Abstract

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Title:: Prediction of recurrence after catheter ablation in patients with atrial fibrillation based on convolutional neural network

Author(s):: XU Liang1; TAO Qian1; ZHONG Jing2; XIAO Jingjing1; 1. Department of Medical Engineering, the Second Affiliated Hospital of Army Medical University, Chongqing 400037, China 2. Department of Cardiology, the Second Affiliated Hospital of Army Medical University, Chongqing 400037, China

Keywords:: Keywords: atrial fibrillation catheter ablation convolutional neural network postoperative recurrence

Abstract:: Abstract: The basic medical history, and the results of imaging examination and biochemical examination of patients before operation are collected for predicting the prognosis of catheter ablation by the combination of statistics and convolution neural network. A total of 121 patients with atrial fibrillation (AF) after radiofrequency ablation were enrolled in this study. The 60 indexes of biochemical examination are used for deep learning to establish 3 different prediction models of AF recurrence by adjusting the structure and parameters, and a recurrence prediction accuracy up to 0.7 is achieved (95% CI: 0.536-0.864). Then, statistical screening and data standardization are performed on the characteristic information of basic medical history and image examination information.According to the P value, the 10 features with the largest difference are combined with the 60 features of biochemical examination to carry out multi-factor cross-modal in-depth learning. The highest accuracy of the AF recurrence prediction model obtained from the 3 models reaches 0.8 (95%CI: 0.657-0.943). Through multiple groups of experiments, it is found that the deep learning model is not the more complex the better. In the case of limited sample size, selecting a reasonable model complexity and incorporating multiple modal features can obtain higher prediction accuracy.