Clinical trial NCT04867408: Endoscopic Severity Image Recognition to Advance Research and Training in Inflammatory Bowel Disease (EVEREST - IBD)
Official title: EVEREST - IBD: Endoscopic Severity Image Recognition to Advance Research and Training in Inflammatory Bowel Disease
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Inflammatory Bowel Disease 1
Lead sponsor: Hull University Teaching Hospitals NHS Trust
Sponsor class: OTHER_GOV
Collaborators: name: Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London
First posted: 2021-04-30
Enrollment: 4000
Sex: ALL
Minimum age (years): 16
Maximum age (years): 99
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: OTHER
Time perspective: CROSS_SECTIONAL
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.

Brief summary: 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.

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

Primary outcomes:
measure: To develop and train a convolutional neural network to detect and characterise disease severity of inflammatory bowel disease during endoscopy
timeFrame: 5 years
Secondary outcomes:
measure: a) To explore whether Artificial Intelligence can predict response to IBD therapies
timeFrame: 5 years
measure: b) To develop an endoscopic image repository to advance training and standardisation in endoscopic detection and characterisation of IBD.
timeFrame: 5 years
measure: c) To develop and assess methodologies for training and quality assurance of IBD diagnostic endoscopy
timeFrame: 5 years
measure: d) To evaluate comparisons in endoscopic image interpretation between endoscopist's
timeFrame: 5 years
measure: e) To develop deep learning algorithms and computer vision techniques to allow for automated measurement of quality metrics in endoscopy for IBD
timeFrame: 5 years
measure: f) To create a future robust research platform to ensure the above objectives are continuously developed as novel imaging techniques emerge over time.
timeFrame: 5 years
Other outcomes:
Locations:
city: Hull
country: United Kingdom
facility: Hull Royal Infirmary
state: East Yorkshire
Study officials:
affiliation: Hull University Teaching Hospitals NHS Trust
name: Shaji Sebastian
role: PRINCIPAL_INVESTIGATOR
References: