<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/NCT03234725</url>
  </required_header>
  <id_info>
    <org_study_id>Deep001</org_study_id>
    <nct_id>NCT03234725</nct_id>
  </id_info>
  <brief_title>Analysis of New Endoscopic Features and Variable Stiffness in Colonoscopy: Prospective Randomised Trial</brief_title>
  <acronym>ELUFIBLI</acronym>
  <official_title>Prospective Randomised Trial to Analyse the Advantages of the New Virtual Chromoendoscopy Features and the Variable Stiffness in Connection With Our Colonoscopic Examinations</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Bács-Kiskun County Teaching Hospital</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
    <collaborator>
      <agency>Endo-Kapszula Privat Medical Center</agency>
      <agency_class>Other</agency_class>
    </collaborator>
  </sponsors>
  <source>Bács-Kiskun County Teaching Hospital</source>
  <oversight_info>
    <has_dmc>Yes</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 aim of the present study is to develop and evaluate a computer-based methods for&#xD;
      automated and improved detection and classification of different colorectal lesions,&#xD;
      especially polyps. For this purpose first, pit pattern and vascularization features of up to&#xD;
      1000 polyps with a size of 10 mm or smaller will be detected and stored in our web based&#xD;
      picture database made by a zoom BLI colonoscopy. These polyps are going to be imaged and&#xD;
      subsequently removed for histological analysis. The polyp images are analyzed by a newly&#xD;
      developed deep learning computer algorithm. The results of the deep learning automatic&#xD;
      classification (sensitivity, specificity, negative predictive value, positive predictive&#xD;
      value and accuracy) are compared to those of human observers, who were blinded to the&#xD;
      histological gold standard.&#xD;
&#xD;
      In a second approach we are planning to use LCI of the colon, rather than the usual white&#xD;
      light. Here, we will determine, whether this technique could improve the detection of flat&#xD;
      neoplastic lesions, laterally spreading tumors, small pedunculated adenomas and serrated&#xD;
      polyps. The polyps are called serrated because of their appearance under the microscope after&#xD;
      they have been removed. They tend to be located up high in the colon, far away from the&#xD;
      rectum. They have been definitely shown to be a type of precancerous polyp and it is possible&#xD;
      that using LCI will make it easier to see them, as they can be quite difficult to see with&#xD;
      standard white light.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      Computer-based Classification and Differentiation of Colorectal Polyps Using Blue Light&#xD;
      Imaging (BLI)&#xD;
&#xD;
      Purpose&#xD;
&#xD;
      Recent studies have shown that optical chromoendoscopy with narrow-band imaging (NBI) of Fuji&#xD;
      Intelligent Color Enhancement (FICE) is a powerful diagnostic tool for the differentiation&#xD;
      between neoplastic and non-neoplastic colorectal polyps. Linked color imaging (LCI) and blue&#xD;
      laser imaging (BLI) are two new imaging systems used in endoscopy which are recently&#xD;
      developed. BLI was developed to compensate for the limitations of NBI. BLI shows a bright&#xD;
      image of the digestive mucosa, enabling the detailed visualization of both the microstructure&#xD;
      and microvasculature. The ELUXEO™ endoscopic system powered by Fujifilm's unique 4-LED&#xD;
      (light-emitting diode) Multi Light™ technology sets a new standard in light intensity and&#xD;
      electronic chromoendoscopy imaging. By combining four different wavelengths and the specific&#xD;
      application of intensified from light spectra created by the integrated light source, this&#xD;
      technology allows to easily switch between the three imaging modes White Light, Blue Light&#xD;
      Imaging (BLI) and Linked Colour Imaging (LCI). Blue light imaging (BLI) is a new system for&#xD;
      image-enhanced electronic chromoendoscopy, since the 410 nm LED visualizes vascular&#xD;
      microarchitecture, similar to narrow band imaging, and a 450 nm provides white light by&#xD;
      excitation. According to three recently published reports, the diagnostic ability of polyp&#xD;
      characterization using blue light imaging compares favorably with narrow band imaging. No&#xD;
