Effect of Two Colonoscopy AI Systems for Colon Polyp Detection

Study Overview
=================
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.

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

Conditions
-----------------
Adenoma, Colonoscopy, Sessile Serrated Adenoma

Intervention / Treatment
-----------------
* Device: Assist by artificial intelligence system for colon polyp detection


Participation Criteria
=================
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

Ages Eligible for Study
-----------------
Minimum Age: 45 Years
Maximum Age: 100 Years

Sexes Eligible for Study
-----------------
All

Accepts Healthy Volunteers
-----------------
Accepts Healthy Volunteers

Study Plan
=================
How is the study designed?
-----------------

Design Details

Primary Purpose: Diagnostic
Allocation: Non-Randomized
Intervention Model: Parallel Assignment
Interventional 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: Single


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental: CADe group<br>Endoscopists perform colonoscopy with CADe system | Device: Assist by artificial intelligence system for colon polyp detection<br>* Assist by artificial intelligence system for colon polyp detection<br>|
| No Intervention: Control<br>Endoscopists perform colonoscopy without CADe system | |

What is the study measuring?
-----------------
Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Adenoma detection rate | proportion of colonoscopies with at least one adenoma detected overall and as detected by the physician. | 12 months | 
| Sessile serrated lesion detection rate | proportion of colonoscopies with at least one sessile serrated lesion detected overall and as detected by the physician. | 12 months | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| polyp detection rate | proportion of colonoscopies with at least one polyp detected overall and as detected by the physician. | 12 months | 

 Terms related to the study
=================
Keywords Provided by Centre Hospitalier Valida
-----------------
endoscopist, adenoma detection rate, colonoscopy, artificial intelligence