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

Youngbae Hwang, Junseok Park, Yun Jeong Lim, Hoon-Jai Chun

Research output: Contribution to journalReview article

3 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)547-551
Number of pages5
JournalClinical Endoscopy
Volume51
Issue number6
DOIs
Publication statusPublished - 2018 Nov 1

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Capsule Endoscopy
Artificial Intelligence
Endoscopy
Direction compound

Keywords

  • Artificial intelligence
  • Capsule endoscopy
  • Deep learning
  • Lesion detection

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • Radiology Nuclear Medicine and imaging
  • Gastroenterology

Cite this

Application of artificial intelligence in capsule endoscopy : Where are we now? / Hwang, Youngbae; Park, Junseok; Lim, Yun Jeong; Chun, Hoon-Jai.

In: Clinical Endoscopy, Vol. 51, No. 6, 01.11.2018, p. 547-551.

Research output: Contribution to journalReview article

Hwang, Youngbae ; Park, Junseok ; Lim, Yun Jeong ; Chun, Hoon-Jai. / Application of artificial intelligence in capsule endoscopy : Where are we now?. In: Clinical Endoscopy. 2018 ; Vol. 51, No. 6. pp. 547-551.
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