      published data are available to date regarding computer assisted polyp characterization with&#xD;
      blue light imaging.&#xD;
&#xD;
      The aim of the present study is to develop and evaluate a computer-based method for automated&#xD;
      classification of small colorectal polyps on the basis of pit pattern and vascularization&#xD;
      features. In this prospective study up to 1000 polyps with a size of 10 mm or smaller should&#xD;
      be detected and stored in our web based picture database made by a zoom BLI colonoscopy.&#xD;
      These polyps were imaged and subsequently removed for histological analysis. The polyp images&#xD;
      were analyzed by a newly developed deep learning computer algorithm. The proposed&#xD;
      computer-based method consists of several steps: picture annotation, preprocessing, vessel&#xD;
      segmentation, feature extraction and classification, parameterization, and finally train and&#xD;
      test of the multiple neural layer algorithms. The results of the deep learning automatic&#xD;
      classification (sensitivity, specificity, negative predictive value, positive predictive&#xD;
      value and accuracy) were compared to those of human observers, who were blinded to the&#xD;
      histological gold standard.&#xD;
&#xD;
      Condition Colorectal Polyps with a size less then 10 mm&#xD;
&#xD;
      Study Type:&#xD;
&#xD;
      Observational&#xD;
&#xD;
      Study Design:&#xD;
&#xD;
      Observational Model: Cohort Time Perspective: Prospective&#xD;
&#xD;
      Official Title:&#xD;
&#xD;
      Computer-based Classification and Differentiation of Colorectal Polyps Using Fujifilm Blue&#xD;
      Light Imaging (BLI)&#xD;
&#xD;
      Linked color imaging (LCI) and magnifying blue laser imaging (BLI) are two new imaging&#xD;
      systems used in endoscopy which are recently developed. The newly developed LCI system&#xD;
      (FUJIFILM Co.) creates clear and bright endoscopic images by using short-wavelength&#xD;
      narrow-band laser light combined with white laser light on the basis of BLI technology. LCI&#xD;
      makes red areas appear redder and white areas appear whiter. Thus, it is easier to recognize&#xD;
      a slight difference in color of the mucosa. The aim the present study to determine if using&#xD;
      LCI of the colon, rather than the usual white light on the colon, will improve the detection&#xD;
      of flat neoplastic lesions, laterally spreading tumors, small pedunculated adenomas and&#xD;
      serrated polyps. The polyps are called serrated because of their appearance under the&#xD;
      microscope after they have been removed. They tend to be located up high in the colon, far&#xD;
      away from the rectum. They have been definitely shown to be a type of precancerous polyp and&#xD;
      it is possible that using LCI will make it easier to see them, as they can be quite difficult&#xD;
      to see with standard white light.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Completed</overall_status>
  <start_date type="Actual">October 1, 2016</start_date>
  <completion_date type="Actual">September 30, 2018</completion_date>
  <primary_completion_date type="Actual">September 30, 2018</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>diagnostic value of the computer algorithm</measure>
    <time_frame>2 years</time_frame>
    <description>diagnostic value of the computer algorithm (sensitivity, specificity, negative predictive value, positive predictive value, accuracy) [ Time Frame: 10 months ] [ Designated as safety issue: No ]</description>
  </primary_outcome>
  <primary_outcome>
    <measure>Number of detected serrated polyps</measure>
    <time_frame>2 years</time_frame>
    <description>Number of Detected Proximal Serrated lesions, flat polyps and colorectal adenomas in proximal colon</description>
  </primary_outcome>
  <primary_outcome>
    <measure>Number of detected polyps</measure>
    <time_frame>2 years</time_frame>
    <description>Quantity of total number of colorectal adenomas found in the colon during colonoscopy was recorded and compared.</description>
  </primary_outcome>
  <primary_outcome>
    <measure>the accuracy of the NICE (NBI International Colorectal Endoscopic) criteria using FICE versus BLI Eluxeo technology</measure>
    <time_frame>2 years</time_frame>
