Back to resultsEffect of Two Colonoscopy AI Systems for Colon Polyp Detection
Primary Purpose
Adenoma, Colonoscopy, Sessile Serrated Adenoma
Status
Completed
Phase
Not Applicable
Locations
Korea, Republic of
Study Type
Interventional
Intervention
Assist by artificial intelligence system for colon polyp detection
Sponsored by
About this trial
This is an interventional diagnostic trial for Adenoma focused on measuring endoscopist, adenoma detection rate, colonoscopy, artificial intelligence
Eligibility Criteria
Inclusion Criteria:
patient for screening or surveillance colonoscopy patients agreed with participating in the study
Exclusion Criteria:
patients who do not agree with participating in the study patients with a history of colon resection patients with a history of inflammatory bowel resection patients with poor bowel preparation
Sites / Locations
- Healthcare System Gangnam Center, Seoul National University Hospital
Arms of the Study
Arm 1
Arm 2
Arm Type
Experimental
No Intervention
Arm Label
CADe group
Control
Arm Description
Endoscopists perform colonoscopy with CADe system
Endoscopists perform colonoscopy without CADe system
Outcomes
Primary Outcome Measures
Adenoma detection rate
proportion of colonoscopies with at least one adenoma detected overall and as detected by the physician.
Sessile serrated lesion detection rate
proportion of colonoscopies with at least one sessile serrated lesion detected overall and as detected by the physician.
Secondary Outcome Measures
polyp detection rate
proportion of colonoscopies with at least one polyp detected overall and as detected by the physician.
Full Information
NCT ID
NCT05089071
First Posted
October 11, 2021
Last Updated
July 25, 2023
Sponsor
Seoul National University Hospital
Collaborators
Seoul National University
1. Study Identification
Unique Protocol Identification Number
NCT05089071
Brief Title
Effect of Two Colonoscopy AI Systems for Colon Polyp Detection
Official Title
Effect of Two Colonoscopy AI Systems for Colon Polyp Detection According to the False Positive Rates of the Systems: A Single-center Prospective Study
Study Type
Interventional
2. Study Status
Record Verification Date
March 2022
Overall Recruitment Status
Completed
Study Start Date
November 1, 2021 (Actual)
Primary Completion Date
October 31, 2022 (Actual)
Study Completion Date
December 31, 2022 (Actual)
3. Sponsor/Collaborators
Responsible Party, by Official Title
Sponsor
Name of the Sponsor
Seoul National University Hospital
Collaborators
Seoul National University
4. Oversight
Studies a U.S. FDA-regulated Drug Product
No
Studies a U.S. FDA-regulated Device Product
No
Data Monitoring Committee
No
5. Study Description
Brief Summary
Computer-aided detection (CADe) systems have been actively researched for polyp detection in colonoscopy. The investigators aim to identify the effect of two CADe systems according to the system performance on false positive rate
Detailed Description
Artificial intelligence technology based on deep learning is being applied in various medical fields, and research is being actively conducted to develop computer-aided detection (CADe) systems for colonoscopies to overcome the limitation of the variance of human skills. These well-trained CADe systems demonstrated high performance for neoplastic polyp detection and reported a 44% increase in adenoma detection rate (ADR) for endoscopists. However, the level of performance in the CADe system is not clear for expert endoscopists to be useful for ADR increase.
Furthermore, false positives(FPs) of the CADe system may negatively influence ADR during a screening colonoscopy. Accordingly, the investigators sought to identify the effect of the colonoscopy CADe system according to FP performance in endoscopists with various levels. The investigators hypothesized that the CADe system with low FPs would be useful to prevent the decrease in ADR in case of a high endoscopy workload according to the performance of CADe systems.
6. Conditions and Keywords
Primary Disease or Condition Being Studied in the Trial, or the Focus of the Study
Adenoma, Colonoscopy, Sessile Serrated Adenoma
Keywords
endoscopist, adenoma detection rate, colonoscopy, artificial intelligence
7. Study Design
Primary Purpose
Diagnostic
Study Phase
Not Applicable
Interventional Study Model
Parallel Assignment
Model Description
The primary outcome was the comparison of ADR between the control and AI groups according to the intervention system (SCAI vs ENAD system).
Masking
Outcomes Assessor
Allocation
Non-Randomized
Enrollment
3046 (Actual)
8. Arms, Groups, and Interventions
Arm Title
CADe group
Arm Type
Experimental
Arm Description
Endoscopists perform colonoscopy with CADe system
Arm Title
Control
Arm Type
No Intervention
Arm Description
Endoscopists perform colonoscopy without CADe system
Intervention Type
Device
Intervention Name(s)
Assist by artificial intelligence system for colon polyp detection
Intervention Description
Assist by artificial intelligence system for colon polyp detection
Primary Outcome Measure Information:
Title
Adenoma detection rate
Description
proportion of colonoscopies with at least one adenoma detected overall and as detected by the physician.
Time Frame
12 months
Title
Sessile serrated lesion detection rate
Description
proportion of colonoscopies with at least one sessile serrated lesion detected overall and as detected by the physician.
Time Frame
12 months
Secondary Outcome Measure Information:
Title
polyp detection rate
Description
proportion of colonoscopies with at least one polyp detected overall and as detected by the physician.
Time Frame
12 months
10. Eligibility
Sex
All
Minimum Age & Unit of Time
45 Years
Maximum Age & Unit of Time
100 Years
Accepts Healthy Volunteers
Accepts Healthy Volunteers
Eligibility Criteria
Inclusion Criteria:
patient for screening or surveillance colonoscopy patients agreed with participating in the study
Exclusion Criteria:
patients who do not agree with participating in the study patients with a history of colon resection patients with a history of inflammatory bowel resection patients with poor bowel preparation
Overall Study Officials:
First Name & Middle Initial & Last Name & Degree
Jung Ho Bae, MD
Organizational Affiliation
Seoul National University Hospital
Official's Role
Principal Investigator
Facility Information:
Facility Name
Healthcare System Gangnam Center, Seoul National University Hospital
City
Seoul
Country
Korea, Republic of
12. IPD Sharing Statement
Plan to Share IPD
No
Citations:
PubMed Identifier
32598963
Citation
Hassan C, Spadaccini M, Iannone A, Maselli R, Jovani M, Chandrasekar VT, Antonelli G, Yu H, Areia M, Dinis-Ribeiro M, Bhandari P, Sharma P, Rex DK, Rosch T, Wallace M, Repici A. Performance of artificial intelligence in colonoscopy for adenoma and polyp detection: a systematic review and meta-analysis. Gastrointest Endosc. 2021 Jan;93(1):77-85.e6. doi: 10.1016/j.gie.2020.06.059. Epub 2020 Jun 26.
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Effect of Two Colonoscopy AI Systems for Colon Polyp Detection
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