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Capsule Endoscopy in Inflammatory Bowel Disease: Panenteric Capsule Endoscopy and Application of Artificial Intelligence

Video capsule endoscopy (VCE) of the small-bowel has been proven to accurately diagnose small-bowel inflammation and to predict future clinical flares among patients with Crohn’s disease (CD). In 2017, the panenteric capsule (PillCam Crohn’s system) was introduced for the first time, enabling a reli...

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Autores principales: Ukashi, Offir, Soffer, Shelly, Klang, Eyal, Eliakim, Rami, Ben-Horin, Shomron, Kopylov, Uri
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Editorial Office of Gut and Liver 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10352070/
https://www.ncbi.nlm.nih.gov/pubmed/37305947
http://dx.doi.org/10.5009/gnl220507
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author Ukashi, Offir
Soffer, Shelly
Klang, Eyal
Eliakim, Rami
Ben-Horin, Shomron
Kopylov, Uri
author_facet Ukashi, Offir
Soffer, Shelly
Klang, Eyal
Eliakim, Rami
Ben-Horin, Shomron
Kopylov, Uri
author_sort Ukashi, Offir
collection PubMed
description Video capsule endoscopy (VCE) of the small-bowel has been proven to accurately diagnose small-bowel inflammation and to predict future clinical flares among patients with Crohn’s disease (CD). In 2017, the panenteric capsule (PillCam Crohn’s system) was introduced for the first time, enabling a reliable evaluation of the whole small and large intestines. The great advantage of visualization of both parts of the gastrointestinal tract in a feasible and single procedure, holds a significant promise for patients with CD, enabling determination of the disease extent and severity, and potentially optimize disease management. In recent years, applications of machine learning, for VCE have been well studied, demonstrating impressive performance and high accuracy for the detection of various gastrointestinal pathologies, among them inflammatory bowel disease lesions. The use of artificial neural network models has been proven to accurately detect/classify and grade CD lesions, and shorten the VCE reading time, resulting in a less tedious process with a potential to minimize missed diagnosis and better predict clinical outcomes. Nevertheless, prospective, and real-world studies are essential to precisely examine artificial intelligence applications in real-life inflammatory bowel disease practice.
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spelling pubmed-103520702023-07-18 Capsule Endoscopy in Inflammatory Bowel Disease: Panenteric Capsule Endoscopy and Application of Artificial Intelligence Ukashi, Offir Soffer, Shelly Klang, Eyal Eliakim, Rami Ben-Horin, Shomron Kopylov, Uri Gut Liver Review Video capsule endoscopy (VCE) of the small-bowel has been proven to accurately diagnose small-bowel inflammation and to predict future clinical flares among patients with Crohn’s disease (CD). In 2017, the panenteric capsule (PillCam Crohn’s system) was introduced for the first time, enabling a reliable evaluation of the whole small and large intestines. The great advantage of visualization of both parts of the gastrointestinal tract in a feasible and single procedure, holds a significant promise for patients with CD, enabling determination of the disease extent and severity, and potentially optimize disease management. In recent years, applications of machine learning, for VCE have been well studied, demonstrating impressive performance and high accuracy for the detection of various gastrointestinal pathologies, among them inflammatory bowel disease lesions. The use of artificial neural network models has been proven to accurately detect/classify and grade CD lesions, and shorten the VCE reading time, resulting in a less tedious process with a potential to minimize missed diagnosis and better predict clinical outcomes. Nevertheless, prospective, and real-world studies are essential to precisely examine artificial intelligence applications in real-life inflammatory bowel disease practice. Editorial Office of Gut and Liver 2023-07-15 2023-06-12 /pmc/articles/PMC10352070/ /pubmed/37305947 http://dx.doi.org/10.5009/gnl220507 Text en Copyright © Gut and Liver. https://creativecommons.org/licenses/by-nc/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0 (https://creativecommons.org/licenses/by-nc/4.0/) ) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Review
Ukashi, Offir
Soffer, Shelly
Klang, Eyal
Eliakim, Rami
Ben-Horin, Shomron
Kopylov, Uri
Capsule Endoscopy in Inflammatory Bowel Disease: Panenteric Capsule Endoscopy and Application of Artificial Intelligence
title Capsule Endoscopy in Inflammatory Bowel Disease: Panenteric Capsule Endoscopy and Application of Artificial Intelligence
title_full Capsule Endoscopy in Inflammatory Bowel Disease: Panenteric Capsule Endoscopy and Application of Artificial Intelligence
title_fullStr Capsule Endoscopy in Inflammatory Bowel Disease: Panenteric Capsule Endoscopy and Application of Artificial Intelligence
title_full_unstemmed Capsule Endoscopy in Inflammatory Bowel Disease: Panenteric Capsule Endoscopy and Application of Artificial Intelligence
title_short Capsule Endoscopy in Inflammatory Bowel Disease: Panenteric Capsule Endoscopy and Application of Artificial Intelligence
title_sort capsule endoscopy in inflammatory bowel disease: panenteric capsule endoscopy and application of artificial intelligence
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10352070/
https://www.ncbi.nlm.nih.gov/pubmed/37305947
http://dx.doi.org/10.5009/gnl220507
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