    <description>the accuracy of the NICE criteria using FICE versus BLI Eluxeo technology without optical zoom for differentiating between the non-neoplastic and neoplastic histotypes in diagnoses with high-confidence on a video-library of 120 polyps reviewed by 5 experts. 5 experts will review pictures from a web-library of subcentimetric polyps removed and histologically verified and will assess each of the three NICE criteria (colour/vascularization/surface), and classify the lesion as neoplastic or non-neoplastic with low or high confidence.</description>
  </primary_outcome>
  <primary_outcome>
    <measure>Inter-observer agreement among the 5 experts</measure>
    <time_frame>2 years</time_frame>
    <description>Inter-observer agreement among the 5 experts [ Time Frame: up to 6 months ] [ Designated as safety issue: No ] The inter-observer agreement, among the 5 experts, on the final diagnosis (neoplastic or non-neoplastic) and on each individual NICE criterion for each polyp will be determined by using K statistics.</description>
  </primary_outcome>
  <primary_outcome>
    <measure>Cecal intubation rate</measure>
    <time_frame>2 years</time_frame>
    <description>The proportion of colonoscopy procedures resulting in successful intubation of the cecum.</description>
  </primary_outcome>
  <primary_outcome>
    <measure>Propofol need for deep sedation</measure>
    <time_frame>2 years</time_frame>
    <description>The main efficacy parameter is the amount of Propofol used for deep sedation during colonoscopy, expressed as the mean for each group.</description>
  </primary_outcome>
  <secondary_outcome>
    <measure>diagnostic interobserver variability based on the computer algorithm</measure>
    <time_frame>2 years</time_frame>
    <description>diagnostic interobserver variability based on the computer algorithm</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>the accuracy of the NICE criteria using FICE versus BLI Eluxeo technology with 50x optical zoom for differentiating between the non-neoplastic and neoplastic histotypes</measure>
    <time_frame>2 years</time_frame>
    <description>the accuracy of the NICE criteria using FICE versus BLI Eluxeo technology with 50x optical zoom for differentiating between the non-neoplastic and neoplastic histotypes in diagnoses with high-confidence on a video-library of 120 polyps reviewed by 5 experts (ML, SZM, OL, SZA, DZS)5 experts will review pictures from a web-library of subcentimetric polyps removed and histologically verified and will assess each of the three NICE criteria (colour/vascularization/surface), and classify the lesion as neoplastic or non-neoplastic with low or high confidence.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Comparison of accuracy of BLI and LCI pictures</measure>
    <time_frame>2 years</time_frame>
    <description>Comparison of accuracy of BLI and LCI pictures with and without zoom on the final diagnosis (neoplastic or non-neoplastic polyp) as compared to histology</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Improvement of adenoma detection rate by using LCI imaging comparing with that under white endoscopy</measure>
    <time_frame>2 years</time_frame>
    <description>Improvement of adenoma detection rate by using LCI imaging comparing with that under white endoscopy</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Time-to-cecum</measure>
    <time_frame>2 years</time_frame>
    <description>Time from initial insertion of colonoscope until successful intubation of the cecum (min)</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Ancillary maneuvers to facilitate procedure</measure>
    <time_frame>2 years</time_frame>
    <description>A number of added maneuvers, including abdominal pressure, repositioning of the patient, endoscope loop reduction techniques, used to facilitate advancement of the endoscope during the procedure.</description>
  </secondary_outcome>
  <number_of_groups>2</number_of_groups>
  <enrollment type="Actual">1000</enrollment>
  <condition>Colorectal Adenoma</condition>
  <condition>Colorectal Adenomatous Polyp</condition>
  <arm_group>
    <arm_group_label>LCI out group</arm_group_label>
    <description>The whitdrawal of the colonoscop happen in LCI mode.</description>
  </arm_group>
  <arm_group>
    <arm_group_label>WL (white light) out group</arm_group_label>
    <description>The whitdrawal of the colonoscop happen in WL mode.</description>
