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Clin Endosc. 2018 Nov;51(6):547-551. English. Review. https://doi.org/10.5946/ce.2018.173
Hwang Y , Park J , Lim YJ , Chun HJ .
Intelligent Image Processing Research Center, Korea Electronics Technology Institute (KETI), Seongnam, Korea.
Digestive Disease Center, Institute for Digestive Research, Department of Internal Medicine, Soonchunhyang University College of Medicine, Seoul, Korea.
Department of Internal Medicine, Dongguk University Ilsan Hospital, Dongguk University College of Medicine, Goyang, Korea. drlimyj@gmail.com
Division of Gastroenterology and Hepatology, Department of Internal Medicine, Korea University College of Medicine, Seoul, Korea.
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.

Copyright © 2019. Korean Association of Medical Journal Editors.