A method using deep learning to discover new predictors from left-ventricular mechanical dyssynchrony for CRT response.

Journal: Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology
PMID: 35915327

Abstract

BACKGROUND: Studies have shown that the conventional parameters characterizing left ventricular mechanical dyssynchrony (LVMD) measured on gated SPECT myocardial perfusion imaging (MPI) have their own statistical limitations in predicting cardiac resynchronization therapy (CRT) response. The purpose of this study is to discover new predictors from the polarmaps of LVMD by deep learning to help select heart failure patients with a high likelihood of response to CRT.

Authors

  • Zhuo He
    College of Computing, Michigan Technological University, 1400 Townsend Drive, Houghton, MI, USA.
  • Xinwei Zhang
    Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Guangzhou Road 300, Nanjing, 210029, Jiangsu, China.
  • Chen Zhao
    Department of Ophthalmology, Fudan Eye & ENT Hospital, Shanghai, China.
  • Xing Ling
    Department of Mathematical Sciences, Michigan Technological University, Houghton, MI, USA.
  • Saurabh Malhotra
    Division of Cardiology, Cook County Health and Hospitals System, Chicago, IL, USA.
  • Zhiyong Qian
    Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.
  • Yao Wang
    Department of Gastrointestinal Surgery, Zhongshan People's Hospital, Zhongshan, Guangdong, China.
  • Xiaofeng Hou
    Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.
  • Jiangang Zou
    Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.
  • Weihua Zhou
    School of Computing, University of Southern Mississippi, Hattiesburg, MS, United States of America.

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