<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/NCT03787784</url>
  </required_header>
  <id_info>
    <org_study_id>2018SDU-QILU-8</org_study_id>
    <nct_id>NCT03787784</nct_id>
  </id_info>
  <brief_title>Automatic Classification of Colorectal Polyps Using Probe-based Endomicroscopy With Artificial Intelligence</brief_title>
  <official_title>Automatic Classification of Colorectal Polyps Using Probe-based Endomicroscopy With Artificial Intelligence</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Shandong University</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
  </sponsors>
  <source>Shandong University</source>
  <oversight_info>
    <is_fda_regulated_drug>No</is_fda_regulated_drug>
    <is_fda_regulated_device>No</is_fda_regulated_device>
  </oversight_info>
  <brief_summary>
    <textblock>
      Probe-based confocal laser endomicroscopy (pCLE) is an endoscopic technique that enables&#xD;
      real-time histological evaluation of gastrointestinal mucosa during ongoing endoscopy&#xD;
      examination. It can predict the classification of Colorectal Polyps accurately. However this&#xD;
      requires much experience, which limits the application of pCLE. The investigators designed a&#xD;
      computer program using deep neural networks to differentiate hyperplastic from neoplastic&#xD;
      polyps automatically in pCLE examination.&#xD;
    </textblock>
  </brief_summary>
  <overall_status>Unknown status</overall_status>
  <last_known_status>Recruiting</last_known_status>
  <start_date type="Actual">May 1, 2018</start_date>
  <completion_date type="Anticipated">March 30, 2019</completion_date>
  <primary_completion_date type="Anticipated">January 30, 2019</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>Diagnostic</primary_purpose>
    <masking>Triple (Participant, Investigator, Outcomes Assessor)</masking>
  </study_design_info>
  <primary_outcome>
    <measure>The accuracy of classifying colorectal Polyps using Probe-based endomicroscopy with deep neural networks</measure>
    <time_frame>4 months</time_frame>
    <description>The primary outcome is to test the diagnostic accuracy, sensitivity, specificity, PPV, NPV of the Artificial Intelligence for diagnosing Colorectal Polyps on real-time pCLE examination.</description>
  </primary_outcome>
  <secondary_outcome>
    <measure>Contrast the diagnosis efficiency of Artificial Intelligence with endoscopists</measure>
    <time_frame>3 month</time_frame>
    <description>The secondary outcome is to compare the diagnosis efficiency (including diagnostic accuracy, sensitivity, specificity, PPV, NPV for diagnosing Colorectal Polyps on real-time pCLE examination) between Artificial Intelligence and endoscopists.</description>
  </secondary_outcome>
  <number_of_arms>2</number_of_arms>
  <enrollment type="Anticipated">200</enrollment>
  <condition>Probe-based Confocal Laser Endomicroscopy</condition>
  <condition>Artificial Intelligence</condition>
  <condition>Colorectal Polyps</condition>
  <arm_group>
    <arm_group_label>AI visible group</arm_group_label>
    <arm_group_type>Experimental</arm_group_type>
  </arm_group>
  <arm_group>
    <arm_group_label>AI invisible group</arm_group_label>
    <arm_group_type>No Intervention</arm_group_type>
  </arm_group>
  <intervention>
    <intervention_type>Other</intervention_type>
    <intervention_name>AI presentation</intervention_name>
    <description>Automatic diagnosis information of AI is visible to endoscopist</description>
    <arm_group_label>AI visible group</arm_group_label>
  </intervention>
  <eligibility>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
        aged between 18 and 80; agree to give written informed consent.&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
        Patients under conditions unsuitable for performing CLE including coagulopathy , impaired&#xD;
        renal or hepatic function, pregnancy or breastfeeding, and known allergy to fluorescein&#xD;
        sodium; Inability to provide informed consent&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>18 Years</minimum_age>
    <maximum_age>80 Years</maximum_age>
    <healthy_volunteers>Accepts Healthy Volunteers</healthy_volunteers>
  </eligibility>
  <overall_contact>
    <last_name>Yangqing Li, PHD.MD.</last_name>
    <phone>053182169385</phone>
    <email>liyanqing@sdu.edu.cn</email>
  </overall_contact>
  <location>
    <facility>
      <name>Endoscopic unit of Qilu Hospital Shandong University</name>
      <address>
        <city>Jinan</city>
        <state>Shandong</state>
        <zip>250001</zip>
        <country>China</country>
      </address>
    </facility>
    <status>Recruiting</status>
    <contact>
      <last_name>Yanqing Li, PhD,MD</last_name>
    </contact>
  </location>
  <location_countries>
    <country>China</country>
  </location_countries>
  <verification_date>September 2018</verification_date>
  <study_first_submitted>December 21, 2018</study_first_submitted>
  <study_first_submitted_qc>December 21, 2018</study_first_submitted_qc>
  <study_first_posted type="Actual">December 26, 2018</study_first_posted>
  <last_update_submitted>December 21, 2018</last_update_submitted>
  <last_update_submitted_qc>December 21, 2018</last_update_submitted_qc>
  <last_update_posted type="Actual">December 26, 2018</last_update_posted>
  <responsible_party>
    <responsible_party_type>Principal Investigator</responsible_party_type>
    <investigator_affiliation>Shandong University</investigator_affiliation>
    <investigator_full_name>Yanqing Li</investigator_full_name>
    <investigator_title>Vice president of QiLu Hospital</investigator_title>
  </responsible_party>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Polyps</mesh_term>
  </condition_browse>
  <!-- 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/NCT03787784
org study id: 2018SDU-QILU-8
nct id: NCT03787784
lead sponsor: 
is fda regulated drug: No
is fda regulated device: No
Probe-based confocal laser endomicroscopy (pCLE) is an endoscopic technique that enables
      real-time histological evaluation of gastrointestinal mucosa during ongoing endoscopy
      examination. It can predict the classification of Colorectal Polyps accurately. However this
      requires much experience, which limits the application of pCLE. The investigators designed a
      computer program using deep neural networks to differentiate hyperplastic from neoplastic
      polyps automatically in pCLE examination.
allocation: Randomized
intervention model: Parallel Assignment
primary purpose: Diagnostic
masking: Triple (Participant, Investigator, Outcomes Assessor)
measure: The accuracy of classifying colorectal Polyps using Probe-based endomicroscopy with deep neural networks
time frame: 4 months
description: The primary outcome is to test the diagnostic accuracy, sensitivity, specificity, PPV, NPV of the Artificial Intelligence for diagnosing Colorectal Polyps on real-time pCLE examination.
measure: Contrast the diagnosis efficiency of Artificial Intelligence with endoscopists
time frame: 3 month
description: The secondary outcome is to compare the diagnosis efficiency (including diagnostic accuracy, sensitivity, specificity, PPV, NPV for diagnosing Colorectal Polyps on real-time pCLE examination) between Artificial Intelligence and endoscopists.
arm group label: AI visible group
arm group type: Experimental
arm group label: AI invisible group
arm group type: No Intervention
intervention type: Other
intervention name: AI presentation
description: Automatic diagnosis information of AI is visible to endoscopist
arm group label: AI visible group
criteria: 
gender: All
minimum age: 18 Years
maximum age: 80 Years
healthy volunteers: Accepts Healthy Volunteers
last name: Yangqing Li, PHD.MD.
phone: 053182169385
email: liyanqing@sdu.edu.cn
facility: 
status: Recruiting
contact: 
country: China
responsible party type: Principal Investigator
investigator affiliation: Shandong University
investigator full name: Yanqing Li
investigator title: Vice president of QiLu Hospital
mesh term: Polyps