<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/NCT04422548</url>
  </required_header>
  <id_info>
    <org_study_id>AI-CLN Study_Protocol</org_study_id>
    <nct_id>NCT04422548</nct_id>
  </id_info>
  <brief_title>Does AI-assisted Colonoscopy Improve Adenoma Detection in Screening Colonoscopy?</brief_title>
  <official_title>Does AI-assisted Colonoscopy Improve Adenoma Detection in Screening Colonoscopy? A Multi-center Randomized Controlled</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Chinese University of Hong Kong</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
  </sponsors>
  <source>Chinese University of Hong Kong</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>
      To date, there is a lack of large-scale randomized controlled study using AI assistance in&#xD;
      the detection of polyps/adenoma in a screening population. The correlation of fecal occult&#xD;
      blood test (FIT or FOBT) and the advantage of AI-assisted colonoscopy has not been&#xD;
      investigated. There is also a lack of information of the benefit of AI-assisted colonoscopy&#xD;
      in experienced colonoscopist versus trainee/resident.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      There are several studies showing that AI-assisted colonoscopy can help in identifying and&#xD;
      characterizing polyps found on colonoscopy.&#xD;
&#xD;
        -  Byrne et al demonstrated that their AI model for real-time assessment of endoscopic&#xD;
           video images of colorectal polyp can differentiate between hyperplastic diminutive&#xD;
           polyps vs adenomatous polyps with sensitivity of 98% and specificity of 83% (Byrne et&#xD;
           al. GUT 2019)&#xD;
&#xD;
        -  Urban et al designed and trained deep CNNs to detect polyps in archived video with a ROC&#xD;
           curve of 0.991 and accuracy of 96.4%. The total number of polyps identified is&#xD;
           significantly higher but mainly in the small (1-3mm and 4-6mm polyps) (Urban et al.&#xD;
           Gastroenterol 2018)&#xD;
&#xD;
        -  Wang et al conducted an open, non-blinded trial consecutive patients (n=1058)&#xD;
           prospectively randomized to undergo diagnostic colonoscopy with or without AI&#xD;
           assistance. They found that AI system increased ADR from 20.3% to 29.1% and the mean&#xD;
           number of adenomas per patients from 0.31 to 0.53. This was due to a higher number of&#xD;
           diminutive polyps found while there was no statistic difference in larger adenoma. (Wang&#xD;
           et al. GUT 2019). In this study, they excluded patients with IBD, CRC and colorectal&#xD;
           surgery. The patients presented with symptoms to hospital for investigation.&#xD;
&#xD;
      To date, there is a lack of large-scale randomized controlled study using AI assistance in&#xD;
      the detection of polyps/adenoma in a screening population. The correlation of fecal occult&#xD;
      blood test (FIT or FOBT) and the advantage of AI-assisted colonoscopy has not been&#xD;
      investigated. There is also a lack of information of the benefit of AI-assisted colonoscopy&#xD;
      in experienced colonoscopist versus trainee/resident.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Unknown status</overall_status>
  <last_known_status>Recruiting</last_known_status>
  <start_date type="Actual">November 28, 2019</start_date>
  <completion_date type="Anticipated">November 27, 2020</completion_date>
  <primary_completion_date type="Anticipated">November 27, 2020</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>Single (Participant)</masking>
  </study_design_info>
  <primary_outcome>
    <measure>Per-patient ADR in each group</measure>
    <time_frame>12 months</time_frame>
    <description>For the AI-Assisted group, it is defined as the number of patients with at least 1 adenoma identified in the colon divided by the total number of patients in the AI-Assisted group.</description>
  </primary_outcome>
  <primary_outcome>
    <measure>Per-patient ADR in each group</measure>
    <time_frame>12 months</time_frame>
    <description>For the Standard group, it is defined as the number of patients with at least 1 adenoma identified in the colon divided by the total number of patients in the Standard group.</description>
  </primary_outcome>
  <number_of_arms>2</number_of_arms>
  <enrollment type="Anticipated">2994</enrollment>
  <condition>Screening Colonoscopy</condition>
  <arm_group>
    <arm_group_label>AI-assisted Group</arm_group_label>
