A 2-year investigation of the impact of the computed tomography-derived fractional flow reserve calculated using a deep learning algorithm on routine decision-making for coronary artery disease management.

Journal: European radiology
Published Date: Sep 1, 2021

Abstract

OBJECTIVE: This study aims to investigate the safety and feasibility of using a deep learning algorithm to calculate computed tomography angiography-based fractional flow reserve (DL-FFRCT) as an alternative to invasive coronary angiography (ICA) in the selection of patients for coronary intervention.

Authors

  • Xin Liu
    Peking University Institute of Advanced Agricultural Sciences, Shandong Laboratory of Advanced Agricultural Sciences, Weifang, Shandong, China.
  • Xukai Mo
    Medical Imaging Center, The First Affiliated Hospital of Jinan University, No 613 Huangpu Dadao West, Guangzhou, 510630, China.
  • Heye Zhang
    School of Biomedical Engineering, Sun Yat-sen University, Shenzhen, China.
  • Guang Yang
    National Heart and Lung Institute, Imperial College London, London, UK.
  • Changzheng Shi
    Medical Imaging Center, The First Affiliated Hospital of Jinan University, No 613 Huangpu Dadao West, Guangzhou, 510630, China. tsczcn@jnu.edu.cn.
  • William Kongtou Hau
    Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, 30-32 Ngan Shing St., Sha Tin, Hong Kong, SAR, China.

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