Screening Colonoscopy
Conditions
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.
Interventions
PROCEDUREAI-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.
PROCEDUREStandard Colonoscopy
Standard Colonoscopy
Sponsors
Chinese University of Hong Kong
Study design
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
DIAGNOSTIC
Masking
SINGLE (Subject)
Eligibility
Sex/Gender
ALL
Age
45 Years to 75 Years
Healthy volunteers
Yes
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Per-patient ADR in each group | 12 months | For the AI-Assisted group, it is defined as the number of patients with at least 1 adenoma identified in the colon divided by the total number of patients in the AI-Assisted group. |
Countries
Hong Kong
Contacts
Primary ContactAndrew Ming Yeung HO
Backup ContactThomas Yuen Tung LAM
Outcome results
None listed