Feasibility of Deep Learning-Based Analysis of Auscultation for Screening Significant Stenosis of Native Arteriovenous Fistula for Hemodialysis Requiring Angioplasty.

Journal: Korean journal of radiology
PMID:

Abstract

OBJECTIVE: To investigate the feasibility of using a deep learning-based analysis of auscultation data to predict significant stenosis of arteriovenous fistulas (AVF) in patients undergoing hemodialysis requiring percutaneous transluminal angioplasty (PTA).

Authors

  • Jae Hyon Park
    Department of Radiology, Yonsei University College of Medicine, Seoul, Korea.
  • Insun Park
    Department of Anesthesiology and Pain Medicine, Seoul National University Bundang Hospital, Seongnam, Korea.
  • Kichang Han
    Department of Radiology, Yonsei University College of Medicine, Seoul, Korea. wowsaycheese@yuhs.ac.
  • Jongjin Yoon
    Department of Radiology, Yonsei University College of Medicine, Seoul, Korea.
  • Yongsik Sim
    From the Department of Radiology, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea (Y.S., K.H., B.W.C.); Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea (M.J.C.); Department of Radiology, University Medical Center Freiburg, Freiburg, Germany (E.K.); Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Mass (S. Yune, M.K., S.D.); and Samsung Electronics, Suwon, Republic of Korea (H.K., S. Yang, D.J.L.).
  • Soo Jin Kim
    Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, Korea (G.R.K., E.-K.K., J.H.Y., H.J.M., J.Y.K.); Department of Radiology, Chung-Ang University Hospital, Chung-Ang University College of Medicine, Seoul, Korea (S.J.K.); Department of Radiology, Ajou University School of Medicine, Suwon, Korea (E.J.H.); Yonsei University College of Medicine, Seoul, Korea (J.Y.); and Department of Research Affairs, Biostatistics Collaboration Unit, Yonsei University College of Medicine, Seoul, Korea (H.S.L., J.H.H.).
  • Jong Yun Won
    Department of Radiology, Yonsei University College of Medicine, Seoul, Korea.
  • Shina Lee
    Department of Internal Medicine, College of Medicine, Ewha Womans University, Seoul, Korea.
  • Joon Ho Kwon
    Department of Radiology, Yonsei University College of Medicine, Seoul, Korea.
  • Sungmo Moon
    Department of Radiology, Yonsei University College of Medicine, Seoul, Korea.
  • Gyoung Min Kim
    Department of Radiology, Yonsei University College of Medicine, Seoul, South Korea.
  • Man-Deuk Kim
    Department of Radiology, Yonsei University College of Medicine, Seoul, Korea.