Artificial Intelligence-assisted System in Colonoscopy

NCT06406062 | Recruiting

• 1 other identifier: 2024K-K017
• Study Type: observational
• Target: 7,500
• Locations: 1 country
• Sites: 1

Brief Summary

In recent years, computer-aided diagnosis system based on artificial intelligence (AI) has been used in colorectal polyp detection. In recent years, computer-aided diagnosis system based on artificial intelligence (AI) has been used in colorectal polyp detection. However, whether AI-assisted can improve the adenoma-detection rate (ADR) is inconclusive. This study aims to evaluate the real-world performance of an AI system that combines polyp detection with colonoscopy quality control. This study aims to explore the clinical application value of AI-based polyp detection and quality control function by comparing the data of polyp detection rate and adenoma detection rate in multiple centers with and without AI-assisted colonoscopy in a multicenter, prospective real world study. However, whether AI-assisted can improve the adenoma-detection rate (ADR) is inconclusive. This study aims to evaluate the real-world performance of an AI system that combines polyp detection with colonoscopy quality control. This study aims to explore the clinical application value of AI-based polyp detection and quality control function by comparing the data of polyp detection rate and adenoma detection rate in multiple centers with and without AI-assisted colonoscopy in a multicenter, prospective real world study.

Conditions

Adenoma Colon Polyp

Outcome Measures

Primary Outcomes (1)

• Adenoma detection rate

  The numerator is the number of patients who had at least one adenoma on colonoscopy, and the denominator is the total number of patients who underwent colonoscopy

  During Endoscopy procesure

Secondary Outcomes (5)

• Polyp detection rate

  During Endoscopy procesure

• Detection rate of serrated adenoma

  During Endoscopy procesure

• Average number of polyps per colonoscopy

  During Endoscopy procesure

• Colonoscopy time

  During Endoscopy procesure

• Proportion of over-speed frames

  During Endoscopy procesure

Study Arms (2)

AI-assisted group

Group with AI assistance

Device: ANDOANGEL

Control group

Group without AI assistance

Interventions

ANDOANGEL DEVICE

Polyps were identified by endoscopists assisted by an AI system: rectangular box marks; Monitoring of ileocecal position: whether blindness was reached was displayed in the lower left corner of the interface. Mirror entry and exit time monitoring: the operation time is displayed in the upper left corner of the interface. Colonoscopy withdrawal speed monitoring: the relative withdrawal speed was displayed on the left side of the interface.

AI-assisted group

Eligibility Criteria

• Age: 50 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

patients required diagnostic colonoscopy, screening colonoscopy, or follow-up colonoscopy

You may qualify if:

• age \> 50 years old;
• required diagnostic colonoscopy, screening colonoscopy, or follow-up colonoscopy;
• voluntarily sign informed consent;
• Commitment to abide by the study procedures and cooperate with the implementation of the whole process of the study.

You may not qualify if:

• have participated in other clinical trials, signed informed consent and are in the follow-up period of other clinical trials;
• known polyposis syndrome patients;
• patients with known IBD;
• patients considered by the investigators to be unsuitable or unable to undergo complete digestive endoscopy and related examinations;
• high-risk diseases or other special conditions considered by the investigator to be unsuitable for clinical trial participation.

Sponsors & Collaborators

• Renmin Hospital of Wuhan University: lead

Study Sites (1)

Renmin Hospital of Wuhan Univercity

Wuhan, Hubei, 430060, China

RECRUITING

Central Study Contacts

Honggang Yu, Doctor

CONTACT

18771146096; wjlnsm@163.com

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: April 25, 2024
• First Posted: May 9, 2024
• Study Start: May 20, 2024
• Primary Completion: December 30, 2025
• Study Completion: December 30, 2025
• Last Updated: April 13, 2025

Record last verified: 2025-04

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)