Machine learning assessment of myocardial ischemia using angiography: Development and retrospective validation.

Journal: PLoS medicine
Published Date: Nov 1, 2018

Abstract

BACKGROUND: Invasive fractional flow reserve (FFR) is a standard tool for identifying ischemia-producing coronary stenosis. However, in clinical practice, over 70% of treatment decisions still rely on visual estimation of angiographic stenosis, which has limited accuracy (about 60%-65%) for the prediction of FFR < 0.80. One of the reasons for the visual-functional mismatch is that myocardial ischemia can be affected by the supplied myocardial size, which is not always evident by coronary angiography. The aims of this study were to develop an angiography-based machine learning (ML) algorithm for predicting the supplied myocardial volume for a stenosis, as measured using coronary computed tomography angiography (CCTA), and then to build an angiography-based classifier for the lesions with an FFR < 0.80 versus ≥ 0.80.

Authors

  • 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.
  • Won-Jang Kim
    Department of Cardiology, CHA Bundang Medical Center, CHA University, Seongnam, Korea.
  • So-Yeon Choi
    Department of Cardiology, Ajou University, Suwon, Korea.
  • June-Goo Lee
    Biomedical Engineering Research Center, University of Ulsan College of Medicine, Asan Medical Center, Seoul 05505, Korea.
  • Youngoh Bae
    School of Medicine, CHA University, Seongnam-si, Gyeonggi-do, South Korea.
  • Hyungjoo Cho
    Department of Cardiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.
  • Dong Hyun Yang
    Department of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.
  • Joon-Won Kang
    Department of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.
  • Tae-Hwan Lim
    Department of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.
  • Cheol Hyun Lee
    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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