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

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

Detailed Description
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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.

Official Title
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EVEREST - IBD: Endoscopic Severity Image Recognition to Advance Research and Training in Inflammatory Bowel Disease

Conditions
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Inflammatory Bowel Disease 1

Participation Criteria
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Eligibility Criteria
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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

Ages Eligible for Study
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Minimum Age: 16 Years
Maximum Age: 99 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Main group<br>Patients with/suspected Inflammatory Bowel Disease attending for an endoscopic procedure | |
| Control<br>Patients without Inflammatory Bowel disease attending for an endoscopic procedure | |

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| To develop and train a convolutional neural network to detect and characterise disease severity of inflammatory bowel disease during endoscopy | To develop and train a convolutional neural network to detect and characterise disease severity of inflammatory bowel disease during endoscopy | 5 years | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| a) To explore whether Artificial Intelligence can predict response to IBD therapies | To explore whether Artificial Intelligence can predict response to IBD therapies | 5 years | 
| b) To develop an endoscopic image repository to advance training and standardisation in endoscopic detection and characterisation of IBD. | b) To develop an endoscopic image repository to advance training and standardisation | 5 years | 
| c) To develop and assess methodologies for training and quality assurance of IBD diagnostic endoscopy | To develop and assess methodologies for training and quality assurance of IBD | 5 years | 
| d) To evaluate comparisons in endoscopic image interpretation between endoscopist's | To evaluate comparisons in endoscopic image interpretation between endoscopist's | 5 years | 
| e) To develop deep learning algorithms and computer vision techniques to allow for automated measurement of quality metrics in endoscopy for IBD | To develop deep learning algorithms and computer vision techniques to allow for automated measurement of quality metrics in endoscopy for IBD | 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. | To create a future robust research platform to ensure the above objectives are continuously developed as novel imaging techniques emerge over time. | 5 years |