Inflammation Detection Using Ensemble Endoscopic Multimodal Assessment in Inflammatory Bowel Disease

Kolawole BB, Chaudhari U, Santacroce G, Zammarchi I, Del Amor R, Meseguer P, Buda A, Bisschops R, Naranjo V, Ghosh S, Iacucci M, Grisan E, Bhandari P, De Hertogh G, Ferraz JG, Goetz M, Gui X, Hayee B, Kiesslich R, Metelli C, Lazarev M, Panaccione R, Parra-Blanco A, Pastorelli L, Rath T, Røyset ES, Vieth M, Villanacci V, Zardo D (2024)

Publication Type: Conference contribution

Publication year: 2024

Journal

Proceedings - International Symposium on Biomedical Imaging

Publisher: IEEE Computer Society

Conference Proceedings Title: Proceedings - International Symposium on Biomedical Imaging

Event location: Athens, GRC

ISBN: 9798350313338

DOI: 10.1109/ISBI56570.2024.10635162

Abstract

Inflammatory bowel diseases (IBD), comprising Crohn's disease (CD) and ulcerative colitis (UC), present chronic inflammatory gastrointestinal disorders with substantial implications for patients' quality of life. Traditional endoscopic evaluation remain pivotal for monitoring and managing IBD. Recent advancements in Virtual Chromoendoscopy (VCE) technologies, such as Flexible Spectral Imaging Color Enhancement (FICE) and iScan with digital enhancement, offer noninvasive alternatives for evaluating gastrointestinal diseases. While overcoming some limitations of White Light Endoscopy (WLE), these technologies introduce challenges related to scoring systems and deep learning algorithm training due to the qualitative nature of existing endoscopic scores. To address these challenges, we propose a combination of a generative (cycleGAN) and an ensemble model that integrates assessments from white light endoscopy (WLE), and generated Virtual Chromoendoscopy (VCE) to enhance inflammation detection and prediction. The ensemble model aims to combine the strengths of diverse modalities, providing a holistic understanding of a patient's inflammation status. Experiments demonstrated in this paper show that by integrating endoscopic findings with other modalities using an ensemble learning method can greatly improve the accuracy of prediction of IBD.

Involved external institutions

London South Bank University (LSBU) GB United Kingdom (GB) University College Cork (UCC) IE Ireland (IE) Polytechnic University of Valencia / Universidad Politécnica de Valencia ES Spain (ES) University Hospital Leuven (UZ) / Universitaire ziekenhuizen Leuven BE Belgium (BE)

How to cite

APA:

Kolawole, B.B., Chaudhari, U., Santacroce, G., Zammarchi, I., Del Amor, R., Meseguer, P.,... Zardo, D. (2024). Inflammation Detection Using Ensemble Endoscopic Multimodal Assessment in Inflammatory Bowel Disease. In Proceedings - International Symposium on Biomedical Imaging. Athens, GRC: IEEE Computer Society.

MLA:

Kolawole, Bisi Bode, et al. "Inflammation Detection Using Ensemble Endoscopic Multimodal Assessment in Inflammatory Bowel Disease." Proceedings of the 21st IEEE International Symposium on Biomedical Imaging, ISBI 2024, Athens, GRC IEEE Computer Society, 2024.

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