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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.

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  • A Review of Biomedical Devices: Classification, Regulatory Guidelines, Human Factors, Software as a Medical Device, and Cybersecurity
    Felix Tettey, Santosh Kumar Parupelli, Salil Desai
    Biomedical Materials & Devices.2024; 2(1): 316.     CrossRef
  • Real‐time small bowel visualization quality assessment in wireless capsule endoscopy images using different lightweight embeddable models
    Vahid Sadeghi, Alireza Mehridehnavi, Yasaman Sanahmadi, Sajed Rakhshani, Mina Omrani, Mohsen Sharifi
    International Journal of Imaging Systems and Technology.2024;[Epub]     CrossRef
  • STAR-RL: Spatial-Temporal Hierarchical Reinforcement Learning for Interpretable Pathology Image Super-Resolution
    Wenting Chen, Jie Liu, Tommy W. S. Chow, Yixuan Yuan
    IEEE Transactions on Medical Imaging.2024; 43(12): 4368.     CrossRef
  • A Novel Computer-Aided Detection/Diagnosis System for Detection and Classification of Polyps in Colonoscopy
    Chia-Pei Tang, Hong-Yi Chang, Wei-Chun Wang, Wei-Xuan Hu
    Diagnostics.2023; 13(2): 170.     CrossRef
  • Revealing the Boundaries of Selected Gastro-Intestinal (GI) Organs by Implementing CNNs in Endoscopic Capsule Images
    Sofia A. Athanasiou, Eleftheria S. Sergaki, Andreas A. Polydorou, Alexios A. Polydorou, George S. Stavrakakis, Nikolaos M. Afentakis, Ioannis O. Vardiambasis, Michail E. Zervakis
    Diagnostics.2023; 13(5): 865.     CrossRef
  • Transformer with Hybrid Attention Mechanism for Stereo Endoscopic Video Super Resolution
    Tianyi Zhang, Jie Yang
    Symmetry.2023; 15(10): 1947.     CrossRef
  • KAPSUL ENDOSKOPİYASI İLƏ İNCƏ BAĞIRSAQ MÜAYİNƏSİNDƏ MÖVCUD VƏZİYYƏT VƏ GƏLƏCƏK PERSPEKTİVLİYİ
    Həbib Həsənzadə, Amalya Həsənova Həbib Həsənzadə, Amalya Həsənova
    PAHTEI-Procedings of Azerbaijan High Technical Educational Institutions.2023; 34(11): 105.     CrossRef
  • Review: Colon Capsule Endoscopy in Inflammatory Bowel Disease
    Writaja Halder, Faidon-Marios Laskaratos, Hanan El-Mileik, Sergio Coda, Stevan Fox, Saswata Banerjee, Owen Epstein
    Diagnostics.2022; 12(1): 149.     CrossRef
  • Small Bowel Detection for Wireless Capsule Endoscopy Using Convolutional Neural Networks with Temporal Filtering
    Geonhui Son, Taejoon Eo, Jiwoong An, Dong Oh, Yejee Shin, Hyenogseop Rha, You Kim, Yun Lim, Dosik Hwang
    Diagnostics.2022; 12(8): 1858.     CrossRef
  • Dynamic Depth-Aware Network for Endoscopy Super-Resolution
    Wenting Chen, Yifan Liu, Jiancong Hu, Yixuan Yuan
    IEEE Journal of Biomedical and Health Informatics.2022; 26(10): 5189.     CrossRef
  • X-ray Imaging for Gastrointestinal Tracking of Microscale Oral Drug Delivery Devices
    Rolf Bech Kjeldsen, Maja Nørgaard Kristensen, Carsten Gundlach, Lasse Højlund Eklund Thamdrup, Anette Müllertz, Thomas Rades, Line Hagner Nielsen, Kinga Zór, Anja Boisen
