Deep Learning-Based Recurrence Prediction of Atrial Fibrillation After Catheter Ablation.

Journal: Circulation journal : official journal of the Japanese Circulation Society
Published Date:

Abstract

BACKGROUND: Radiofrequency catheter ablation (RFCA) is an effective therapy for atrial fibrillation (AF). However, it the problem of AF recurrence remains. This study investigates whether a deep convolutional neural network (CNN) can accurately predict AF recurrence in patients with AF who underwent RFCA, and compares CNN with conventional statistical analysis.

Authors

  • Xue Zhou
    Biomedical Information Engineering Lab, The University of Aizu.
  • Keijiro Nakamura
    Division of Cardiovascular Medicine, Ohashi Medical Center, Toho University, Meguro, Tokyo, Japan.
  • Naohiko Sahara
    Division of Cardiovascular Medicine, Toho University Ohashi Medical Center.
  • Takahito Takagi
    Division of Cardiovascular Medicine, Toho University Ohashi Medical Center.
  • Yasutake Toyoda
    Division of Cardiovascular Medicine, Toho University Ohashi Medical Center.
  • Yoshinari Enomoto
    Division of Cardiovascular Medicine, Toho University Ohashi Medical Center.
  • Hidehiko Hara
    Division of Cardiovascular Medicine, Toho University Ohashi Medical Center.
  • Mahito Noro
    Division of Cardiovascular Medicine, Odawara Cardiovascular Hospital.
  • Kaoru Sugi
    Division of Cardiovascular Medicine, Odawara Cardiovascular Hospital.
  • Masao Moroi
    Division of Cardiovascular Medicine, Toho University Ohashi Medical Center.
  • Masato Nakamura
    Division of Cardiovascular Medicine, Toho University Ohashi Medical Center.
  • Xin Zhu
    Biomedical Information Engineering Lab, The University of Aizu, Fukushima, Japan.