A deep learning-based automatic analysis of cardiovascular borders on chest radiographs of valvular heart disease: development/external validation.

Abstract

Authors

• Cherry Kim
  Department of Radiology (J.Y.L., Y.W.O., S.H.H.) and Division of Cardiology, Department of Internal Medicine (D.S.L., C.W.Y., J.H.P., H.J.J.), Korea University Anam Hospital, 73 Inchon-ro, Seongbuk-gu, Seoul 02841, Republic of Korea; Department of Radiology, Korea University Guro Hospital, Seoul, Republic of Korea (H.S.Y., E.Y.K.); and Department of Radiology, Korea University Ansan Hospital, Ansan, Republic of Korea (C.K., K.Y.L.).
• Gaeun Lee
  Department of Convergence Medicine, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.
• Hongmin Oh
  Department of Radiology and Research Institute of Radiology, Cardiac Imaging Center, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
• Gyujun Jeong
  Biomedical Engineering Research Center, Asan Institute for Life Sciences, University of Ulsan College of Medicine, Seoul, Korea.
• Sun Won Kim
  Department of Cardiology, Korea University Ansan Hospital, Ansan, Korea.
• Eun Ju Chun
  Department of Radiology, Seoul National University Bundang Hospital, Seoul, Republic of Korea.
• Young-Hak Kim
  Asan Medical Center, Seoul, Republic of Korea.
• June-Goo Lee
  Biomedical Engineering Research Center, University of Ulsan College of Medicine, Asan Medical Center, Seoul 05505, Korea.
• Dong Hyun Yang
  Department of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.

Keywords

External Resources