<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/NCT04487626</url>
  </required_header>
  <id_info>
    <org_study_id>NCC2020-0159</org_study_id>
    <nct_id>NCT04487626</nct_id>
  </id_info>
  <brief_title>Development and Validation of an Artificial Intelligence System for Bowel Preparation Quality Scoring</brief_title>
  <official_title>Development and Validation of an Artificial Intelligence System for Bowel Preparation Quality Scoring</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>National Cancer Center, Korea</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
  </sponsors>
  <source>National Cancer Center, Korea</source>
  <oversight_info>
    <has_dmc>No</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>
      The purpose of this study is to develop and validate the accuracy and reliability of an&#xD;
      artificial intelligence(AI) system for bowel preparation quality scoring based of Boston&#xD;
      Bowel preparation Scoring(BBPS). Then evaluate whether this AI system can help endoscopists&#xD;
      to improve the quality of colonoscopy in clinical practice.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      Bowel preparation is one of the most important factors which decide the quality of&#xD;
      colonoscopy. Adequate bowel preparation is essential to guarantee a clear vision of colonic&#xD;
      mucosa, complete inspection of entire colon and improves the adenoma detection rates(ADRs).&#xD;
      Therefore, the quality of bowel preparation should be evaluated accurately and objectively.&#xD;
      However, current bowel preparation quality scales depend on memories and subjective decision&#xD;
      of endoscopists. Recently, articifial intelligence system based on deep learning algorithm&#xD;
      has been used widely in medical fields. But, few studies have been reported to evaluate the&#xD;
      performance of AI system based on deep learning in bowel preparation quality scoring. This&#xD;
      study aims to develop and train an AI system to assess bowel preparation quality using the&#xD;
      BBPS, and validate the AI system to improve the quality of colonoscopy.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Completed</overall_status>
  <start_date type="Actual">July 1, 2020</start_date>
  <completion_date type="Actual">December 30, 2020</completion_date>
  <primary_completion_date type="Actual">October 1, 2020</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>
    <primary_purpose>Health Services Research</primary_purpose>
    <masking>None (Open Label)</masking>
  </study_design_info>
  <primary_outcome>
    <measure>accuracy of bowel preparation score</measure>
    <time_frame>6 months</time_frame>
    <description>Bowel preparation quality was measured by BBPS. After fully washing or suctioning of colonic contents, three segments including right colon (containing cecum and ascending colon), transvers colon (containing hepatic and splenic flexures) and left colon (containing descending and sigmoid colon) were individually scored from 0 to 3. Point 0 refers to unprepared colon segment with obscured solid stool making mucosa cannot be seen; Point 1 refers to part of mucosa can be seen, but some areas are covered by staining, residual stool, and/or opaque liquid; Point 2 refers to entire mucosa is well-seen; Point 3 refers to clean colon segment without staining, fecal materials or liquids. A sub-score of each colon segment was used, ranging from minimum 0 to maximum 3. The highest score means the excellent bowel preparation. Adequate bowel preparation was defined as a total BBPS≥6 and sub-BBPS≥2 per segment.</description>
  </primary_outcome>
  <number_of_arms>1</number_of_arms>
  <enrollment type="Actual">100</enrollment>
  <condition>Colonic Disease</condition>
  <arm_group>
    <arm_group_label>Artificial Intelligence assisted Scoring Group</arm_group_label>
    <arm_group_type>Experimental</arm_group_type>
    <description>Patients in this group go through colonoscopy under the AI monitoring device.</description>
  </arm_group>
  <intervention>
    <intervention_type>Device</intervention_type>
    <intervention_name>AI</intervention_name>
    <description>After taking standard bowel preparation regimen, patients receive colonoscopy under the AI system device. During the withdrawal phase, bowel preparation quality is assessed by AI-associated scoring system. The withdrawal time is targeted at least 6min. Every detected polyp will be removed and obtained for pathological assessment.</description>
    <arm_group_label>Artificial Intelligence assisted Scoring Group</arm_group_label>
  </intervention>
  <eligibility>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  Patients aged 20-80 years undergoing elective colonoscopy&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  Patients received colon or rectal surgery&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>20 Years</minimum_age>
