Clinical impact of a deep learning system for automated detection of missed pulmonary nodules on routine body computed tomography including the chest region.

Journal: European radiology
Published Date: Jan 9, 2022

Abstract

OBJECTIVES: To evaluate the clinical impact of a deep learning system (DLS) for automated detection of pulmonary nodules on computed tomography (CT) images as a second reader.

Authors

  • Kueian Chen
    Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Taoyuan, 33382, Guishan, Taiwan.
  • Ying-Chieh Lai
    Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Taoyuan, 33382, Guishan, Taiwan.
  • Balamuralidhar Vanniarajan
    FerrumFerrum Health, Santa Clara, CA, USA.
  • Pieh-Hsu Wang
    Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Taoyuan, 33382, Guishan, Taiwan.
  • Shao-Chung Wang
    Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Taoyuan, 33382, Guishan, Taiwan.
  • Yu-Chun Lin
    Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital, Linkou, Chang Gung University, Taoyuan, Taiwan.
  • Shu-Hang Ng
    Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital, Linkou, Chang Gung University, Taoyuan, Taiwan.
  • Pelu Tran
    FerrumFerrum Health, Santa Clara, CA, USA.
  • Gigin Lin
    Department of Medical Imaging and Intervention, Imaging Core Laboratory, Institute for Radiological Research, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan.

Keywords

External Resources

Popular Topics
Recent Journals