To develop and train a convolutional neural network to detect and characterize disease severity of inflammatory bowel disease during endoscopy
• 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.
• 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
Inflammatory Bowel Disease (IBD) affects at least one in 250 people of the UK population and the prevalence is rising.
Other
Cross-sectional
Not listed
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
a) To explore whether Artificial Intelligence can predict response to IBD therapies
To explore whether Artificial Intelligence can predict response to IBD therapies
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
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
d) To evaluate comparisons in endoscopic image interpretation between endoscopist's
To evaluate comparisons in endoscopic image interpretation between endoscopist's
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Hull University Teaching Hospitals NHS Trust
Lead sponsor
Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London
Collaborator
