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Endoscopic Severity Image Recognition to Advance Research and Training in Inflammatory Bowel Disease (EVEREST - IBD)

Sponsor: Hull University Teaching Hospitals NHS Trust

NCT ID: NCT04867408

View on ClinicalTrials.gov ↗

At a glance

What the study gets you
Health checks and monitoring — no treatment given
Type of study
Observational (no treatment given)
Time in hospital
In-person visits at study sites — visit count not specified by the sponsor
Drug or intervention
Not specified by the sponsor
How long the study runs
Study runs about 120 months (dates as stated)
About the drug or intervention
Not specified by the sponsor
Patient visit burden
Not specified by the sponsor
Type of study
Observing health over time
Ages
16 Years to 99 Years
Who
All
Number of participants
4,000
Started
2021-09-17
Last checked
2024-11

Plain English Summary

What is this study?

  • • Testing a new treatment for inflammatory bowel disease 1
  • • Clinical study - 4,000 participants
  • • To develop and train a convolutional neural network to detect and characterize disease severity of inflammatory bowel disease during endoscopy

Who can take part?

  • • Ages 16 Years to 99 Years
  • • Diagnosed with inflammatory bowel disease 1

Where?

  • • Hull - Hull Royal Infirmary

This is a simplified summary. Always discuss with your doctor before making any decisions.

About This Trial

To develop and train a convolutional neural network to detect and characterize disease severity of inflammatory bowel disease during endoscopy

More detail

To develop and train a Convolutional Neural Network to detect and characterize disease severity in inflammatory bowel disease during endoscopy. This initiative will inevitably establish a high-quality large image database. Our secondary study aims are therefore to use the images we collect to advance the field of deep learning and computer aided diagnosis in inflammatory bowel disease by establishing an image database. This will involve developing a framework combining deep learning and computer vision algorithms. The ultimate aim is to use the image database to produce high impact research outcomes and training resources leading to an improvement in the quality of endoscopy performed, reduce inter-observer variability in disease assessment and a reduction in missed bowel cancer rates and associated mortality.

Inflammatory Bowel Disease 1

How this trial compares with your answers

Answer 2 more questions to improve match

What we know so far

Condition· Matched your search
Age· Tell us your age for better matching
Gender· Tell us your sex for better matching

Still need:

  • • Tell us your age for better matching
  • • Tell us your sex for better matching

Preliminary match based on your answers. Full eligibility requires on-site assessment including medical history, physical exam, and lab tests. This does not guarantee enrolment.

Eligibility at a Glance

Key info

  • Age: 16 Years - 99 Years
  • Who can join: All genders

What the study is looking for

  • ✓• Any adult patient aged 16 years or older who has consented to undergo endoscopic investigation where images are...

Who cannot take part

  • ✗• Any patient under the age of 16
  • ✗Patients who are unable to give agreement to take part to undergo endoscopic investigation or those who do not wish their...
See the full 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 Is This Study? (1 UK site)

Hull Royal Infirmary

Hull HU3 2JZ, United Kingdom

Recruiting
Hospital R&D contact (matched)

James Illingworth

hyp-tr.development.research@nhs.net01482 461883 or 461903

How to Get in Touch

Shaji Sebastian

Sponsor contact

CONTACT

01482 816764 shaji.sebastian@hey.nhs.uk

Laurence Lovat

Sponsor contact

CONTACT

02076799606 l.lovat@ucl.ac.uk
Data sourced from ClinicalTrials.gov · Last verified: 2024-11