Impact of Automatic Polyp Detection System on Adenoma Detection Rate
Impact of Automatic Polyp Detection System on Adenoma Detection Rate-a Multicenter,Prospective, Randomized Controlled Trial
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
Study Type
Study Type
Enrollment (Anticipated)
Enrollment
Phase
Phase
- Not Applicable
Contacts and Locations
Study Contact
Study Contact
- Name: Zhaoshen Li, M.D
- Phone Number: 86-21-31161365
- Email: li.zhaoshen@hotmail.com
Study Contact Backup
- Name: Yu Bai, M.D
- Phone Number: 86-21-31161335
- Email: baiyu1998@hotmail.com
Study Locations
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Shanghai, China, 200433
- Recruiting
- Changhai Hospital, Second Military Medical University
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Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
Accepts Healthy Volunteers
Genders Eligible for Study
Description
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
Study Plan
How is the study designed?
Design Details
- Primary Purpose: DIAGNOSTIC
- Allocation: RANDOMIZED
- Interventional Model: PARALLEL
- Masking: NONE
Number of Arms
Arms and Interventions
Participant Group / ArmParticipant Group / Arm |
Intervention / TreatmentIntervention / Treatment |
|---|---|
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EXPERIMENTAL: AI-assisted withdrawal group
A deep learning-based automatic polyp detection system was used to assist the endoscopist.
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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.
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NO_INTERVENTION: Routine withdrawal group
Routine withdrawal without any assist.
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What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
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adenoma detection rate(ADR)
Time Frame: 30 minutes
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the number of patients with at least one adenoma divided by the total number of patients.
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30 minutes
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Secondary Outcome Measures
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
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polyp detection rate(PDR)
Time Frame: 30 minutes
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the number of patients with at least one polyp divided by the total number of patients.
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30 minutes
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adenoma per colonoscopy
Time Frame: 30 minutes
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the number of adenomas detected during colonoscopy withdraw divided by the number of colonoscopies.
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30 minutes
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polyp per colonoscopy
Time Frame: 30 minutes
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the number of polyps detected during colonoscopy withdraw divided by the number of colonoscopies.
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30 minutes
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Collaborators and Investigators
Sponsor
Sponsor
Collaborators
Collaborators
Publications and helpful links
General 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.
Study record dates
Study Major Dates
Study Start (ACTUAL)
Study Start
Primary Completion (ANTICIPATED)
Primary Completion
Study Completion (ANTICIPATED)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (ACTUAL)
First Posted
Study Record Updates
Last Update Posted (ACTUAL)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
Other Study ID Numbers
- AI-2
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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