<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/NCT03759756</url>
  </required_header>
  <id_info>
    <org_study_id>2018SDU-QILU-2</org_study_id>
    <nct_id>NCT03759756</nct_id>
  </id_info>
  <brief_title>Artificial Intelligence for Early Diagnosis of Esophageal Squamous Cell Carcinoma</brief_title>
  <official_title>Application of Artificial Intelligence for Early Diagnosis of Esophageal Squamous Cell Carcinoma During Optical Enhancement Magnifying Endoscopy</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>
      Esophageal squamous cell carcinoma is one of the most common malignant tumor of upper&#xD;
      digestive tract. However, the detection rate and diagnosis accuracy of early esophageal&#xD;
      squamous cell cancer is low. The aim of this study is to develop a computer-assisted&#xD;
      diagnosis tool combining with optical magnifying endoscopy for early detection and accurate&#xD;
      diagnosis of it.&#xD;
    </textblock>
  </brief_summary>
  <overall_status>Completed</overall_status>
  <start_date type="Actual">December 1, 2018</start_date>
  <completion_date type="Actual">April 1, 2020</completion_date>
  <primary_completion_date type="Actual">March 1, 2020</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>Retrospective</time_perspective>
  </study_design_info>
  <primary_outcome>
    <measure>the diagnosis efficiency of the AI model</measure>
    <time_frame>12 months</time_frame>
    <description>the sensitivity, specificity and accuracy of the AI model</description>
  </primary_outcome>
  <number_of_groups>2</number_of_groups>
  <enrollment type="Actual">119</enrollment>
  <condition>Artificial Intelligence</condition>
  <condition>Optical Enhancement Endoscopy</condition>
  <condition>Magnifying Endoscopy</condition>
  <arm_group>
    <arm_group_label>AI visible group</arm_group_label>
    <description>the endoscopic novices analyzing the images can see the automatic diagnosis of AI during the process</description>
  </arm_group>
  <arm_group>
    <arm_group_label>AI invisible group</arm_group_label>
    <description>the endoscopic novice analyzing the images can not see the automatic diagnosis of AI during the process</description>
  </arm_group>
  <intervention>
    <intervention_type>Other</intervention_type>
    <intervention_name>AI presentation</intervention_name>
    <description>AI presentation means the automatic diagnosis information of AI and AI presentation means it is visible in the group.</description>
    <arm_group_label>AI visible group</arm_group_label>
  </intervention>
  <intervention>
    <intervention_type>Other</intervention_type>
    <intervention_name>no AI presentation</intervention_name>
    <description>AI presentation means the automatic diagnosis information of AI and no AI presentation means it is invisible in the group.</description>
    <arm_group_label>AI invisible group</arm_group_label>
  </intervention>
  <eligibility>
    <study_pop>
      <textblock>
        Consecutive patients who came to Qilu Hospital of Shandong University and received optical&#xD;
        magnifying OE endoscopy examination&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Non-Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  high risk patients for esophageal cancer aged 18 years or older;&#xD;
&#xD;
          -  Histologically verified early esophageal squamous cell cancer.&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  patients whose images of esophagus not suitable for the training, validation and&#xD;
             testing the computer-assist diagnosis tool.&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>18 Years</minimum_age>
    <maximum_age>N/A</maximum_age>
    <healthy_volunteers>No</healthy_volunteers>
  </eligibility>
  <overall_official>
    <last_name>Yanqing Li, PHD</last_name>
    <role>Principal Investigator</role>
    <affiliation>Qilu Hospital, Shandong University</affiliation>
  </overall_official>
  <location>
    <facility>
      <name>Department of Gastroenterology, Qilu Hospital, Shandong University</name>
      <address>
        <city>Jinan</city>
        <state>Shandong</state>
        <zip>250012</zip>
        <country>China</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>China</country>
  </location_countries>
  <verification_date>April 2020</verification_date>
  <study_first_submitted>November 28, 2018</study_first_submitted>
  <study_first_submitted_qc>November 28, 2018</study_first_submitted_qc>
  <study_first_posted type="Actual">November 30, 2018</study_first_posted>
  <last_update_submitted>April 25, 2020</last_update_submitted>
  <last_update_submitted_qc>April 25, 2020</last_update_submitted_qc>
  <last_update_posted type="Actual">April 28, 2020</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>Carcinoma, Squamous Cell</mesh_term>
    <mesh_term>Esophageal Squamous Cell Carcinoma</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/NCT03759756
org study id: 2018SDU-QILU-2
nct id: NCT03759756
lead sponsor: 
is fda regulated drug: No
is fda regulated device: No
Esophageal squamous cell carcinoma is one of the most common malignant tumor of upper
      digestive tract. However, the detection rate and diagnosis accuracy of early esophageal
      squamous cell cancer is low. The aim of this study is to develop a computer-assisted
      diagnosis tool combining with optical magnifying endoscopy for early detection and accurate
      diagnosis of it.
observational model: Other
time perspective: Retrospective
measure: the diagnosis efficiency of the AI model
time frame: 12 months
description: the sensitivity, specificity and accuracy of the AI model
arm group label: AI visible group
description: the endoscopic novices analyzing the images can see the automatic diagnosis of AI during the process
arm group label: AI invisible group
description: the endoscopic novice analyzing the images can not see the automatic diagnosis of AI during the process
intervention type: Other
intervention name: AI presentation
description: AI presentation means the automatic diagnosis information of AI and AI presentation means it is visible in the group.
arm group label: AI visible group
intervention type: Other
intervention name: no AI presentation
description: AI presentation means the automatic diagnosis information of AI and no AI presentation means it is invisible in the group.
arm group label: AI invisible group
study pop: 
sampling method: Non-Probability Sample
criteria: 
gender: All
minimum age: 18 Years
maximum age: N/A
healthy volunteers: No
last name: Yanqing Li, PHD
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: Carcinoma, Squamous Cell
mesh term: Esophageal Squamous Cell Carcinoma