    ACS Biomaterials Science & Engineering.2021; 7(6): 2538.     CrossRef
  • VR-Caps: A Virtual Environment for Capsule Endoscopy
    Kağan İncetan, Ibrahim Omer Celik, Abdulhamid Obeid, Guliz Irem Gokceler, Kutsev Bengisu Ozyoruk, Yasin Almalioglu, Richard J. Chen, Faisal Mahmood, Hunter Gilbert, Nicholas J. Durr, Mehmet Turan
    Medical Image Analysis.2021; 70: 101990.     CrossRef
  • Development of a deep learning-based software for calculating cleansing score in small bowel capsule endoscopy
    Ji Hyung Nam, Youngbae Hwang, Dong Jun Oh, Junseok Park, Ki Bae Kim, Min Kyu Jung, Yun Jeong Lim
    Scientific Reports.2021;[Epub]     CrossRef
  • Kvasir-Capsule, a video capsule endoscopy dataset
    Pia H. Smedsrud, Vajira Thambawita, Steven A. Hicks, Henrik Gjestang, Oda Olsen Nedrejord, Espen Næss, Hanna Borgli, Debesh Jha, Tor Jan Derek Berstad, Sigrun L. Eskeland, Mathias Lux, Håvard Espeland, Andreas Petlund, Duc Tien Dang Nguyen, Enrique Garcia
    Scientific Data.2021;[Epub]     CrossRef
  • Development and Verification of a Deep Learning Algorithm to Evaluate Small-Bowel Preparation Quality
    Ji Hyung Nam, Dong Jun Oh, Sumin Lee, Hyun Joo Song, Yun Jeong Lim
    Diagnostics.2021; 11(6): 1127.     CrossRef
  • Role of Artificial Intelligence in Video Capsule Endoscopy
    Ioannis Tziortziotis, Faidon-Marios Laskaratos, Sergio Coda
    Diagnostics.2021; 11(7): 1192.     CrossRef
  • 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
    Mathematical Problems in Engineering.2021; 2021: 1.     CrossRef
  • Efficacy of a comprehensive binary classification model using a deep convolutional neural network for wireless capsule endoscopy
    Sang Hoon Kim, Youngbae Hwang, Dong Jun Oh, Ji Hyung Nam, Ki Bae Kim, Junseok Park, Hyun Joo Song, Yun Jeong Lim
    Scientific Reports.2021;[Epub]     CrossRef
  • Artificial intelligence that determines the clinical significance of capsule endoscopy images can increase the efficiency of reading
    Junseok Park, Youngbae Hwang, Ji Hyung Nam, Dong Jun Oh, Ki Bae Kim, Hyun Joo Song, Su Hwan Kim, Sun Hyung Kang, Min Kyu Jung, Yun Jeong Lim, Sudipta Roy
    PLOS ONE.2020; 15(10): e0241474.     CrossRef
  • EndoL2H: Deep Super-Resolution for Capsule Endoscopy
    Yasin Almalioglu, Kutsev Bengisu Ozyoruk, Abdulkadir Gokce, Kagan Incetan, Guliz Irem Gokceler, Muhammed Ali Simsek, Kivanc Ararat, Richard J. Chen, Nicholas J. Durr, Faisal Mahmood, Mehmet Turan
    IEEE Transactions on Medical Imaging.2020; 39(12): 4297.     CrossRef
  • 7,013 View
  • 249 Download
  • 19 Web of Science
  • 20 Crossref
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Image Quality Analysis of Various Gastrointestinal Endoscopes: Why Image Quality Is a Prerequisite for Proper Diagnostic and Therapeutic Endoscopy
Weon Jin Ko, Pyeong An, Kwang Hyun Ko, Ki Baik Hahm, Sung Pyo Hong, Joo Young Cho
Clin Endosc 2015;48(5):374-379.   Published online September 30, 2015
DOI: https://doi.org/10.5946/ce.2015.48.5.374
AbstractAbstract PDFPubReaderePub

