Clin Endosc.  2019 Jul;52(4):328-333. 10.5946/ce.2018.172.

Recent Development of Computer Vision Technology to Improve Capsule Endoscopy

  • 1Digestive Disease Center, Institute for Digestive Research, Department of Internal Medicine, Soonchunhyang University College of Medicine, Seoul, Korea.
  • 2Intelligent Image Processing Research Center, Korea Electronics Technology Institute (KETI), Seongnam, Korea.
  • 3Department of Internal Medicine, Dongguk University Ilsan Hospital, Dongguk University College of Medicine, Goyang, Korea.
  • 4Division of Gastroenterology and Hepatology, Department of Internal Medicine, Institute of Gastrointestinal Medical Instrument Research, Korea University College of Medicine, Seoul, Korea.


Capsule endoscopy (CE) is a preferred diagnostic method for analyzing small bowel diseases. However, capsule endoscopes capture a sparse number of images because of their mechanical limitations. Post-procedural management using computational methods can enhance image quality. Additional information, including depth, can be obtained by using recently developed computer vision techniques. It is possible to measure the size of lesions and track the trajectory of capsule endoscopes using the computer vision technology, without requiring additional equipment. Moreover, the computational analysis of CE images can help detect lesions more accurately within a shorter time. Newly introduced deep leaning-based methods have shown more remarkable results over traditional computerized approaches. A large-scale standard dataset should be prepared to develop an optimal algorithms for improving the diagnostic yield of CE. The close collaboration between information technology and medical professionals is needed.


Capsule endoscopy; Computer vision technology; Deep learning

MeSH Terms

Capsule Endoscopes
Capsule Endoscopy*
Cooperative Behavior
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