    <arm_group_type>Active Comparator</arm_group_type>
  </arm_group>
  <arm_group>
    <arm_group_label>Standard</arm_group_label>
    <arm_group_type>Active Comparator</arm_group_type>
  </arm_group>
  <intervention>
    <intervention_type>Procedure</intervention_type>
    <intervention_name>AI-assisted Colonoscopy</intervention_name>
    <description>This is a multi-center prospective randomized controlled study comparing real-time AI-assisted colonoscopy versus standard colonoscopy in a real-life setting.</description>
    <arm_group_label>AI-assisted Group</arm_group_label>
  </intervention>
  <intervention>
    <intervention_type>Procedure</intervention_type>
    <intervention_name>Standard Colonoscopy</intervention_name>
    <description>Standard Colonoscopy</description>
    <arm_group_label>Standard</arm_group_label>
  </intervention>
  <eligibility>
    <criteria>
      <textblock>
        Inclusion Criteria&#xD;
&#xD;
          -  Patients receiving colonoscopy screening&#xD;
&#xD;
          -  Patients aged 45-75 years&#xD;
&#xD;
          -  Both patients who have or have not done a FIT test and both FIT +ve and FIT -ve&#xD;
             subjects&#xD;
&#xD;
        Exclusion Criteria&#xD;
&#xD;
          -  Patients who have symptom(s) suggestive of colorectal diseases&#xD;
&#xD;
          -  Patients who have a history of inflammatory bowel disease, colorectal cancer or&#xD;
             polyposis syndrome (anaemia, bloody stool, tenesmus and obstructive symptoms)&#xD;
&#xD;
          -  Patients who had colonoscopy or other investigation of colon and rectum in the past 10&#xD;
             years&#xD;
&#xD;
          -  Patients who had surgery for colorectal diseases&#xD;
&#xD;
          -  Patients who cannot tolerate bowel preparation or have suboptimal bowel preparations&#xD;
             (Boston Bowel Preparation Scale)&#xD;
&#xD;
          -  Cannot reach caecum&#xD;
&#xD;
          -  Patients who are incompetent in giving informed consent&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>45 Years</minimum_age>
    <maximum_age>75 Years</maximum_age>
    <healthy_volunteers>Accepts Healthy Volunteers</healthy_volunteers>
  </eligibility>
  <overall_contact>
    <last_name>Andrew Ming Yeung HO</last_name>
    <phone>26371398</phone>
    <email>andrewho@cuhk.edu.hk</email>
  </overall_contact>
  <overall_contact_backup>
    <last_name>Thomas Yuen Tung LAM</last_name>
    <phone>26370355</phone>
    <email>thomaslam@cuhk.edu.hk</email>
  </overall_contact_backup>
  <location>
    <facility>
      <name>Prince of Wales Hospital</name>
      <address>
        <city>Hong Kong</city>
        <country>Hong Kong</country>
      </address>
    </facility>
    <status>Recruiting</status>
    <contact>
      <last_name>Ming Yeung HO</last_name>
      <phone>26371398</phone>
      <email>andrewho@cuhk.edu.hk</email>
    </contact>
  </location>
  <location_countries>
    <country>Hong Kong</country>
  </location_countries>
  <verification_date>July 2020</verification_date>
  <study_first_submitted>June 5, 2020</study_first_submitted>
  <study_first_submitted_qc>June 8, 2020</study_first_submitted_qc>
  <study_first_posted type="Actual">June 9, 2020</study_first_posted>
  <last_update_submitted>July 14, 2020</last_update_submitted>
  <last_update_submitted_qc>July 14, 2020</last_update_submitted_qc>
  <last_update_posted type="Actual">July 16, 2020</last_update_posted>
  <responsible_party>
    <responsible_party_type>Principal Investigator</responsible_party_type>
    <investigator_affiliation>Chinese University of Hong Kong</investigator_affiliation>
    <investigator_full_name>Joseph JY SUNG</investigator_full_name>
    <investigator_title>Professor</investigator_title>
  </responsible_party>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Adenoma</mesh_term>
  </condition_browse>
  <patient_data>
    <sharing_ipd>No</sharing_ipd>
  </patient_data>
  <!-- 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/NCT04422548
org study id: AI-CLN Study_Protocol
nct id: NCT04422548
lead sponsor: 
is fda regulated drug: No
is fda regulated device: No
To date, there is a lack of large-scale randomized controlled study using AI assistance in
      the detection of polyps/adenoma in a screening population. The correlation of fecal occult
      blood test (FIT or FOBT) and the advantage of AI-assisted colonoscopy has not been
      investigated. There is also a lack of information of the benefit of AI-assisted colonoscopy
      in experienced colonoscopist versus trainee/resident.
