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Clinical Trial Details — Status: Recruiting

Administrative data

NCT number NCT04867408
Other study ID # 299614
Secondary ID
Status Recruiting
Phase
First received
Last updated
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
Health authority
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

Study Design


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
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