  </arm_group>
  <eligibility>
    <study_pop>
      <textblock>
        All adult patients who did not meet the exclusion criteria were included in the study&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  The patient must sign, understand and provide written consent for the procedure.&#xD;
&#xD;
          -  Undergoing colonoscopy at our endoscopy unit for any indication in Propofol deep&#xD;
             sedation&#xD;
&#xD;
          -  Intact colon and rectum&#xD;
&#xD;
          -  ASA (American Society of Anesthesiology) risk class 1, 2 or 3&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  Patients with inflammatory bowel disease;&#xD;
&#xD;
          -  Patients with poor bowel preparation; (Boston score &lt;4)&#xD;
&#xD;
          -  Female patients with pregnancy;&#xD;
&#xD;
          -  Patients with mechanical bowel obstruction;&#xD;
&#xD;
          -  Patients with diverticulitis or toxic megacolon;&#xD;
&#xD;
          -  Patients with a history of radiation therapy to abdomen or pelvis;&#xD;
&#xD;
          -  Patients with a history of severe cardiovascular, pulmonary, liver or renal disease&#xD;
             and high ASA (&gt;3) risk of propofol sedation;&#xD;
&#xD;
          -  Personal history of coagulation disorders or use of anticoagulants;&#xD;
&#xD;
          -  Patients who are currently enrolled in another clinical investigation in which the&#xD;
             intervention might compromise the safety of the patient's participation in this study.&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>18 Years</minimum_age>
    <maximum_age>99 Years</maximum_age>
    <healthy_volunteers>No</healthy_volunteers>
  </eligibility>
  <overall_official>
    <last_name>Laszlo Madacsy, MD,pHd</last_name>
    <role>Principal Investigator</role>
    <affiliation>Bács Kiskun Coeunty Teaching Hospital</affiliation>
  </overall_official>
  <location>
    <facility>
      <name>Bács Kiskun County and Teaching Hospital</name>
      <address>
        <city>Kecskemét</city>
        <state>Nyiri Street 38</state>
        <zip>6000</zip>
        <country>Hungary</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>Hungary</country>
  </location_countries>
  <verification_date>July 2019</verification_date>
  <study_first_submitted>July 27, 2017</study_first_submitted>
  <study_first_submitted_qc>July 27, 2017</study_first_submitted_qc>
  <study_first_posted type="Actual">July 31, 2017</study_first_posted>
  <last_update_submitted>July 9, 2019</last_update_submitted>
  <last_update_submitted_qc>July 9, 2019</last_update_submitted_qc>
  <last_update_posted type="Actual">July 10, 2019</last_update_posted>
  <responsible_party>
    <responsible_party_type>Principal Investigator</responsible_party_type>
    <investigator_affiliation>Bács-Kiskun County Teaching Hospital</investigator_affiliation>
    <investigator_full_name>László Madácsy Md, PhD</investigator_full_name>
    <investigator_title>Clinical Professor</investigator_title>
  </responsible_party>
  <keyword>Blue light imaging</keyword>
  <keyword>Linked color imaging</keyword>
  <keyword>Optical histology</keyword>
  <keyword>Adenoma detection rate</keyword>
  <keyword>Variable stiffness</keyword>
  <keyword>Deep learning</keyword>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Adenoma</mesh_term>
    <mesh_term>Adenomatous Polyps</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/NCT03234725
org study id: Deep001
nct id: NCT03234725
lead sponsor: 
collaborator: 
has dmc: Yes
is fda regulated drug: No
is fda regulated device: No
The aim of the present study is to develop and evaluate a computer-based methods for
      automated and improved detection and classification of different colorectal lesions,
      especially polyps. For this purpose first, pit pattern and vascularization features of up to
      1000 polyps with a size of 10 mm or smaller will be detected and stored in our web based
      picture database made by a zoom BLI colonoscopy. These polyps are going to be imaged and
      subsequently removed for histological analysis. The polyp images are analyzed by a newly
      developed deep learning computer algorithm. The results of the deep learning automatic
      classification (sensitivity, specificity, negative predictive value, positive predictive
      value and accuracy) are compared to those of human observers, who were blinded to the
      histological gold standard.

