<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/NCT03784209</url>
  </required_header>
  <id_info>
    <org_study_id>2018SDU-QILU-12</org_study_id>
    <nct_id>NCT03784209</nct_id>
  </id_info>
  <brief_title>Automatic Real-time Diagnosis of Gastric Mucosal Disease Using pCLE With Artificial Intelligence</brief_title>
  <official_title>Automatic Real-time Diagnosis of Gastric Mucosal Disease Using Probe-based Confocal Laser 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 gastric mucosal disease during ongoing endoscopy&#xD;
      examination. However this requires much experience, which limits the application of pCLE. The&#xD;
      investigators designed a computer-aided diagnosis program using deep neural network to make&#xD;
      diagnosis automatically in pCLE examination and contrast its performance with endoscopists.&#xD;
    </textblock>
  </brief_summary>
  <overall_status>Completed</overall_status>
  <start_date type="Actual">July 1, 2018</start_date>
  <completion_date type="Actual">September 29, 2021</completion_date>
  <primary_completion_date type="Actual">September 29, 2021</primary_completion_date>
  <study_type>Observational</study_type>
  <has_expanded_access>No</has_expanded_access>
  <study_design_info>
    <observational_model>Other</observational_model>
    <time_perspective>Prospective</time_perspective>
  </study_design_info>
  <primary_outcome>
    <measure>The diagnosis efficiency of Artificial Intelligence</measure>
    <time_frame>24 months</time_frame>
    <description>The primary outcome is to test the diagnostic accuracy, sensitivity, specificity, PPV, NPV of the Artificial Intelligence for diagnosing gastric mucosal disease on real-time pCLE examination.</description>
  </primary_outcome>
  <secondary_outcome>
    <measure>Contrast the diagnosis efficiency of Artificial Intelligence with endoscopists</measure>
    <time_frame>24 months</time_frame>
    <description>The secondary outcome is to compare the diagnosis efficiency (including diagnostic accuracy, sensitivity, specificity, PPV, NPV for diagnosing gastric mucosal disease on real-time pCLE examination) between Artificial Intelligence and endoscopists.</description>
  </secondary_outcome>
  <number_of_groups>1</number_of_groups>
  <enrollment type="Actual">951</enrollment>
  <condition>Gastric Diseases</condition>
  <condition>Artificial Intelligence</condition>
  <condition>Confocal Laser Endomicroscopy</condition>
  <arm_group>
    <arm_group_label>lesions observed by pCLE</arm_group_label>
    <description>pCLE is used to distinguish the suspected lesions detected by white light endoscopy.</description>
  </arm_group>
  <intervention>
    <intervention_type>Diagnostic Test</intervention_type>
    <intervention_name>The diagnosis of Artificial Intelligence and endoscopist</intervention_name>
    <description>When suspected lesion is observed using pCLE, endoscopist and AI will make a diagnosis independently. In addition, the endoscopist can not see the diagnosis of AI.</description>
    <arm_group_label>lesions observed by pCLE</arm_group_label>
  </intervention>
  <biospec_retention>Samples With DNA</biospec_retention>
  <biospec_descr>
    <textblock>
      When a gastric mucosal lesion is found using white light endoscopy , endoscopist will observe&#xD;
      this lesion using pCLE and then take biopsy for histology examination.&#xD;
    </textblock>
  </biospec_descr>
  <eligibility>
    <study_pop>
      <textblock>
        Consecutive patients who receive the upper gastrointestinal tract pCLE examination and&#xD;
        screened that fulfill the eligibility criteria at Qilu Hospital, Shandong University will&#xD;
        be enrolled into the study&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Non-Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  aged between 18 and 80;&#xD;
&#xD;
          -  agree to give written informed consent.&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  Patients under conditions unsuitable for performing CLE including coagulopathy ,&#xD;
             impaired renal or hepatic function, pregnancy or breastfeeding, and known allergy to&#xD;
             fluorescein sodium;&#xD;
&#xD;
          -  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>No</healthy_volunteers>
  </eligibility>
  <overall_official>
    <last_name>Yanqing Li</last_name>
    <role>Principal Investigator</role>
    <affiliation>Qilu Hospital, Shandong University</affiliation>
  </overall_official>
  <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>
  </location>
  <location_countries>
    <country>China</country>
  </location_countries>
  <verification_date>March 2022</verification_date>
  <study_first_submitted>December 16, 2018</study_first_submitted>
  <study_first_submitted_qc>December 19, 2018</study_first_submitted_qc>
  <study_first_posted type="Actual">December 21, 2018</study_first_posted>
  <last_update_submitted>March 20, 2022</last_update_submitted>
  <last_update_submitted_qc>March 20, 2022</last_update_submitted_qc>
  <last_update_posted type="Actual">April 1, 2022</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>Stomach Diseases</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/NCT03784209
org study id: 2018SDU-QILU-12
nct id: NCT03784209
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 gastric mucosal disease during ongoing endoscopy
      examination. However this requires much experience, which limits the application of pCLE. The
      investigators designed a computer-aided diagnosis program using deep neural network to make
      diagnosis automatically in pCLE examination and contrast its performance with endoscopists.
observational model: Other
time perspective: Prospective
measure: The diagnosis efficiency of Artificial Intelligence
time frame: 24 months
description: The primary outcome is to test the diagnostic accuracy, sensitivity, specificity, PPV, NPV of the Artificial Intelligence for diagnosing gastric mucosal disease on real-time pCLE examination.
measure: Contrast the diagnosis efficiency of Artificial Intelligence with endoscopists
time frame: 24 months
description: The secondary outcome is to compare the diagnosis efficiency (including diagnostic accuracy, sensitivity, specificity, PPV, NPV for diagnosing gastric mucosal disease on real-time pCLE examination) between Artificial Intelligence and endoscopists.
arm group label: lesions observed by pCLE
description: pCLE is used to distinguish the suspected lesions detected by white light endoscopy.
intervention type: Diagnostic Test
intervention name: The diagnosis of Artificial Intelligence and endoscopist
description: When suspected lesion is observed using pCLE, endoscopist and AI will make a diagnosis independently. In addition, the endoscopist can not see the diagnosis of AI.
arm group label: lesions observed by pCLE
When a gastric mucosal lesion is found using white light endoscopy , endoscopist will observe
      this lesion using pCLE and then take biopsy for histology examination.
study pop: 
sampling method: Non-Probability Sample
criteria: 
gender: All
minimum age: 18 Years
maximum age: 80 Years
healthy volunteers: No
last name: Yanqing Li
role: Principal Investigator
affiliation: Qilu Hospital, Shandong University
facility: 
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: Stomach Diseases