Automatic stenosis recognition from coronary angiography using convolutional neural networks.

Journal: Computer methods and programs in biomedicine
Published Date: Nov 2, 2020

Abstract

BACKGROUND AND OBJECTIVE: Coronary artery disease, which is mostly caused by atherosclerotic narrowing of the coronary artery lumen, is a leading cause of death. Coronary angiography is the standard method to estimate the severity of coronary artery stenosis, but is frequently limited by intra- and inter-observer variations. We propose a deep-learning algorithm that automatically recognizes stenosis in coronary angiographic images.

Authors

  • Jong Hak Moon
    Medical AI Research Center, Institute of Smart Healthcare, Samsung Medical Center, Seoul, Korea.
  • Da Young Lee
    Medical AI Research Center, Institute of Smart Healthcare, Samsung Medical Center, Seoul, Korea.
  • Won Chul Cha
    Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.
  • Myung Jin Chung
    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.).
  • Kyu-Sung Lee
    Department of Medical Device Management and Research, SAIHST, Sungkyunkwan University, Seoul 06351, South Korea; Department of Urology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, South Korea. Electronic address: ks63.lee@samsung.com.
  • Baek Hwan Cho
    Smart Healthcare & Device Research Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.
  • Jin Ho Choi
    Department of Medical Device Management and Research, SAIHST, Sungkyunkwan University, Seoul 06351, South Korea; Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, South Korea. Electronic address: jhchoimd@gmail.com.

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