      In a second approach we are planning to use LCI of the colon, rather than the usual white
      light. Here, we will determine, whether this technique could improve the detection of flat
      neoplastic lesions, laterally spreading tumors, small pedunculated adenomas and serrated
      polyps. The polyps are called serrated because of their appearance under the microscope after
      they have been removed. They tend to be located up high in the colon, far away from the
      rectum. They have been definitely shown to be a type of precancerous polyp and it is possible
      that using LCI will make it easier to see them, as they can be quite difficult to see with
      standard white light.
Computer-based Classification and Differentiation of Colorectal Polyps Using Blue Light
      Imaging (BLI)

      Purpose

      Recent studies have shown that optical chromoendoscopy with narrow-band imaging (NBI) of Fuji
      Intelligent Color Enhancement (FICE) is a powerful diagnostic tool for the differentiation
      between neoplastic and non-neoplastic colorectal polyps. Linked color imaging (LCI) and blue
      laser imaging (BLI) are two new imaging systems used in endoscopy which are recently
      developed. BLI was developed to compensate for the limitations of NBI. BLI shows a bright
      image of the digestive mucosa, enabling the detailed visualization of both the microstructure
      and microvasculature. The ELUXEO™ endoscopic system powered by Fujifilm's unique 4-LED
      (light-emitting diode) Multi Light™ technology sets a new standard in light intensity and
      electronic chromoendoscopy imaging. By combining four different wavelengths and the specific
      application of intensified from light spectra created by the integrated light source, this
      technology allows to easily switch between the three imaging modes White Light, Blue Light
      Imaging (BLI) and Linked Colour Imaging (LCI). Blue light imaging (BLI) is a new system for
      image-enhanced electronic chromoendoscopy, since the 410 nm LED visualizes vascular
      microarchitecture, similar to narrow band imaging, and a 450 nm provides white light by
      excitation. According to three recently published reports, the diagnostic ability of polyp
      characterization using blue light imaging compares favorably with narrow band imaging. No
      published data are available to date regarding computer assisted polyp characterization with
      blue light imaging.

      The aim of the present study is to develop and evaluate a computer-based method for automated
      classification of small colorectal polyps on the basis of pit pattern and vascularization
      features. In this prospective study up to 1000 polyps with a size of 10 mm or smaller should
      be detected and stored in our web based picture database made by a zoom BLI colonoscopy.
      These polyps were imaged and subsequently removed for histological analysis. The polyp images
      were analyzed by a newly developed deep learning computer algorithm. The proposed
      computer-based method consists of several steps: picture annotation, preprocessing, vessel
      segmentation, feature extraction and classification, parameterization, and finally train and
      test of the multiple neural layer algorithms. The results of the deep learning automatic
      classification (sensitivity, specificity, negative predictive value, positive predictive
      value and accuracy) were compared to those of human observers, who were blinded to the
      histological gold standard.

      Condition Colorectal Polyps with a size less then 10 mm

      Study Type:

      Observational

      Study Design:

      Observational Model: Cohort Time Perspective: Prospective

      Official Title:

      Computer-based Classification and Differentiation of Colorectal Polyps Using Fujifilm Blue
      Light Imaging (BLI)

