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NCT Number: NCT04867408

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

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

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Key information

Conditions

Inflammatory Bowel Disease 1 Crohn Disease Digestive System Diseases Gastroenteritis Gastrointestinal Diseases Inflammatory Bowel Diseases Intestinal Diseases

Age range

16 year–99 year

Sex eligibility

All sexes

Study type

Observational

Primary location

Hull Royal Infirmary

Hull, East Yorkshire, HU3 2JZ, United Kingdom

Location status: Recruiting

About this study

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.

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

Primary outcomes

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

    Time frame: 5 years

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

Secondary outcomes

  1. a) To explore whether Artificial Intelligence can predict response to IBD therapies

    Time frame: 5 years

    To explore whether Artificial Intelligence can predict response to IBD therapies

  2. b) To develop an endoscopic image repository to advance training and standardisation in endoscopic detection and characterisation of IBD.

    Time frame: 5 years

    b) To develop an endoscopic image repository to advance training and standardisation

  3. c) To develop and assess methodologies for training and quality assurance of IBD diagnostic endoscopy

    Time frame: 5 years

    To develop and assess methodologies for training and quality assurance of IBD

  4. d) To evaluate comparisons in endoscopic image interpretation between endoscopist's

    Time frame: 5 years

    To evaluate comparisons in endoscopic image interpretation between endoscopist's

  5. 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

    To develop deep learning algorithms and computer vision techniques to allow for automated measurement of quality metrics in endoscopy for IBD

  6. 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

    To create a future robust research platform to ensure the above objectives are continuously developed as novel imaging techniques emerge over time.

Study contacts

Contact information is provided by the study sponsor or research team.

Laurence Lovat

CONTACT

[email protected]

02076799606

Shaji Sebastian

CONTACT

[email protected]

01482 816764

Sponsors and collaborators

Lead sponsor

Hull University Teaching Hospitals NHS Trust

Other Gov

Collaborators

  • Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London

Registry information

Official study title

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

Important dates

Study start
2021
Primary completion
2031
Study completion
2031
First posted
Apr 30, 2021
Registry last updated
Nov 15, 2024

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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