Arising from human curiosity in terms of the desire to look within the human body, endoscopy has undergone significant advances in modern medicine. Direct visualization of the gastrointestinal (GI) tract by traditional endoscopy was first introduced over 50 years ago, after which fairly rapid advancement from rigid esophagogastric scopes to flexible scopes and high definition videoscopes has occurred. In an effort towards early detection of precancerous lesions in the GI tract, several high-technology imaging scopes have been developed, including narrow band imaging, autofocus imaging, magnified endoscopy, and confocal microendoscopy. However, these modern developments have resulted in fundamental imaging technology being skewed towards red-green-blue and this technology has obscured the advantages of other endoscope techniques. In this review article, we have described the importance of image quality analysis using a survey to consider the diversity of endoscope system selection in order to better achieve diagnostic and therapeutic goals. The ultimate aims can be achieved through the adoption of modern endoscopy systems that obtain high image quality.

Citations

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  • Colonoscopy Quality, Innovation, and the Assessment of New Technology
    Sanjay R.V. Gadi, Sriya S. Muralidharan, Jeremy R. Glissen Brown
    Techniques and Innovations in Gastrointestinal Endoscopy.2024; 26(2): 177.     CrossRef
  • Endoscopy image enhancement method by generalized imaging defect models based adversarial training
    Wenjie Li, Jingfan Fan, Yating Li, Pengcheng Hao, Yucong Lin, Tianyu Fu, Danni Ai, Hong Song, Jian Yang
    Physics in Medicine & Biology.2022; 67(9): 095016.     CrossRef
  • Reduced detection rate of artificial intelligence in images obtained from untrained endoscope models and improvement using domain adaptation algorithm
    Junseok Park, Youngbae Hwang, Hyun Gun Kim, Joon Seong Lee, Jin-Oh Kim, Tae Hee Lee, Seong Ran Jeon, Su Jin Hong, Bong Min Ko, Seokmin Kim
    Frontiers in Medicine.2022;[Epub]     CrossRef
  • Diagnosis of Early Gastric Cancer Using Image-enhanced Endoscopy
    Weon Jin Ko
    The Korean Journal of Medicine.2017; 92(3): 264.     CrossRef
  • 8,843 View
  • 120 Download
  • 5 Web of Science
  • 4 Crossref
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Focused Review Series: Endoscopic and Molecular Imaging of Premalignant GI Lesions, Part I
Endoscopic Molecular Imaging: Status and Future Perspective
Naoki Muguruma, Hiroshi Miyamoto, Toshiya Okahisa, Tetsuji Takayama
Clin Endosc 2013;46(6):603-610.   Published online November 19, 2013
DOI: https://doi.org/10.5946/ce.2013.46.6.603
AbstractAbstract PDFPubReaderePub

During the last decade, researchers have made great progress in the development of new image processing technologies for gastrointestinal endoscopy. However, diagnosis using conventional endoscopy with white light optical imaging is essentially limited, and ultimately, we still rely on the histopathological diagnosis from biopsy specimens. Molecular imaging represents the most novel imaging methods in medicine, and the future of endoscopic diagnosis is likely to be impacted by a combination of biomarkers and technology. Endoscopic molecular imaging can be defined as the visualization of molecular characteristics with endoscopy. These innovations will allow us not only to locate a tumor or dysplastic lesion but also to visualize its molecular characteristics and the activity of specific molecules and biological processes that affect tumor behavior and/or its response to therapy. In the near future, these promising technologies will play a central role in endoluminal oncology.

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    PLOS ONE.2023; 18(5): e0286189.     CrossRef
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    WIREs Nanomedicine and Nanobiotechnology.2022;[Epub]     CrossRef
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    XIAOFENG YANG, FAN ZHANG, JUNQIAN LUO, JIANZHI PANG, SANHUA YAN, FANG LUO, JIEHAO LIU, WEI WANG, YONGPING CUI, XIXI SU
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  • 7,366 View
  • 79 Download
  • 15 Crossref
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