  Ulcerative Colitis (UC) is a chronic inflammatory bowel disease decreasing
life quality through symptoms such as bloody diarrhoea and abdominal pain.
Endoscopy is a cornerstone of diagnosis and monitoring of UC. The Mayo
endoscopic subscore (MES) index is the standard for measuring UC severity
during endoscopic evaluation. However, the MES is subject to high
inter-observer variability leading to misdiagnosis and suboptimal treatment. We
propose using a machine-learning based MES classification system to support the
endoscopic process and to mitigate the observer-variability. The system runs
real-time in the clinic and augments doctors' decision-making during the
endoscopy. This project report outlines the process of designing, creating and
evaluating our system. We describe our initial evaluation, which is a
combination of a standard non-clinical model test and a first clinical test of
the system on a real patient.
