Intravascular ultrasound-based machine learning for predicting fractional flow reserve in intermediate coronary artery lesions.

Journal: Atherosclerosis
Published Date: Nov 2, 2019

Abstract

BACKGROUND AND AIMS: Intravascular ultrasound (IVUS)-derived morphological criteria are poor predictors of the functional significance of intermediate coronary stenosis. IVUS-based supervised machine learning (ML) algorithms were developed to identify lesions with a fractional flow reserve (FFR) ≤0.80 (vs. >0.80).

Authors

  • June-Goo Lee
    Biomedical Engineering Research Center, University of Ulsan College of Medicine, Asan Medical Center, Seoul 05505, Korea.
  • Jiyuon Ko
    2 Biomedical Engineering Research Center Asan Institute for Life Sciences Seoul Korea.
  • Hyeonyong Hae
    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.
  • 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.
  • 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.
  • Young-Hak Kim
    Asan Medical Center, Seoul, Republic of 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.

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