Performance of a deep-learning algorithm for referable thoracic abnormalities on chest radiographs: A multicenter study of a health screening cohort.

Journal: PloS one
Published Date: Jan 1, 2021

Abstract

PURPOSE: This study evaluated the performance of a commercially available deep-learning algorithm (DLA) (Insight CXR, Lunit, Seoul, South Korea) for referable thoracic abnormalities on chest X-ray (CXR) using a consecutively collected multicenter health screening cohort.

Authors

  • Eun Young Kim
    Department of Surgery, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Korea.
  • Young Jae Kim
    Department of Biomedical Engineering, College of Medicine, Gachon University, Gyeonggi-do, Republic of Korea.
  • Won-Jun Choi
    Department of Occupational and Environmental Medicine, Gachon University College of Medicine, Incheon, South Korea.
  • Gi Pyo Lee
    Department of Biomedical Engineering, Gachon University College of Medicine, Incheon, South Korea.
  • Ye Ra Choi
    Department of Radiology, Boramae Medical Center, Seoul, South Korea.
  • Kwang Nam Jin
    Department of Radiology, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, Seoul, South Korea.
  • Young Jun Cho
    Department of Radiology, Konyang University Hospital, Daejeon, South Korea.

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