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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Editorial Office of Gut and Liver
2023
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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. |
format | Online Article Text |
id | pubmed-10352070 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Editorial Office of Gut and Liver |
record_format | MEDLINE/PubMed |
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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