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Application of Artificial Intelligence in Capsule Endoscopy: Where Are We Now?

Unlike wired endoscopy, capsule endoscopy requires additional time for a clinical specialist to review the operation and examine the lesions. To reduce the tedious review time and increase the accuracy of medical examinations, various approaches have been reported based on artificial intelligence fo...

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Detalles Bibliográficos
Autores principales: Hwang, Youngbae, Park, Junseok, Lim, Yun Jeong, Chun, Hoon Jai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Korean Society of Gastrointestinal Endoscopy 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6283750/
https://www.ncbi.nlm.nih.gov/pubmed/30508880
http://dx.doi.org/10.5946/ce.2018.173
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author Hwang, Youngbae
Park, Junseok
Lim, Yun Jeong
Chun, Hoon Jai
author_facet Hwang, Youngbae
Park, Junseok
Lim, Yun Jeong
Chun, Hoon Jai
author_sort Hwang, Youngbae
collection PubMed
description Unlike wired endoscopy, capsule endoscopy requires additional time for a clinical specialist to review the operation and examine the lesions. To reduce the tedious review time and increase the accuracy of medical examinations, various approaches have been reported based on artificial intelligence for computer-aided diagnosis. Recently, deep learning–based approaches have been applied to many possible areas, showing greatly improved performance, especially for image-based recognition and classification. By reviewing recent deep learning–based approaches for clinical applications, we present the current status and future direction of artificial intelligence for capsule endoscopy.
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spelling pubmed-62837502018-12-20 Application of Artificial Intelligence in Capsule Endoscopy: Where Are We Now? Hwang, Youngbae Park, Junseok Lim, Yun Jeong Chun, Hoon Jai Clin Endosc Review Unlike wired endoscopy, capsule endoscopy requires additional time for a clinical specialist to review the operation and examine the lesions. To reduce the tedious review time and increase the accuracy of medical examinations, various approaches have been reported based on artificial intelligence for computer-aided diagnosis. Recently, deep learning–based approaches have been applied to many possible areas, showing greatly improved performance, especially for image-based recognition and classification. By reviewing recent deep learning–based approaches for clinical applications, we present the current status and future direction of artificial intelligence for capsule endoscopy. Korean Society of Gastrointestinal Endoscopy 2018-11 2018-11-30 /pmc/articles/PMC6283750/ /pubmed/30508880 http://dx.doi.org/10.5946/ce.2018.173 Text en Copyright © 2018 Korean Society of Gastrointestinal Endoscopy This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Review
Hwang, Youngbae
Park, Junseok
Lim, Yun Jeong
Chun, Hoon Jai
Application of Artificial Intelligence in Capsule Endoscopy: Where Are We Now?
title Application of Artificial Intelligence in Capsule Endoscopy: Where Are We Now?
title_full Application of Artificial Intelligence in Capsule Endoscopy: Where Are We Now?
title_fullStr Application of Artificial Intelligence in Capsule Endoscopy: Where Are We Now?
title_full_unstemmed Application of Artificial Intelligence in Capsule Endoscopy: Where Are We Now?
title_short Application of Artificial Intelligence in Capsule Endoscopy: Where Are We Now?
title_sort application of artificial intelligence in capsule endoscopy: where are we now?
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6283750/
https://www.ncbi.nlm.nih.gov/pubmed/30508880
http://dx.doi.org/10.5946/ce.2018.173
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