<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/NCT04425941</url>
  </required_header>
  <id_info>
    <org_study_id>PetzACTHospital</org_study_id>
    <nct_id>NCT04425941</nct_id>
  </id_info>
  <brief_title>Polyp Artificial Intelligence Study</brief_title>
  <official_title>Artificial Intelligence Based Colorectal Polyp Histology Prediction by Using Narrow-band Imaging Magnifying Colonoscopy</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Petz Aladar County Teaching Hospital</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
  </sponsors>
  <source>Petz Aladar County Teaching Hospital</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>
      Background We are developing artificial intelligence based polyp histology prediction (AIPHP)&#xD;
      method to automatically classify Narrow Band Imaging (NBI) magnifying colonoscopy images to&#xD;
      predict the non-neoplastic or neoplastic histology of polyps.&#xD;
&#xD;
      Aim Our aim was to analyse the accuracy of AIPHP and NICE classification based histology&#xD;
      predictions and also to compare the results of the two methods.&#xD;
&#xD;
      Methods We examined colorectal polyps obtained from colonoscopy patients who had polypectomy&#xD;
      or endoscopic mucosectomy. Polyps detected by white light colonoscopy were observed then by&#xD;
      using NBI at the optical maximum magnificent (60x). The obtained and stored NBI magnifying&#xD;
      images were analysed by NICE classification and by AIPHP method parallelly. Pathology&#xD;
      examinations were performed blinded to the NICE and AIPHP diagnosis, as well. Our AIPHP&#xD;
      software is based on a machine learning method. This program measures five geometrical and&#xD;
      colour features on the endoscopic image.&#xD;
    </textblock>
  </brief_summary>
  <overall_status>Completed</overall_status>
  <start_date type="Actual">January 5, 2014</start_date>
  <completion_date type="Actual">May 31, 2020</completion_date>
  <primary_completion_date type="Actual">May 31, 2020</primary_completion_date>
  <study_type>Observational</study_type>
  <has_expanded_access>No</has_expanded_access>
  <study_design_info>
    <observational_model>Cohort</observational_model>
    <time_perspective>Prospective</time_perspective>
  </study_design_info>
  <primary_outcome>
    <measure>Software accuracy of polyp histology prediction</measure>
    <time_frame>2014-2020</time_frame>
    <description>Artificial intelligence software diagnosis in comparison with the polyp histology</description>
  </primary_outcome>
  <enrollment type="Actual">373</enrollment>
  <condition>Software Analysis on Polyp Histology Prediction</condition>
  <intervention>
    <intervention_type>Other</intervention_type>
    <intervention_name>artificial intelligence diagnosis</intervention_name>
    <description>artificial intelligence prediction of colorectal polyp histology</description>
  </intervention>
  <eligibility>
    <study_pop>
      <textblock>
        patients with symptoms of colorectal polypoid laesions, colorectal cancer screening&#xD;
        patients&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Non-Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  endoscopic diagnosis of colorectal polyp&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  colonoscopy result without polyps or IBD diagnosis&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>18 Years</minimum_age>
    <maximum_age>90 Years</maximum_age>
    <healthy_volunteers>No</healthy_volunteers>
  </eligibility>
  <verification_date>June 2020</verification_date>
  <study_first_submitted>June 5, 2020</study_first_submitted>
  <study_first_submitted_qc>June 9, 2020</study_first_submitted_qc>
  <study_first_posted type="Actual">June 11, 2020</study_first_posted>
  <last_update_submitted>June 9, 2020</last_update_submitted>
  <last_update_submitted_qc>June 9, 2020</last_update_submitted_qc>
  <last_update_posted type="Actual">June 11, 2020</last_update_posted>
  <responsible_party>
    <responsible_party_type>Sponsor</responsible_party_type>
  </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/NCT04425941
org study id: PetzACTHospital
nct id: NCT04425941
lead sponsor: 
has dmc: No
is fda regulated drug: No
is fda regulated device: No
Background We are developing artificial intelligence based polyp histology prediction (AIPHP)
      method to automatically classify Narrow Band Imaging (NBI) magnifying colonoscopy images to
      predict the non-neoplastic or neoplastic histology of polyps.

      Aim Our aim was to analyse the accuracy of AIPHP and NICE classification based histology
      predictions and also to compare the results of the two methods.

      Methods We examined colorectal polyps obtained from colonoscopy patients who had polypectomy
      or endoscopic mucosectomy. Polyps detected by white light colonoscopy were observed then by
      using NBI at the optical maximum magnificent (60x). The obtained and stored NBI magnifying
      images were analysed by NICE classification and by AIPHP method parallelly. Pathology
      examinations were performed blinded to the NICE and AIPHP diagnosis, as well. Our AIPHP
      software is based on a machine learning method. This program measures five geometrical and
      colour features on the endoscopic image.
observational model: Cohort
time perspective: Prospective
measure: Software accuracy of polyp histology prediction
time frame: 2014-2020
description: Artificial intelligence software diagnosis in comparison with the polyp histology
intervention type: Other
intervention name: artificial intelligence diagnosis
description: artificial intelligence prediction of colorectal polyp histology
study pop: 
sampling method: Non-Probability Sample
criteria: 
gender: All
minimum age: 18 Years
maximum age: 90 Years
healthy volunteers: No
responsible party type: Sponsor
mesh term: Polyps