Inflammatory Bowel Disease 1
Conditions
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.
Interventions
None listed
Sponsors
Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London
Hull University Teaching Hospitals NHS Trust
Study design
Observational model
OTHER
Time perspective
CROSS_SECTIONAL
Eligibility
Sex/Gender
ALL
Age
16 Years to 99 Years
Healthy volunteers
Yes
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| To develop and train a convolutional neural network to detect and characterise disease severity of inflammatory bowel disease during endoscopy | 5 years | To develop and train a convolutional neural network to detect and characterise disease severity of inflammatory bowel disease during endoscopy |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| b) To develop an endoscopic image repository to advance training and standardisation in endoscopic detection and characterisation of IBD. | 5 years | 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 | 5 years | To develop and assess methodologies for training and quality assurance of IBD |
| a) To explore whether Artificial Intelligence can predict response to IBD therapies | 5 years | To explore whether Artificial Intelligence can predict response to IBD therapies |
| e) To develop deep learning algorithms and computer vision techniques to allow for automated measurement of quality metrics in endoscopy for IBD | 5 years | To develop deep learning algorithms and computer vision techniques to allow for automated measurement of quality metrics in endoscopy for IBD |
| f) To create a future robust research platform to ensure the above objectives are continuously developed as novel imaging techniques emerge over time. | 5 years | To create a future robust research platform to ensure the above objectives are continuously developed as novel imaging techniques emerge over time. |
| d) To evaluate comparisons in endoscopic image interpretation between endoscopist's | 5 years | To evaluate comparisons in endoscopic image interpretation between endoscopist's |
Countries
United Kingdom
Contacts
Primary ContactShaji Sebastian
Backup ContactLaurence Lovat
Outcome results
None listed