COVID-19 Pneumonia Diagnosis Using a Simple 2D Deep Learning Framework With a Single Chest CT Image: Model Development and Validation.

Journal: Journal of medical Internet research
Published Date: Jun 29, 2020

Abstract

BACKGROUND: Coronavirus disease (COVID-19) has spread explosively worldwide since the beginning of 2020. According to a multinational consensus statement from the Fleischner Society, computed tomography (CT) is a relevant screening tool due to its higher sensitivity for detecting early pneumonic changes. However, physicians are extremely occupied fighting COVID-19 in this era of worldwide crisis. Thus, it is crucial to accelerate the development of an artificial intelligence (AI) diagnostic tool to support physicians.

Authors

  • Hoon Ko
  • Heewon Chung
  • Wu Seong Kang
    Department of Trauma Surgery, Wonkwang University Hospital, Iksan-si, Republic of Korea.
  • Kyung Won Kim
    Department of Pediatrics, Severance Children's Hospital, Institute of Allergy, Institute for Immunology and Immunological Diseases, Brain Korea 21 PLUS Project for Medical Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea. kwkim@yuhs.ac.
  • Youngbin Shin
    Department of Radiology and Research Institute of Radiology, Asan Image Metrics, Clinical Trial Center, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
  • Seung Ji Kang
    Department of Internal Medicine, Chonnam National University Medical School, Gwangju-si, Republic of Korea.
  • Jae Hoon Lee
    Department of Food Science and Biotechnology of Animal Resources, Konkuk University, Seoul 05029, Korea.
  • Young Jun Kim
    3Department of Food and Biotechnology, Korea University, Sejong City, 30019 Republic of Korea.
  • Nan Yeol Kim
    Department of Trauma Surgery, Wonkwang University Hospital, Iksan-si, Republic of Korea.
  • Hyunseok Jung
    Department of Radiology, Wonkwang University Hospital, Iksan-si, Republic of Korea.
  • Jinseok Lee

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