xD;
&#xD;
          -  Any contraindication for colonoscopy or biopsies of the colon&#xD;
&#xD;
          -  Uncontrolled coagulopathy&#xD;
&#xD;
          -  Confirmed diagnosis of inflammatory bowel disease prior to the scheduled colonoscopy&#xD;
&#xD;
          -  Short bowel or ileostomy&#xD;
&#xD;
          -  Pregnancy&#xD;
&#xD;
        Exclusion criteria for study analysis&#xD;
&#xD;
          -  Colonic inflammation of &gt; 30cm during colonoscopy&#xD;
&#xD;
          -  Incomplete colonoscopy for any reason&#xD;
&#xD;
          -  Incomplete recording or technical failure of the artificial intelligence system&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>40 Years</minimum_age>
    <maximum_age>N/A</maximum_age>
    <healthy_volunteers>Accepts Healthy Volunteers</healthy_volunteers>
  </eligibility>
  <overall_official>
    <last_name>Raf Bisschops, MD,PhD</last_name>
    <role>Principal Investigator</role>
    <affiliation>Universitaire Ziekenhuizen KU Leuven</affiliation>
  </overall_official>
  <location>
    <facility>
      <name>University Hospitals Leuven</name>
      <address>
        <city>Leuven</city>
        <state>Vlaams-Brabant</state>
        <zip>3000</zip>
        <country>Belgium</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>Belgium</country>
  </location_countries>
  <verification_date>November 2022</verification_date>
  <study_first_submitted>June 18, 2020</study_first_submitted>
  <study_first_submitted_qc>June 18, 2020</study_first_submitted_qc>
  <study_first_posted type="Actual">June 22, 2020</study_first_posted>
  <last_update_submitted>November 29, 2022</last_update_submitted>
  <last_update_submitted_qc>November 29, 2022</last_update_submitted_qc>
  <last_update_posted type="Actual">November 30, 2022</last_update_posted>
  <responsible_party>
    <responsible_party_type>Sponsor</responsible_party_type>
  </responsible_party>
  <keyword>colonic polyps</keyword>
  <keyword>endoscopy</keyword>
  <keyword>artificial intelligence</keyword>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Polyps</mesh_term>
    <mesh_term>Colonic Polyps</mesh_term>
  </condition_browse>
  <patient_data>
    <sharing_ipd>No</sharing_ipd>
    <ipd_description>We do not plan to make individual participant data available. We might share a overview of anonymized data with the collaborating institutions.</ipd_description>
  </patient_data>
  <provided_document_section>
    <provided_document>
      <document_type>Study Protocol and Statistical Analysis Plan</document_type>
      <document_has_protocol>Yes</document_has_protocol>
      <document_has_icf>No</document_has_icf>
      <document_has_sap>Yes</document_has_sap>
      <document_date>May 26, 2020</document_date>
      <document_url>https://ClinicalTrials.gov/ProvidedDocs/07/NCT04442607/Prot_SAP_000.pdf</document_url>
    </provided_document>
    <provided_document>
      <document_type>Informed Consent Form</document_type>
      <document_has_protocol>No</document_has_protocol>
      <document_has_icf>Yes</document_has_icf>
      <document_has_sap>No</document_has_sap>
      <document_date>May 26, 2020</document_date>
      <document_url>https://ClinicalTrials.gov/ProvidedDocs/07/NCT04442607/ICF_001.pdf</document_url>
    </provided_document>
  </provided_document_section>
  <!-- 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/NCT04442607
org study id: S64243
nct id: NCT04442607
lead sponsor: 
collaborator: 
collaborator: 
collaborator: 
collaborator: 
collaborator: 
collaborator: 
collaborator: 
has dmc: Yes
is fda regulated drug: No
is fda regulated device: No
This study is an open label, unblinded, non-randomized interventional study, comparing the
      investigational artificial intelligence tool with the current "gold standard": Data
      acquisition will be obtained during one scheduled colonoscopic procedure by a trained
      endoscopist. During insertion, no action will be taken, colonoscopy is performed following
      the standard of care. Once withdrawal is started, a second observer (not a trained
      endoscopist but person trained in polyp recognition) will start the bedside Artificial
      intelligence (AI) tool, connected to the endoscope's tower, for detection. This second
      observer is trained in assessing endoscopic images to define the AI tool's outcome. Due to
      the second observer watching the separate AI screen, the endoscopist is blinded of the AI
      outcome. When a detection is made by the AI system that is not recognized by the endoscopist,
      the endoscopist will be asked to relocate that same detection and to reassess the lesion and
      the possible need of therapeutic action. All detections are separately counted and
      categorized by the second observer. All polyp detections will be removed following standard
      of care for histological assessment. The entire colonoscopic procedure is recorded via a
      separate linked video-recorder.
This is an investigator-initiated non-randomized prospective interventional trial to validate
      the performance of a novel state-of-the-art computer-aided detection (CADe) tool for
      colorectal polyp detection implemented as second observer during routine diagnostic
      colonoscopy and to evaluate its feasibility in daily endoscopy. Consecutive patients referred
      for a screening, surveillance or diagnostic colonoscopy will be included.

