AID-GI – Artificial Intelligence-supported Diagnostics of Gastrointestinal diseases with video capsule endoscopy

"This project is aiming to improve the diagnostic accuracy for lower gastrointestinal diseases, especially inflammations, by applying _machine learning_ to aid the analysis of internal images.

Currently, the gastrointestinal tract is analysed using markers in the stool or with images taken via ingestible cameras or traditional tube-mounted cameras. While the former is limited to detecting cancers (FIT) or generic inflammation (calprotectin) and therefore rather unspecific, the latter is undertaken by human operators and therefore prone to human errors resulting from fatigue, distraction, variable experience or other cognitive limitations. The volume of images and the volume of patients requiring screening, places unmanageable loads on the operators in terms of effort and quality.

This project focuses on Video Capsule Endoscopy -- ingestible 'capsule' cameras which are increasingly likely to become the dominant approach to gastrointestinal imagery, where the capsules capture images as patients go about their daily lives rather than attending a hospital or clinic.

Through the project we will test the latest approaches to automated image analysis, quantify benefits to the patient, clinician and NHS -- financially and clinically -- and make recommendations on how to implement the solution.

When successful, this approach can remove the 'diagnostic bottleneck' that limits optimal IBD treatments, bowel cancer prevention, early detection and other disease investigations."