    <maximum_age>80 Years</maximum_age>
    <healthy_volunteers>No</healthy_volunteers>
  </eligibility>
  <location>
    <facility>
      <name>National Cancer Center</name>
      <address>
        <city>Goyang-si</city>
        <state>Gyeonggi-do</state>
        <zip>410-769</zip>
        <country>Korea, Republic of</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>Korea, Republic of</country>
  </location_countries>
  <verification_date>March 2021</verification_date>
  <study_first_submitted>July 22, 2020</study_first_submitted>
  <study_first_submitted_qc>July 22, 2020</study_first_submitted_qc>
  <study_first_posted type="Actual">July 27, 2020</study_first_posted>
  <last_update_submitted>March 1, 2021</last_update_submitted>
  <last_update_submitted_qc>March 1, 2021</last_update_submitted_qc>
  <last_update_posted type="Actual">March 3, 2021</last_update_posted>
  <responsible_party>
    <responsible_party_type>Principal Investigator</responsible_party_type>
    <investigator_affiliation>National Cancer Center, Korea</investigator_affiliation>
    <investigator_full_name>Dae Kyung Sohn</investigator_full_name>
    <investigator_title>Staff surgeon</investigator_title>
  </responsible_party>
  <keyword>artificial intelligence, deep learning, bowel preparation</keyword>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Colonic Diseases</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/NCT04487626
org study id: NCC2020-0159
nct id: NCT04487626
lead sponsor: 
has dmc: No
is fda regulated drug: No
is fda regulated device: No
The purpose of this study is to develop and validate the accuracy and reliability of an
      artificial intelligence(AI) system for bowel preparation quality scoring based of Boston
      Bowel preparation Scoring(BBPS). Then evaluate whether this AI system can help endoscopists
      to improve the quality of colonoscopy in clinical practice.
Bowel preparation is one of the most important factors which decide the quality of
      colonoscopy. Adequate bowel preparation is essential to guarantee a clear vision of colonic
      mucosa, complete inspection of entire colon and improves the adenoma detection rates(ADRs).
      Therefore, the quality of bowel preparation should be evaluated accurately and objectively.
      However, current bowel preparation quality scales depend on memories and subjective decision
      of endoscopists. Recently, articifial intelligence system based on deep learning algorithm
      has been used widely in medical fields. But, few studies have been reported to evaluate the
      performance of AI system based on deep learning in bowel preparation quality scoring. This
      study aims to develop and train an AI system to assess bowel preparation quality using the
      BBPS, and validate the AI system to improve the quality of colonoscopy.
allocation: N/A
intervention model: Single Group Assignment
primary purpose: Health Services Research
masking: None (Open Label)
measure: accuracy of bowel preparation score
time frame: 6 months
description: Bowel preparation quality was measured by BBPS. After fully washing or suctioning of colonic contents, three segments including right colon (containing cecum and ascending colon), transvers colon (containing hepatic and splenic flexures) and left colon (containing descending and sigmoid colon) were individually scored from 0 to 3. Point 0 refers to unprepared colon segment with obscured solid stool making mucosa cannot be seen; Point 1 refers to part of mucosa can be seen, but some areas are covered by staining, residual stool, and/or opaque liquid; Point 2 refers to entire mucosa is well-seen; Point 3 refers to clean colon segment without staining, fecal materials or liquids. A sub-score of each colon segment was used, ranging from minimum 0 to maximum 3. The highest score means the excellent bowel preparation. Adequate bowel preparation was defined as a total BBPS≥6 and sub-BBPS≥2 per segment.
arm group label: Artificial Intelligence assisted Scoring Group
arm group type: Experimental
description: Patients in this group go through colonoscopy under the AI monitoring device.
intervention type: Device
intervention name: AI
description: After taking standard bowel preparation regimen, patients receive colonoscopy under the AI system device. During the withdrawal phase, bowel preparation quality is assessed by AI-associated scoring system. The withdrawal time is targeted at least 6min. Every detected polyp will be removed and obtained for pathological assessment.
arm group label: Artificial Intelligence assisted Scoring Group
criteria: 
gender: All
minimum age: 20 Years
maximum age: 80 Years
healthy volunteers: No
facility: 
country: Korea, Republic of
responsible party type: Principal Investigator
investigator affiliation: National Cancer Center, Korea
investigator full name: Dae Kyung Sohn
investigator title: Staff surgeon
mesh term: Colonic Diseases
sharing ipd: No