To develop and train a convolutional neural network to detect and characterize disease severity of inflammatory bowel disease during endoscopy
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.
Study Type
OBSERVATIONAL
Enrollment
4,000
Hull Royal Infirmary
Hull, East Yorkshire, United Kingdom
RECRUITINGTo 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
Time frame: 5 years
a) To explore whether Artificial Intelligence can predict response to IBD therapies
To explore whether Artificial Intelligence can predict response to IBD therapies
Time frame: 5 years
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
Time frame: 5 years
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
Time frame: 5 years
d) To evaluate comparisons in endoscopic image interpretation between endoscopist's
To evaluate comparisons in endoscopic image interpretation between endoscopist's
Time frame: 5 years
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
Time frame: 5 years
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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.
Time frame: 5 years