Does AI-assisted Colonoscopy Improve Adenoma Detection in Screening Colonoscopy?

NCT ID: NCT04422548

Last Updated: 2020-07-16

Basic Information

• Recruitment Status: UNKNOWN
• Clinical Phase: NA
• Total Enrollment: 2994 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2019-11-28
• Study Completion Date: 2020-11-27

Brief Summary

To date, there is a lack of large-scale randomized controlled study using AI assistance in the detection of polyps/adenoma in a screening population. The correlation of fecal occult blood test (FIT or FOBT) and the advantage of AI-assisted colonoscopy has not been investigated. There is also a lack of information of the benefit of AI-assisted colonoscopy in experienced colonoscopist versus trainee/resident.

Detailed Description

There are several studies showing that AI-assisted colonoscopy can help in identifying and characterizing polyps found on colonoscopy.

• Byrne et al demonstrated that their AI model for real-time assessment of endoscopic video images of colorectal polyp can differentiate between hyperplastic diminutive polyps vs adenomatous polyps with sensitivity of 98% and specificity of 83% (Byrne et al. GUT 2019)
• Urban et al designed and trained deep CNNs to detect polyps in archived video with a ROC curve of 0.991 and accuracy of 96.4%. The total number of polyps identified is significantly higher but mainly in the small (1-3mm and 4-6mm polyps) (Urban et al. Gastroenterol 2018)
• Wang et al conducted an open, non-blinded trial consecutive patients (n=1058) prospectively randomized to undergo diagnostic colonoscopy with or without AI assistance. They found that AI system increased ADR from 20.3% to 29.1% and the mean number of adenomas per patients from 0.31 to 0.53. This was due to a higher number of diminutive polyps found while there was no statistic difference in larger adenoma. (Wang et al. GUT 2019). In this study, they excluded patients with IBD, CRC and colorectal surgery. The patients presented with symptoms to hospital for investigation.

To date, there is a lack of large-scale randomized controlled study using AI assistance in the detection of polyps/adenoma in a screening population. The correlation of fecal occult blood test (FIT or FOBT) and the advantage of AI-assisted colonoscopy has not been investigated. There is also a lack of information of the benefit of AI-assisted colonoscopy in experienced colonoscopist versus trainee/resident.

Conditions

Screening Colonoscopy

Study Design

• Allocation Method: RANDOMIZED
• Intervention Model: PARALLEL
• Primary Study Purpose: DIAGNOSTIC
• Blinding Strategy: SINGLE

Study Groups

AI-assisted Group

Group Type: ACTIVE_COMPARATOR

AI-assisted Colonoscopy

Intervention Type: PROCEDURE

This is a multi-center prospective randomized controlled study comparing real-time AI-assisted colonoscopy versus standard colonoscopy in a real-life setting.

Standard

Group Type: ACTIVE_COMPARATOR

Standard Colonoscopy

Intervention Type: PROCEDURE

Standard Colonoscopy

Interventions

AI-assisted Colonoscopy

This is a multi-center prospective randomized controlled study comparing real-time AI-assisted colonoscopy versus standard colonoscopy in a real-life setting.

Intervention Type: PROCEDURE

Standard Colonoscopy

Standard Colonoscopy

Intervention Type: PROCEDURE

Eligibility Criteria

Inclusion Criteria

• Patients receiving colonoscopy screening
• Patients aged 45-75 years
• Both patients who have or have not done a FIT test and both FIT +ve and FIT -ve subjects

Exclusion Criteria

• Patients who have symptom(s) suggestive of colorectal diseases
• Patients who have a history of inflammatory bowel disease, colorectal cancer or polyposis syndrome (anaemia, bloody stool, tenesmus and obstructive symptoms)
• Patients who had colonoscopy or other investigation of colon and rectum in the past 10 years
• Patients who had surgery for colorectal diseases
• Patients who cannot tolerate bowel preparation or have suboptimal bowel preparations (Boston Bowel Preparation Scale)
• Cannot reach caecum
• Patients who are incompetent in giving informed consent

• Minimum Eligible Age: 45 Years
• Maximum Eligible Age: 75 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

Chinese University of Hong Kong

OTHER

Sponsor Role: lead

Responsible Party

Joseph JY SUNG

Professor

Responsibility Role: PRINCIPAL_INVESTIGATOR

Locations

Prince of Wales Hospital

Hong Kong, , Hong Kong

Site Status: RECRUITING

Countries

Hong Kong

Central Contacts

Andrew Ming Yeung HO

Role: CONTACT

andrewho@cuhk.edu.hk

26371398

Thomas Yuen Tung LAM

Role: CONTACT

thomaslam@cuhk.edu.hk

26370355

Facility Contacts

Ming Yeung HO

Role: primary

andrewho@cuhk.edu.hk

26371398

References

Xu H, Tang RSY, Lam TYT, Zhao G, Lau JYW, Liu Y, Wu Q, Rong L, Xu W, Li X, Wong SH, Cai S, Wang J, Liu G, Ma T, Liang X, Mak JWY, Xu H, Yuan P, Cao T, Li F, Ye Z, Shutian Z, Sung JJY. Artificial Intelligence-Assisted Colonoscopy for Colorectal Cancer Screening: A Multicenter Randomized Controlled Trial. Clin Gastroenterol Hepatol. 2023 Feb;21(2):337-346.e3. doi: 10.1016/j.cgh.2022.07.006. Epub 2022 Jul 19.

Reference Type: DERIVED

PMID: 35863686 (View on PubMed)

Other Identifiers

AI-CLN Study_Protocol

Identifier Type: -

Identifier Source: org_study_id