Using AI to assess severity in inflammatory bowel disease during endoscopy
Official title: EVEREST - IBD: Endoscopic Severity Image Recognition to Advance Research and Training in Inflammatory Bowel Disease
Run by Hull University Teaching Hospitals NHS Trust · NCT04867408 · Data updated 2026-11-12
This study is testing if artificial intelligence can help doctors better see and understand the severity of inflammatory bowel disease during endoscopy to improve patient care.
Quick facts
| Study type | Observational |
|---|---|
| Ages | 16 Years to 99 Years |
| Sex | All |
| Enrollment | 4000 (estimated) |
| Sponsor | Hull University Teaching Hospitals NHS Trust Government |
| Locations | 1 site (Hull, East Yorkshire) |
| Status | Recruiting |
| Started | September 17, 2021 |
| Main results expected | September 2031 |
| Study ends | September 2031 |
| Registry record updated | November 15, 2024 |
| Trial ID | NCT04867408 on ClinicalTrials.gov |
Is this trial a fit for you?
✓Likely a good fit
Ideal candidates include adults aged 16 years or older who are undergoing endoscopic investigation for inflammatory bowel disease.
✕Probably not a fit
Patients under the age of 16 or those unable to provide informed consent will not benefit from this study.
A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.
What this trial studies
This study aims to develop and train a convolutional neural network (CNN) to detect and characterize the severity of inflammatory bowel disease (IBD) during endoscopic procedures. By creating a large image database from endoscopic images, the study seeks to enhance deep learning and computer-aided diagnosis in IBD. The initiative will also focus on improving the quality of endoscopy, reducing inter-observer variability in disease assessment, and lowering missed bowel cancer rates. Ultimately, the goal is to produce impactful research outcomes and training resources for healthcare professionals.
Why it matters
Potential benefit: If successful, this study could lead to improved accuracy in diagnosing and assessing inflammatory bowel disease, ultimately enhancing patient care.
How similar studies have gone: Other studies utilizing deep learning for medical image analysis have shown promising results, indicating potential success for this approach.
Who can join: the full rules
Show full inclusion / exclusion criteria
Inclusion Criteria: * • Any adult patient aged 16 years or older who has consented to undergo endoscopic investigation where images are captured as part of routine clinical care. Exclusion Criteria: * • Any patient under the age of 16 * Patients who are unable to give informed consent to undergo endoscopic investigation or those who do not wish their pseudo-anonymised images to be used
Where it's running
-
Hull Royal Infirmary
Hull, East Yorkshire, United Kingdom Recruiting
Study contacts
- Principal investigator: Shaji Sebastian — Hull University Teaching Hospitals NHS Trust
- Study coordinator: Shaji Sebastian
- Email: shaji.sebastian@hey.nhs.uk
- Phone: 01482 816764
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How to take part
- Review the eligibility criteria above with your treating physician.
- Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
- Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.