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出境医 / 临床实验 / Impact of Automatic Polyp Detection System on Adenoma Detection Rate

Impact of Automatic Polyp Detection System on Adenoma Detection Rate

Study Description
Brief Summary:
In recent years, with the continuous development of artificial intelligence, automatic polyp detection systems have shown its potential in increasing the colorectal lesions. Yet, whether this system can increase polyp and adenoma detection rates in the real clinical setting is still need to be proved. The primary objective of this study is to examine whether a combination of colonoscopy and a deep learning-based automatic polyp detection system is a feasible way to increase adenoma detection rate compared to standard colonoscopy.

Condition or disease Intervention/treatment Phase
Colonic Polyps Colorectal Adenomas Device: Automatic polyp detection system Not Applicable

Study Design
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Study Type : Interventional  (Clinical Trial)
Estimated Enrollment : 1118 participants
Allocation: Randomized
Intervention Model: Parallel Assignment
Masking: None (Open Label)
Primary Purpose: Diagnostic
Official Title: Impact of Automatic Polyp Detection System on Adenoma Detection Rate-a Multicenter,Prospective, Randomized Controlled Trial
Actual Study Start Date : June 1, 2019
Estimated Primary Completion Date : July 20, 2021
Estimated Study Completion Date : October 1, 2021
Arms and Interventions
Arm Intervention/treatment
Experimental: AI-assisted withdrawal group
A deep learning-based automatic polyp detection system was used to assist the endoscopist.
 Device: Automatic polyp detection system
When colonoscopists withdraw the colonoscopies and inspect the colons, the video streaming of colonoscopies was real-time switched to the automatic polyp detection system, which made it feasible to detect lesions in real time. When any potential polyp is detected by the system， there will be a tracing box on an adjacent monitor to locate the lesion with a simultaneous sound alarm.
No Intervention: Routine withdrawal group
Routine withdrawal without any assist.
Outcome Measures
Primary Outcome Measures :
  1. adenoma detection rate(ADR) [ Time Frame: 30 minutes ]
    the number of patients with at least one adenoma divided by the total number of patients.


Secondary Outcome Measures :
  1. polyp detection rate(PDR) [ Time Frame: 30 minutes ]
    the number of patients with at least one polyp divided by the total number of patients.

  2. adenoma per colonoscopy [ Time Frame: 30 minutes ]
    the number of adenomas detected during colonoscopy withdraw divided by the number of colonoscopies.

  3. polyp per colonoscopy [ Time Frame: 30 minutes ]
    the number of polyps detected during colonoscopy withdraw divided by the number of colonoscopies.


Eligibility Criteria
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Ages Eligible for Study:   40 Years to 85 Years   (Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
Criteria

Inclusion Criteria:

  • Patients aged between 40-85 years old who have indications for screening, surveillance and diagnostic.
  • Patients who have signed inform consent form.

Exclusion Criteria:

  • Patients who have undergone colonic resection
  • Patients with intracranial and/or central nervous system disease, including cerebral infarction and cerebral hemorrhage.
  • Patients with severe chronic cardiopulmonary and renal disease.
  • Patients who are unwilling or unable to consent.
  • Patients who are not suitable for colonoscopy
  • Patients who received urgent or therapeutic colonoscopy
  • Patients with pregnancy, inflammatory bowel disease, polyposis of colon, colorectal cancer, or intestinal obstruction
  • Patients who are taking aspirin, clopidogrel or other anticoagulants
  • Patients with withdrawal time < 6 min
Contacts and Locations

Contacts
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Contact: Zhaoshen Li, M.D 86-21-31161365 li.zhaoshen@hotmail.com
Contact: Yu Bai, M.D 86-21-31161335 baiyu1998@hotmail.com

Locations
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China
Changhai Hospital, Second Military Medical University Recruiting
Shanghai, China, 200433
Contact: zhaoshen Li, MD    86-21-81873241    zhaoshenlismmu@gmail.com   
Principal Investigator: Zhaoshen Li, MD         
Sponsors and Collaborators
Changhai Hospital
The First Affiliated Hospital of Dalian Medical University
Wenzhou Central Hospital
Wuhan Union Hospital, China
Investigators
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Principal Investigator: Zhaoshen Li, M.D Changhai Hospital
Tracking Information
First Submitted Date  ICMJE May 28, 2019
First Posted Date  ICMJE May 30, 2019
Last Update Posted Date April 6, 2021
Actual Study Start Date  ICMJE June 1, 2019
Estimated Primary Completion Date July 20, 2021   (Final data collection date for primary outcome measure)
Current Primary Outcome Measures  ICMJE
 (submitted: June 20, 2019) 		adenoma detection rate(ADR) [ Time Frame: 30 minutes ]
the number of patients with at least one adenoma divided by the total number of patients.
Original Primary Outcome Measures  ICMJE
 (submitted: May 28, 2019) 		adenoma detection rate(ADR) [ Time Frame: 30min ]
the number of patients with at least one adenoma divided by the total number of patients.
Change History
Current Secondary Outcome Measures  ICMJE
 (submitted: June 20, 2019)
  • polyp detection rate(PDR) [ Time Frame: 30 minutes ]
    the number of patients with at least one polyp divided by the total number of patients.
  • adenoma per colonoscopy [ Time Frame: 30 minutes ]
    the number of adenomas detected during colonoscopy withdraw divided by the number of colonoscopies.
  • polyp per colonoscopy [ Time Frame: 30 minutes ]
    the number of polyps detected during colonoscopy withdraw divided by the number of colonoscopies.
Original Secondary Outcome Measures  ICMJE
 (submitted: May 28, 2019)
  • polyp detection rate(PDR) [ Time Frame: 30min ]
    the number of patients with at least one polyp divided by the total number of patients.
  • adenoma per colonoscopy [ Time Frame: 30min ]
    the number of adenomas detected during colonoscopy withdraw divided by the number of colonoscopies.
  • polyp per colonoscopy [ Time Frame: 30min ]
    the number of polyps detected during colonoscopy withdraw divided by the number of colonoscopies.
Current Other Pre-specified Outcome Measures Not Provided
Original Other Pre-specified Outcome Measures Not Provided
 
