Endoscopic Severity Image Recognition to Advance Research and Training in Inflammatory Bowel Disease (EVEREST - IBD)
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- This is an observational study: the protocol does not assign a study treatment.
- Who it may be relevant to
- Registry conditions: Inflammatory Bowel Disease 1. Basic parameters: 16 years — 99 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- United Kingdom
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title
EVEREST - IBD: Endoscopic Severity Image Recognition to Advance Research and Training in Inflammatory Bowel Disease
Overview
To develop and train a convolutional neural network to detect and characterize disease severity of inflammatory bowel disease during endoscopy
Detailed description
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.
Primary outcome measures
- To develop and train a convolutional neural network to detect and characterise disease severity of inflammatory bowel disease during endoscopy [Time frame: 5 years]
Secondary outcome measures (6)
- a) To explore whether Artificial Intelligence can predict response to IBD therapies [Time frame: 5 years]
- b) To develop an endoscopic image repository to advance training and standardisation in endoscopic detection and characterisation of IBD. [Time frame: 5 years]
- c) To develop and assess methodologies for training and quality assurance of IBD diagnostic endoscopy [Time frame: 5 years]
- d) To evaluate comparisons in endoscopic image interpretation between endoscopist's [Time frame: 5 years]
- e) To develop deep learning algorithms and computer vision techniques to allow for automated measurement of quality metrics in endoscopy for IBD [Time frame: 5 years]
- f) To create a future robust research platform to ensure the above objectives are continuously developed as novel imaging techniques emerge over time. [Time frame: 5 years]
Eligibility 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
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: Yes
Study design
- Observational model
- Other
Study locations
United Kingdom · 1 center
- Hull Royal Infirmary — Hull
Identifiers
NCT: NCT04867408 · 299614