Automatically detecting Crohn’s disease and Ulcerative Colitis from endoscopic imaging

Abstract

Background The SI-CURA project (Soluzioni Innovative per la gestione del paziente e il follow up terapeutico della Colite UlceRosA) is an Italian initiative aimed at the development of artificial intelligence solutions to discriminate pathologies of different nature, including inflammatory bowel disease (IBD), namely Ulcerative Colitis (UC) and Crohn’s disease (CD), based on endoscopic imaging of patients (P) and healthy controls (N).

Methods In this study we develop a deep learning (DL) prototype to identify disease patterns through three binary classification tasks, namely (1) discriminating positive (pathological) samples from negative (healthy) samples (P vs N); (2) discrimination between Ulcerative Colitis and Crohn’s Disease samples (UC vs CD) and, (3) discrimination between Ulcerative Colitis and negative (healthy) samples (UC vs N).

Results The model derived from our approach achieves a high performance of Matthews correlation coefficient (MCC) > 0.9 on the test set for P versus N and UC versus N, and MCC > 0.6 on the test set for UC versus CD.

Conclusion Our DL model effectively discriminates between pathological and negative samples, as well as between IBD subgroups, providing further evidence of its potential as a decision support tool for endoscopy-based diagnosis.

Metadata

Item Type:	Article
Authors/Creators:	Chierici, Marco https://orcid.org/0000-0001-9791-9301 Puica, Nicolae Pozzi, Matteo Capistrano, Antonello Donzella, Marcello Dorian Colangelo, Antonio Osmani, Venet https://orcid.org/0000-0001-7306-2972 Jurman, Giuseppe
Copyright, Publisher and Additional Information:	© The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativeco mmons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
Keywords:	Artificial intelligence; Crohn’s disease; Diagnosis; Endoscopy; Inflammatory bowel disease; Machine learning; Predictive models; Ulcerative Colitis; Humans; Colitis, Ulcerative; Crohn Disease; Artificial Intelligence; Inflammatory Bowel Diseases; Endoscopy
Dates:	Published: December 2022 Published (online): 18 November 2022 Accepted: 8 November 2022
Institution:	The University of Sheffield
Academic Units:	The University of Sheffield > Faculty of Social Sciences (Sheffield) > Information School (Sheffield)
Depositing User:	Symplectic Sheffield
Date Deposited:	04 Jan 2023 14:13
Last Modified:	04 Jan 2023 14:18
Published Version:	http://dx.doi.org/10.1186/s12911-022-02043-w
Status:	Published
Publisher:	Springer Science and Business Media LLC
Refereed:	Yes
Identification Number:	10.1186/s12911-022-02043-w
Related URLs:	PubMed URL
Open Archives Initiative ID (OAI ID):	oai:eprints.whiterose.ac.uk:194829

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Automatically detecting Crohn’s disease and Ulcerative Colitis from endoscopic imaging

Abstract

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