There are several studies showing that AI-assisted colonoscopy can help in identifying and
      characterizing polyps found on colonoscopy.

        -  Byrne et al demonstrated that their AI model for real-time assessment of endoscopic
           video images of colorectal polyp can differentiate between hyperplastic diminutive
           polyps vs adenomatous polyps with sensitivity of 98% and specificity of 83% (Byrne et
           al. GUT 2019)

        -  Urban et al designed and trained deep CNNs to detect polyps in archived video with a ROC
           curve of 0.991 and accuracy of 96.4%. The total number of polyps identified is
           significantly higher but mainly in the small (1-3mm and 4-6mm polyps) (Urban et al.
           Gastroenterol 2018)

        -  Wang et al conducted an open, non-blinded trial consecutive patients (n=1058)
           prospectively randomized to undergo diagnostic colonoscopy with or without AI
           assistance. They found that AI system increased ADR from 20.3% to 29.1% and the mean
           number of adenomas per patients from 0.31 to 0.53. This was due to a higher number of
           diminutive polyps found while there was no statistic difference in larger adenoma. (Wang
           et al. GUT 2019). In this study, they excluded patients with IBD, CRC and colorectal
           surgery. The patients presented with symptoms to hospital for investigation.

      To date, there is a lack of large-scale randomized controlled study using AI assistance in
      the detection of polyps/adenoma in a screening population. The correlation of fecal occult
      blood test (FIT or FOBT) and the advantage of AI-assisted colonoscopy has not been
      investigated. There is also a lack of information of the benefit of AI-assisted colonoscopy
      in experienced colonoscopist versus trainee/resident.
allocation: Randomized
intervention model: Parallel Assignment
primary purpose: Diagnostic
masking: Single (Participant)
measure: Per-patient ADR in each group
time frame: 12 months
description: For the AI-Assisted group, it is defined as the number of patients with at least 1 adenoma identified in the colon divided by the total number of patients in the AI-Assisted group.
measure: Per-patient ADR in each group
time frame: 12 months
description: For the Standard group, it is defined as the number of patients with at least 1 adenoma identified in the colon divided by the total number of patients in the Standard group.
arm group label: AI-assisted Group
arm group type: Active Comparator
arm group label: Standard
arm group type: Active Comparator
intervention type: Procedure
intervention name: AI-assisted Colonoscopy
description: This is a multi-center prospective randomized controlled study comparing real-time AI-assisted colonoscopy versus standard colonoscopy in a real-life setting.
arm group label: AI-assisted Group
intervention type: Procedure
intervention name: Standard Colonoscopy
description: Standard Colonoscopy
arm group label: Standard
criteria: 
gender: All
minimum age: 45 Years
maximum age: 75 Years
healthy volunteers: Accepts Healthy Volunteers
last name: Andrew Ming Yeung HO
phone: 26371398
email: andrewho@cuhk.edu.hk
last name: Thomas Yuen Tung LAM
phone: 26370355
email: thomaslam@cuhk.edu.hk
facility: 
status: Recruiting
contact: 
country: Hong Kong
responsible party type: Principal Investigator
investigator affiliation: Chinese University of Hong Kong
investigator full name: Joseph JY SUNG
investigator title: Professor
mesh term: Adenoma
sharing ipd: No