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 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.

• a) To explore whether Artificial Intelligence can predict response to IBD therapies [Срок оценки: 5 years]
• b) To develop an endoscopic image repository to advance training and standardisation in endoscopic detection and characterisation of IBD. [Срок оценки: 5 years]
• c) To develop and assess methodologies for training and quality assurance of IBD diagnostic endoscopy [Срок оценки: 5 years]
• d) To evaluate comparisons in endoscopic image interpretation between endoscopist's [Срок оценки: 5 years]
• e) To develop deep learning algorithms and computer vision techniques to allow for automated measurement of quality metrics in endoscopy for IBD [Срок оценки: 5 years]
• f) To create a future robust research platform to ensure the above objectives are continuously developed as novel imaging techniques emerge over time. [Срок оценки: 5 years]