Opinion Review
Copyright ©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.
Artif Intell Gastrointest Endosc. Aug 28, 2021; 2(4): 95-102
Published online Aug 28, 2021. doi: 10.37126/aige.v2.i4.95
Artificial intelligence assisted assessment of endoscopic disease activity in inflammatory bowel disease
Bobby Lo, Johan Burisch
Bobby Lo, Johan Burisch, Gastrounit, Medical Section, Copenhagen University Hospital Hvidovre, Hvidovre 2650, Denmark
Bobby Lo, Johan Burisch, Copenhagen Centre for Inflammatory Bowel Disease in Children, Adolescents and Adults, Copenhagen University Hospital Hvidovre, Hvidovre 2650, Denmark
Author contributions: Lo B and Burisch J authors have made a significant contribution to the research described in this manuscript; all authors approved the final manuscript as well as the authorship list.
Conflict-of-interest statement: Lo B has received a lecture fee from Janssen-Cilag; Burisch J has received consulting fees from Celgene, Janssen-Cilag, AbbVie, Vifor Pharma, Jansen and Ferring; lecture fees from Abbvie, Pfizer, MSD, Pharmacosmos and Takeda Pharma, and unrestricted grant support from Takeda Pharma and Tillotts Pharma.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Bobby Lo, MD, Doctor, Gastrounit, Medical Section, Copenhagen University Hospital Hvidovre, Kettegård Alle 30, Hvidovre 2650, Denmark. bobby_lo@protonmail.com
Received: April 28, 2021
Peer-review started: April 28, 2021
First decision: June 13, 2021
Revised: June 27, 2021
Accepted: August 16, 2021
Article in press: August 16, 2021
Published online: August 28, 2021
Processing time: 130 Days and 13.4 Hours
Abstract

Assessment of endoscopic disease activity can be difficult in patients with inflammatory bowel disease (IBD) [comprises Crohn's disease (CD) and ulcerative colitis (UC)]. Endoscopic assessment is currently the foundation of disease evaluation and the grading is pivotal for the initiation of certain treatments. Yet, disharmony is found among experts; even when reassessed by the same expert. Some studies have demonstrated that the evaluation is no better than flipping a coin. In UC, the greatest achieved consensus between physicians when assessing endoscopic disease activity only reached a Kappa value of 0.77 (or 77% agreement adjustment for chance/accident). This is unsatisfactory when dealing with patients at risk of surgery or disease progression without proper care. Lately, across all medical specialities, computer assistance has become increasingly interesting. Especially after the emanation of machine learning – colloquially referred to as artificial intelligence (AI). Compared to other data analysis methods, the strengths of AI lie in its capability to derive complex models from a relatively small dataset and its ability to learn and optimise its predictions from new inputs. It is therefore evident that with such a model, one hopes to be able to remove inconsistency among humans and standardise the results across educational levels, nationalities and resources. This has manifested in a handful of studies where AI is mainly applied to capsule endoscopy in CD and colonoscopy in UC. However, due to its recent place in IBD, there is a great inconsistency between the results, as well as the reporting of the same. In this opinion review, we will explore and evaluate the method and results of the published studies utilising AI within IBD (with examples), and discuss the future possibilities AI can offer within IBD.

Keywords: Inflammatory bowel disease; Artificial intelligence; Deep learning; Endoscopy; Disease severity; Machine learning

Core Tip: Artificial intelligence (AI) is on the rise in inflammatory bowel diseases (IBD). Endoscopic evaluation is so far the most studied modality with promising results. Studies with others or the combination of several modalities have been carried out with moderate results leaving room for future research. Data availability and standardisation of the reporting of these new models seem to be the biggest challenges for AI's breakthrough within IBD. International consensus in the field is required to optimise research in AI.