@article{Oniga_Eelbode_Maes_Bossuyt_Bisschops_Blaschko_2023, title={Deep 
learning for prediction of future endoscopic disease activity in 
Ulcerative Colitis}, volume={1}, 
url={https://opacj.org/index.php/star/article/view/115}, 
abstractNote={<p>Ulcerative Colitis is an inflammatory bowel 
disease that affects the lower gastrointestinal tract which is composed of 
the colon and the rectum. The disease exhibits itself with alternating 
periods of acute phases and remission during which the patient can suffer 
various clinical manifestations. The best way at this time to asses the 
disease is through colonoscopies where the clinician looks for clinical 
symptoms such as redness, ulcerations, bleeding, stool frequency all of 
these being part of a scoring<br>system called the MAYO 
scoring system. The main limitation of this scoring system is the high 
subjectivity of the clinician that takes part in the assessment of the 
disease. This calls for an automated method of both diagnosing and scoring 
the disease using machine learning algorithms that are capable of 
detecting even the slightest differences in the evolution of the disease 
such that the treatment of said patient can be adjusted accordingly while 
predicting the clinical outcome: remission or 
non-remission.</p>}, number={1}, journal={Student Thinkers 
and AdvancedResearch}, author={Oniga, Robert Stefan and Eelbode, Tom and 
Maes, Frederik and Bossuyt, Peter and Bisschops, Raf and Blaschko, 
Matthew}, year={2023}, month={Nov.}, pages={7} }