Deep learning analysis in coronary computed tomographic angiography imaging for the assessment of patients with coronary artery stenosis.

Journal: Computer methods and programs in biomedicine
Published Date: Jul 9, 2020

Abstract

BACKGROUND AND OBJECTIVE: Recently, deep convolutional neural network has significantly improved image classification and image segmentation. If coronary artery disease (CAD) can be diagnosed through machine learning and deep learning, it will significantly reduce the burdens of the doctors and accelerate the critical patient diagnoses. The purpose of the study is to assess the practicability of utilizing deep learning approaches to process coronary computed tomographic angiography (CCTA) imaging (termed CCTA-artificial intelligence, CCTA-AI) in coronary artery stenosis.

Authors

  • Dan Han
    College of Life Science and Bio-engineering, Beijing University of Technology, Beijing, 100124, China.
  • Jiayi Liu
    Beijing University of Chinese Medicine, China-Japan Friendship Clinical School of Medicine, Beijing, 100029, People's Republic of China.
  • Zhonghua Sun
    Beijing Univ. of Technology, China.
  • Yu Cui
    Shukun (Beijing) Technology Co., Ltd, China.
  • Yi He
    National Institutes for Food and Drug Control, 2 Tiantan Xili, Beijing 100050, China.
  • Zhenghan Yang
    Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China.

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