Concordance rate of radiologists and a commercialized deep-learning solution for chest X-ray: Real-world experience with a multicenter health screening cohort.

Journal: PloS one
Published Date: Feb 24, 2022

Abstract

PURPOSE: Lunit INSIGHT CXR (Lunit) is a commercially available deep-learning algorithm-based decision support system for chest radiography (CXR). This retrospective study aimed to evaluate the concordance rate of radiologists and Lunit for thoracic abnormalities in a 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.
  • Ji Soo Jeon
    Department of Biomedical Engineering, Gachon University College of Medicine, Incheon, South Korea.
  • Moon Young Kim
    Department of Radiology, SMG-SNU Boramae Medical Center, Seoul, Korea.
  • Dong Hyun Oh
    Department of Radiology, Konyang University Hospital, Daejeon, 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.

Keywords

External Resources

Popular Topics
Recent Journals