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

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The Chinese University of Hong Kong

Status

Unknown

Conditions

Screening Colonoscopy

Treatments

Procedure: AI-assisted Colonoscopy
Procedure: Standard Colonoscopy

Study type

Interventional

Funder types

Other

Identifiers

NCT04422548
AI-CLN Study_Protocol

Details and patient eligibility

About

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.

Full 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.

Enrollment

2,994 estimated patients

Sex

All

Ages

45 to 75 years old

Volunteers

Accepts Healthy Volunteers

Inclusion and exclusion 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

Trial design

Primary purpose

Diagnostic

Allocation

Randomized

Interventional model

Parallel Assignment

Masking

Single Blind

2,994 participants in 2 patient groups

AI-assisted Group
Active Comparator group
Treatment:
Procedure: AI-assisted Colonoscopy
Standard
Active Comparator group
Treatment:
Procedure: Standard Colonoscopy

Trial contacts and locations

1

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Central trial contact

Andrew Ming Yeung HO; Thomas Yuen Tung LAM

Data sourced from clinicaltrials.gov

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