      Patients will undergo a standard colonoscopy performed by a trained endoscopist. A second
      observer, who is not a trained endoscopist, will follow the procedure on a bedside AI-tool to
      count the number of detections made by the AI system and categorize the results into positive
      or negative results as follows (1) true positive, (2) false negative or (3) false positive.
      In case of a detection of the AI-system that was not seen by the endoscopist or unclear to
      the second observer, the second observer will ask to re-evaluate the indicated region to
      determine whether after second look the endoscopist has to take extra action. The entire
      procedure will be recorded.

      There are no additional risks specific to the use of the AI tool to be taken into account.
      General risk of colonoscopy (i.e.: perforation, bleeding or post-polypectomy syndrome) could
      occur with the same frequency as that of a colonoscopy without the use of this AI tool.

      All patients will receive a standard of care protocol during their colonoscopy. The AI system
      can only have a beneficial outcome for the patient, a better polyp detection, as it has shown
      to be non-inferior in terms of accuracy when compared to high detecting endoscopist in our
      pilot trial
allocation: N/A
intervention model: Single Group Assignment
intervention model description: This is an investigator-initiated non-randomized prospective interventional trial to validate the performance of a novel state-of-the-art computer-aided detection (CADe) tool for colorectal polyp detection implemented as second observer during routine diagnostic colonoscopy and to evaluate its feasibility in daily endoscopy.
primary purpose: Diagnostic
masking: None (Open Label)
measure: Total polyp detection during single pass colonoscopy by the artificial intelligence tool in comparison to polyp detection by the endoscopist with endoscopic diagnosis as a gold standard
time frame: 1.5 year
measure: Total polyp detection during single pass colonoscopy by the artificial intelligence tool in comparison to polyp detection by the endoscopist with histological diagnosis as a gold standard.
time frame: 1.5 year
measure: The number of extra detected polyps by artificial intelligence with the endoscopic diagnosis as a gold standard.
time frame: 1.5 year
measure: The number of extra detected polyps by artificial intelligence with the histological diagnosis as a gold standard
time frame: 1.5 year
measure: The endoscopist's polyp miss rate defined as the additional detection of polyps during colonoscopy
time frame: 1.5 year
measure: The false positive rate during clean withdrawal.
time frame: 1.5 year
measure: Correlation between the Boston Bowel Preparation Score and the number of false positive detections during colonoscopy
time frame: 1.5 year
measure: Correlation between the endoscopist's historical adenoma detection rate and the number of extra detections and false negative detections by the artificial intelligence system.
time frame: 1.5 year
measure: Correlation between the polyp size and number of false negatives and additional detections
time frame: 1.5 year
measure: Correlation between the Paris classification and the number of false negatives and additional detections.
time frame: 1.5 year
measure: Correlation between the total number of polyps per colonoscopy and additional detections.
time frame: 1.5 year
measure: Correlation between the experience of the endoscopist and additional detections
time frame: 1.5 year
arm group label: AI arm
arm group type: Experimental
description: Only one arm in this study. Every patient who is eligible for this study and is included, after informed consent, will receive a standard colonoscopy combined with real-time AI video analysis
intervention type: Device
intervention name: artificial intelligence image processing
description: Patients will undergo a standard colonoscopy performed by a trained endoscopist. A second observer, who is not a trained endoscopist, will follow the procedure on a bedside AI-tool to count the number of detections made by the AI system and categorize the results into positive or negative results as follows (1) true positive, (2) false negative or (3) false positive.
arm group label: AI arm
criteria: 
gender: All
minimum age: 40 Years
maximum age: N/A
healthy volunteers: Accepts Healthy Volunteers
last name: Raf Bisschops, MD,PhD
role: Principal Investigator
affiliation: Universitaire Ziekenhuizen KU Leuven
facility: 
country: Belgium
responsible party type: Sponsor
mesh term: Polyps
mesh term: Colonic Polyps
sharing ipd: No
ipd description: We do not plan to make individual participant data available. We might share a overview of anonymized data with the collaborating institutions.
provided document: 
provided document: 