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

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

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

Study Overview

• Status: Recruiting
• Conditions: Inflammatory Bowel Disease 1

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.

• Study Type: Observational
• Enrollment (Estimated): 4000

Contacts and Locations

• Study Contact: Name: Shaji Sebastian; Phone Number: 01482 816764; Email: shaji.sebastian@hey.nhs.uk
• Study Contact Backup: Name: Laurence Lovat; Phone Number: 02076799606; Email: l.lovat@ucl.ac.uk

Study Locations

• United Kingdom
  • East Yorkshire
    • Hull, East Yorkshire, United Kingdom, HU3 2JZ
      • Recruiting
      • Hull Royal Infirmary

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 16 years to 99 years (Child, Adult, Older Adult)
• Accepts Healthy Volunteers: Yes

• Sampling Method: Probability Sample

Study Population

Inflammatory Bowel Disease (IBD) affects at least one in 250 people of the UK population and the prevalence is rising.

Description

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

Study Plan

How is the study designed?

Design Details

• Observational Models: Other
• Time Perspectives: Cross-Sectional

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort
Main group; Patients with/suspected Inflammatory Bowel Disease attending for an endoscopic procedure
Control; Patients without Inflammatory Bowel disease attending for an endoscopic procedure

What is the study measuring?

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

Collaborators and Investigators

• Sponsor: Hull University Teaching Hospitals NHS Trust
• Collaborators: Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London
• Investigators: Principal Investigator: Shaji Sebastian, Hull University Teaching Hospitals NHS Trust

Study record dates

Study Major Dates

• Study Start (Actual): September 17, 2021
• Primary Completion (Estimated): September 1, 2031
• Study Completion (Estimated): September 1, 2031

Study Registration Dates

• First Submitted: April 27, 2021
• First Submitted That Met QC Criteria: April 27, 2021
• First Posted (Actual): April 30, 2021

Study Record Updates

• Last Update Posted (Actual): November 15, 2024
• Last Update Submitted That Met QC Criteria: November 14, 2024
• Last Verified: November 1, 2024

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Digestive System Diseases; Gastrointestinal Diseases; Gastroenteritis; Crohn Disease; Intestinal Diseases; Inflammatory Bowel Diseases
• Other Study ID Numbers: 299614

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No