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

Inflammatory Bowel Disease 1 Clinical Trial

Official title:

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

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT04867408
• Other study ID #: 299614
• Status: Recruiting
• Start date: September 17, 2021
• Est. completion date: September 2031

Study information

• Verified date: July 2023
• Source: Hull University Teaching Hospitals NHS Trust
• Contact: Shaji Sebastian
• Phone: 01482 816764
• Email: shaji.sebastian[@]hey.nhs.uk
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

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

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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 4000
• Est. completion date: September 2031
• Est. primary completion date: September 2031
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 16 Years to 99 Years

Eligibility

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

Related Conditions & MeSH terms

• Crohn Disease
• Inflammatory Bowel Disease 1
• Inflammatory Bowel Diseases
• Intestinal Diseases

Locations

Country	Name	City	State
United Kingdom	Hull Royal Infirmary	Hull	East Yorkshire

Sponsors (2)

Lead Sponsor	Collaborator
Hull University Teaching Hospitals NHS Trust	Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London

Country where clinical trial is conducted

United Kingdom, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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	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
Secondary	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
Secondary	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
Secondary	d) To evaluate comparisons in endoscopic image interpretation between endoscopist's	To evaluate comparisons in endoscopic image interpretation between endoscopist's	5 years
Secondary	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
Secondary	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