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Hee Jin Kim 1 Article
Activities of the Korean Institute of Tuberculosis
Sungweon Ryoo, Hee Jin Kim
Osong Public Health Res Perspect. 2014;5(Suppl):S43-S49.   Published online December 31, 2014
DOI: https://doi.org/10.1016/j.phrp.2014.10.007
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AbstractAbstract PDF
The Korean National Tuberculosis Association (KNTA) set up the Korean Institute of Tuberculosis (KIT) in 1970 to foster research and technical activities pertaining to tuberculosis (TB). The KNTA/KIT had successfully conducted a countrywide TB prevalence survey from 1965 to 1995 at 5-year intervals. The survey results (decline in TB rates) established Korea as a country that had successfully implemented national control programs for TB. The KIT developed the Korea Tuberculosis Surveillance System and the Laboratory Management Information System, both of which were transferred to the Korea Centers for Disease Control and Prevention after its establishment. The KIT functions as a central and supranational reference TB laboratory for microbiological and epidemiological research and provides training and education for health-care workers and medical practitioners. Recently, the KIT has expanded its activities to countries such as Ethiopia, Laos, and Timor-Leste to support TB control and prevention. The KIT will continue to support research activities and provide technical assistance in diagnosing the infection until it is completely eliminated in Korea.

Citations

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    BMJ Global Health.2023; 8(10): e013573.     CrossRef
  • Review on Pneumonia Image Detection: A Machine Learning Approach
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    Mathematics.2022; 10(19): 3646.     CrossRef
  • An incremental learning approach to automatically recognize pulmonary diseases from the multi-vendor chest radiographs
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    Computers in Biology and Medicine.2021; 134: 104435.     CrossRef
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  • Intelligent Pneumonia Identification From Chest X-Rays: A Systematic Literature Review
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  • Deep learning, reusable and problem-based architectures for detection of consolidation on chest X-ray images
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    Computer Methods and Programs in Biomedicine.2020; 185: 105162.     CrossRef
  • Deep learning applications in pulmonary medical imaging: recent updates and insights on COVID-19
    Hanan Farhat, George E. Sakr, Rima Kilany
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  • PadChest: A large chest x-ray image dataset with multi-label annotated reports
    Aurelia Bustos, Antonio Pertusa, Jose-Maria Salinas, Maria de la Iglesia-Vayá
    Medical Image Analysis.2020; 66: 101797.     CrossRef
  • Utilizing Knowledge Distillation in Deep Learning for Classification of Chest X-Ray Abnormalities
    Thi Kieu Khanh Ho, Jeonghwan Gwak
    IEEE Access.2020; 8: 160749.     CrossRef
  • Computer-aided detection in chest radiography based on artificial intelligence: a survey
    Chunli Qin, Demin Yao, Yonghong Shi, Zhijian Song
    BioMedical Engineering OnLine.2018;[Epub]     CrossRef
  • The Relationship between Illness Perception and Health Behaviors among Patients with Tuberculosis: Mediating Effects of Self-efficacy and Family Support
    Hye-jin Kim, Myung Kyung Lee
    Korean Journal of Adult Nursing.2017; 29(6): 626.     CrossRef
  • Is Tuberculosis Still the Number One Infectious Disease in Korea?
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    Osong Public Health and Research Perspectives.2014; 5: S1.     CrossRef

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