Descriptive Information
Brief Title  ICMJE Impact of Automatic Polyp Detection System on Adenoma Detection Rate
Official Title  ICMJE Impact of Automatic Polyp Detection System on Adenoma Detection Rate-a Multicenter,Prospective, Randomized Controlled Trial
Brief Summary In recent years, with the continuous development of artificial intelligence, automatic polyp detection systems have shown its potential in increasing the colorectal lesions. Yet, whether this system can increase polyp and adenoma detection rates in the real clinical setting is still need to be proved. The primary objective of this study is to examine whether a combination of colonoscopy and a deep learning-based automatic polyp detection system is a feasible way to increase adenoma detection rate compared to standard colonoscopy.
Detailed Description Not Provided
Study Type  ICMJE Interventional
Study Phase  ICMJE Not Applicable
Study Design  ICMJE Allocation: Randomized
Intervention Model: Parallel Assignment
Masking: None (Open Label)
Primary Purpose: Diagnostic
Condition  ICMJE
  • Colonic Polyps
  • Colorectal Adenomas
Intervention  ICMJE Device: Automatic polyp detection system
When colonoscopists withdraw the colonoscopies and inspect the colons, the video streaming of colonoscopies was real-time switched to the automatic polyp detection system, which made it feasible to detect lesions in real time. When any potential polyp is detected by the system， there will be a tracing box on an adjacent monitor to locate the lesion with a simultaneous sound alarm.
Study Arms  ICMJE
  • Experimental: AI-assisted withdrawal group
    A deep learning-based automatic polyp detection system was used to assist the endoscopist.
    Intervention: Device: Automatic polyp detection system
  • No Intervention: Routine withdrawal group
    Routine withdrawal without any assist.
Publications *
  • Urban G, Tripathi P, Alkayali T, Mittal M, Jalali F, Karnes W, Baldi P. Deep Learning Localizes and Identifies Polyps in Real Time With 96% Accuracy in Screening Colonoscopy. Gastroenterology. 2018 Oct;155(4):1069-1078.e8. doi: 10.1053/j.gastro.2018.06.037. Epub 2018 Jun 18.
  • Ahmad OF, Soares AS, Mazomenos E, Brandao P, Vega R, Seward E, Stoyanov D, Chand M, Lovat LB. Artificial intelligence and computer-aided diagnosis in colonoscopy: current evidence and future directions. Lancet Gastroenterol Hepatol. 2019 Jan;4(1):71-80. doi: 10.1016/S2468-1253(18)30282-6. Epub 2018 Dec 6. Review.

*   Includes publications given by the data provider as well as publications identified by ClinicalTrials.gov Identifier (NCT Number) in Medline.
 
Recruitment Information
Recruitment Status  ICMJE Recruiting
Estimated Enrollment  ICMJE
 (submitted: May 28, 2019) 		1118
Original Estimated Enrollment  ICMJE Same as current
Estimated Study Completion Date  ICMJE October 1, 2021
Estimated Primary Completion Date July 20, 2021   (Final data collection date for primary outcome measure)
Eligibility Criteria  ICMJE

Inclusion Criteria:

  • Patients aged between 40-85 years old who have indications for screening, surveillance and diagnostic.
  • Patients who have signed inform consent form.

Exclusion Criteria:

  • Patients who have undergone colonic resection
  • Patients with intracranial and/or central nervous system disease, including cerebral infarction and cerebral hemorrhage.
  • Patients with severe chronic cardiopulmonary and renal disease.
  • Patients who are unwilling or unable to consent.
  • Patients who are not suitable for colonoscopy
  • Patients who received urgent or therapeutic colonoscopy
  • Patients with pregnancy, inflammatory bowel disease, polyposis of colon, colorectal cancer, or intestinal obstruction
  • Patients who are taking aspirin, clopidogrel or other anticoagulants
  • Patients with withdrawal time < 6 min
Sex/Gender  ICMJE
Sexes Eligible for Study: All
Ages  ICMJE 40 Years to 85 Years   (Adult, Older Adult)
Accepts Healthy Volunteers  ICMJE No
Contacts  ICMJE
Contact: Zhaoshen Li, M.D 86-21-31161365 li.zhaoshen@hotmail.com
Contact: Yu Bai, M.D 86-21-31161335 baiyu1998@hotmail.com
Listed Location Countries  ICMJE China
Removed Location Countries  
 
Administrative Information
NCT Number  ICMJE NCT03967756
Other Study ID Numbers  ICMJE AI-2
Has Data Monitoring Committee Yes
U.S. FDA-regulated Product
Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
IPD Sharing Statement  ICMJE
Plan to Share IPD: No
Responsible Party Zhaoshen Li, Changhai Hospital
Study Sponsor  ICMJE Changhai Hospital
Collaborators  ICMJE
  • The First Affiliated Hospital of Dalian Medical University
  • Wenzhou Central Hospital
  • Wuhan Union Hospital, China
Investigators  ICMJE
Principal Investigator: Zhaoshen Li, M.D Changhai Hospital
PRS Account Changhai Hospital
Verification Date April 2021

ICMJE     Data element required by the International Committee of Medical Journal Editors and the World Health Organization ICTRP

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