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Application of artificial intelligence in capsule endoscopy: Where are we now?

Authors
Hwang Y.Park J.Lim Y.J.Chun H.J.
Issue Date
2018
Publisher
Korean Society of Gastrointestinal Endoscopy
Keywords
Artificial intelligence; Capsule endoscopy; Deep learning; Lesion detection
Citation
Clinical Endoscopy, v.51, no.6, pp.547 - 551
Indexed
SCOPUS
KCI
Journal Title
Clinical Endoscopy
Volume
51
Number
6
Start Page
547
End Page
551
URI
https://scholarworks.korea.ac.kr/kumedicine/handle/2020.sw.kumedicine/4203
DOI
10.5946/ce.2018.173
ISSN
2234-2400
Abstract
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. © 2018 Korean Society of Gastrointestinal Endoscopy.
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Chun, Hoon Jai
Anam Hospital (Department of Gastroenterology and Hepatology, Anam Hospital)
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