      Linked color imaging (LCI) and magnifying blue laser imaging (BLI) are two new imaging
      systems used in endoscopy which are recently developed. The newly developed LCI system
      (FUJIFILM Co.) creates clear and bright endoscopic images by using short-wavelength
      narrow-band laser light combined with white laser light on the basis of BLI technology. LCI
      makes red areas appear redder and white areas appear whiter. Thus, it is easier to recognize
      a slight difference in color of the mucosa. The aim the present study to determine if using
      LCI of the colon, rather than the usual white light on the colon, will improve the detection
      of flat neoplastic lesions, laterally spreading tumors, small pedunculated adenomas and
      serrated polyps. The polyps are called serrated because of their appearance under the
      microscope after they have been removed. They tend to be located up high in the colon, far
      away from the rectum. They have been definitely shown to be a type of precancerous polyp and
      it is possible that using LCI will make it easier to see them, as they can be quite difficult
      to see with standard white light.
observational model: Cohort
time perspective: Prospective
measure: diagnostic value of the computer algorithm
time frame: 2 years
description: diagnostic value of the computer algorithm (sensitivity, specificity, negative predictive value, positive predictive value, accuracy) [ Time Frame: 10 months ] [ Designated as safety issue: No ]
measure: Number of detected serrated polyps
time frame: 2 years
description: Number of Detected Proximal Serrated lesions, flat polyps and colorectal adenomas in proximal colon
measure: Number of detected polyps
time frame: 2 years
description: Quantity of total number of colorectal adenomas found in the colon during colonoscopy was recorded and compared.
measure: the accuracy of the NICE (NBI International Colorectal Endoscopic) criteria using FICE versus BLI Eluxeo technology
time frame: 2 years
description: the accuracy of the NICE criteria using FICE versus BLI Eluxeo technology without optical zoom for differentiating between the non-neoplastic and neoplastic histotypes in diagnoses with high-confidence on a video-library of 120 polyps reviewed by 5 experts. 5 experts will review pictures from a web-library of subcentimetric polyps removed and histologically verified and will assess each of the three NICE criteria (colour/vascularization/surface), and classify the lesion as neoplastic or non-neoplastic with low or high confidence.
measure: Inter-observer agreement among the 5 experts
time frame: 2 years
description: Inter-observer agreement among the 5 experts [ Time Frame: up to 6 months ] [ Designated as safety issue: No ] The inter-observer agreement, among the 5 experts, on the final diagnosis (neoplastic or non-neoplastic) and on each individual NICE criterion for each polyp will be determined by using K statistics.
measure: Cecal intubation rate
time frame: 2 years
description: The proportion of colonoscopy procedures resulting in successful intubation of the cecum.
measure: Propofol need for deep sedation
time frame: 2 years
description: The main efficacy parameter is the amount of Propofol used for deep sedation during colonoscopy, expressed as the mean for each group.
measure: diagnostic interobserver variability based on the computer algorithm
time frame: 2 years
description: diagnostic interobserver variability based on the computer algorithm
measure: the accuracy of the NICE criteria using FICE versus BLI Eluxeo technology with 50x optical zoom for differentiating between the non-neoplastic and neoplastic histotypes
time frame: 2 years
description: the accuracy of the NICE criteria using FICE versus BLI Eluxeo technology with 50x optical zoom for differentiating between the non-neoplastic and neoplastic histotypes in diagnoses with high-confidence on a video-library of 120 polyps reviewed by 5 experts (ML, SZM, OL, SZA, DZS)5 experts will review pictures from a web-library of subcentimetric polyps removed and histologically verified and will assess each of the three NICE criteria (colour/vascularization/surface), and classify the lesion as neoplastic or non-neoplastic with low or high confidence.
measure: Comparison of accuracy of BLI and LCI pictures
time frame: 2 years
description: Comparison of accuracy of BLI and LCI pictures with and without zoom on the final diagnosis (neoplastic or non-neoplastic polyp) as compared to histology
measure: Improvement of adenoma detection rate by using LCI imaging comparing with that under white endoscopy
time frame: 2 years
description: Improvement of adenoma detection rate by using LCI imaging comparing with that under white endoscopy
measure: Time-to-cecum
time frame: 2 years
description: Time from initial insertion of colonoscope until successful intubation of the cecum (min)
measure: Ancillary maneuvers to facilitate procedure
time frame: 2 years
description: A number of added maneuvers, including abdominal pressure, repositioning of the patient, endoscope loop reduction techniques, used to facilitate advancement of the endoscope during the procedure.
arm group label: LCI out group
description: The whitdrawal of the colonoscop happen in LCI mode.
arm group label: WL (white light) out group
description: The whitdrawal of the colonoscop happen in WL mode.
study pop: 
sampling method: Probability Sample
criteria: 
gender: All
minimum age: 18 Years
maximum age: 99 Years
healthy volunteers: No
last name: Laszlo Madacsy, MD,pHd
role: Principal Investigator
affiliation: Bács Kiskun Coeunty Teaching Hospital
facility: 
country: Hungary
responsible party type: Principal Investigator
investigator affiliation: Bács-Kiskun County Teaching Hospital
investigator full name: László Madácsy Md, PhD
investigator title: Clinical Professor
mesh term: Adenoma
mesh term: Adenomatous Polyps
sharing ipd: No