Clinical Endoscopy

Youngbae Hwang

2 Articles

Recent Development of Computer Vision Technology to Improve Capsule Endoscopy: Junseok Park, Youngbae Hwang, Ju-Hong Yoon, Min-Gyu Park, Jungho Kim, Yun Jeong Lim, Hoon Jai Chun; Clin Endosc 2019;52(4):328-333. Published online February 21, 2019; DOI: https://doi.org/10.5946/ce.2018.172

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.

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Application of Artificial Intelligence in Capsule Endoscopy: Where Are We Now?: Youngbae Hwang, Junseok Park, Yun Jeong Lim, Hoon Jai Chun; Clin Endosc 2018;51(6):547-551. Published online November 30, 2018; DOI: https://doi.org/10.5946/ce.2018.173

Abstract PDF PubReader ePub

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.

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