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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Korean Society of Gastrointestinal Endoscopy
2018
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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. |
format | Online Article Text |
id | pubmed-6283750 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Korean Society of Gastrointestinal Endoscopy |
record_format | MEDLINE/PubMed |
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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