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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
AbstractAbstract PDFPubReaderePub
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.

Citations

Citations to this article as recorded by  
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    Sofia A. Athanasiou, Eleftheria S. Sergaki, Andreas A. Polydorou, Alexios A. Polydorou, George S. Stavrakakis, Nikolaos M. Afentakis, Ioannis O. Vardiambasis, Michail E. Zervakis
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    Rolf Bech Kjeldsen, Maja Nørgaard Kristensen, Carsten Gundlach, Lasse Højlund Eklund Thamdrup, Anette Müllertz, Thomas Rades, Line Hagner Nielsen, Kinga Zór, Anja Boisen
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  • Design and Research of Interactive Animation of Immersive Space Scene Based on Computer Vision Technology
    Shan Wu, Hubin Liu, Qi Xu, Yulong Liu, Sang-Bing Tsai
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  • Efficacy of a comprehensive binary classification model using a deep convolutional neural network for wireless capsule endoscopy
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  • 7,256 View
  • 251 Download
  • 20 Web of Science
  • 21 Crossref
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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
AbstractAbstract PDFPubReaderePub
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.

Citations

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    Diagnostics.2023; 13(8): 1389.     CrossRef
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Focused Review Series: Updates on Capsule Endoscopy from Esophagus to Colon
Current and Future Use of Esophageal Capsule Endoscopy
Junseok Park, Young Kwan Cho, Ji Hyun Kim
Clin Endosc 2018;51(4):317-322.   Published online July 31, 2018
DOI: https://doi.org/10.5946/ce.2018.101
AbstractAbstract PDFPubReaderePub
Capsule endoscopy can be a diagnostic option for patients with esophageal diseases who cannot tolerate esophagogastroduodenoscopy.Functional modifications of the capsule allow for thorough examination of the esophagus. Esophageal capsule endoscopy has so farfailed to show sufficient performance to justify the replacement of traditional endoscopy for the diagnosis of esophageal diseasesbecause the esophagus has a short transit time and common pathologies appear near the esophagogastric junction. However,technological improvements are being introduced to overcome the limitations of capsule endoscopy, which is expected to become agood alternative to conventional endoscopy.

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