Effect of Colonoscopy AI System for Colon Polyp Detection According to the Workload of Endoscopy Units

Sponsor

Seoul National University Hospital (Other)

Overall Status

Recruiting

CT.gov ID

NCT05089071

Collaborator

Seoul National University (Other)

3,000

Enrollment

Location

Arms

Anticipated Duration (Months)

214.3

Patients Per Site Per Month

Study Details

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 CADe system according to the workload of endoscopy units

Condition or Disease	Intervention/Treatment	Phase
Adenoma Colonoscopy Fatigue	Device: Assist by artificial intelligence system for colon polyp detection	N/A

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.

Operator fatigue may negatively influence ADR during a screening colonoscopy, and many studies reported a decrease in ADR according to the increase of working hours and procedure number. Accordingly, the investigators sought to identify the effect of the colonoscopy CADe system according to working hours and procedure numbers in endoscopists with various levels. The investigators hypothesized that the CADe system would be useful to prevent the decrease in ADR in case of a high endoscopy workload.

Study Design

Study Type:

Interventional

Anticipated Enrollment :

3000 participants

Allocation:

Non-Randomized

Intervention Model:

Parallel Assignment

Masking:

Single (Outcomes Assessor)

Primary Purpose:

Diagnostic

Official Title:

Effect of Colonoscopy AI System for Colon Polyp Detection According to the Workload of Endoscopy Units: A Single-center Prospective Study

Actual Study Start Date :

Oct 1, 2021

Anticipated Primary Completion Date :

May 1, 2022

Anticipated Study Completion Date :

Dec 1, 2022

Arms and Interventions

Arm	Intervention/Treatment
Experimental: CADe group Endoscopists perform colonoscopy with CADe system	Device: Assist by artificial intelligence system for colon polyp detection Assist by artificial intelligence system for colon polyp detection
No Intervention: Control Endoscopists perform colonoscopy without CADe system

Arm

Intervention/Treatment

Experimental: CADe group

Endoscopists perform colonoscopy with CADe system

Device: Assist by artificial intelligence system for colon polyp detection

Assist by artificial intelligence system for colon polyp detection

No Intervention: Control

Endoscopists perform colonoscopy without CADe system

Outcome Measures

Primary Outcome Measures

Adenoma detection rate [8 months]
proportion of colonoscopies with at least one adenoma detected overall and as detected by the physician.
Sessile serrated lesion detection rate [8 months]
proportion of colonoscopies with at least one sessile serrated lesion detected overall and as detected by the physician.

Secondary Outcome Measures

polyp detection rate [8 months]
proportion of colonoscopies with at least one polyp detected overall and as detected by the physician.

Eligibility Criteria

Criteria

Ages Eligible for Study:

18 Years to 100 Years

Sexes Eligible for Study:

All

Accepts Healthy Volunteers:

Yes

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