Artificial intelligence-based quantitative coronary angiography of major vessels using deep-learning.

Journal: International journal of cardiology
Published Date: Mar 11, 2024

Abstract

BACKGROUND: Quantitative coronary angiography (QCA) offers objective and reproducible measures of coronary lesions. However, significant inter- and intra-observer variability and time-consuming processes hinder the practical application of on-site QCA in the current clinical setting. This study proposes a novel method for artificial intelligence-based QCA (AI-QCA) analysis of the major vessels and evaluates its performance.

Authors

  • Young In Kim
    Department of Medical Science, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
  • Jae-Hyung Roh
    Department of Cardiology in Internal Medicine, School of Medicine, Chungnam National University, Chungnam National University Hospital, Daejeon, Korea.
  • Jihoon Kweon
    Division of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea. kjihoon2@naver.com.
  • Hwi Kwon
    Department of Biomedical Engineering, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
  • Jihye Chae
    Departments of Convergence Medicine, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.
  • Keunwoo Park
    Departments of Convergence Medicine, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.
  • Jae-Hwan Lee
    Department of Cardiology in Internal Medicine, School of Medicine, Chungnam National University, Chungnam National University Hospital, Daejeon, Korea.
  • Jin-Ok Jeong
    Division of Cardiology, Department of Internal Medicine, Chungnam National University Hospital, Chungnam National University College of Medicine, Daejeon, Republic of Korea.
  • Do-Yoon Kang
    Department of Cardiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.
  • Pil Hyung Lee
    Department of Cardiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.
  • Jung-Min Ahn
    Department of Cardiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.
  • Soo-Jin Kang
    Department of Cardiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.
  • Duk-Woo Park
    Department of Cardiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.
  • Seung-Whan Lee
    Department of Cardiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.
  • Cheol Whan Lee
    Department of Cardiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.
  • Seong-Wook Park
    Department of Cardiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.
  • Seung-Jung Park
    Department of Cardiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.
  • Young-Hak Kim
    Asan Medical Center, Seoul, Republic of Korea.

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