Extensible artificial intelligence model predicts post-ablation AF recurrence using coronary sinus electrogram

Abstract

Abstract Background Atrial fibrillation (AF) is a major public health problem with significant adverse outcomes and catheter ablation is a widely adopted treatment. The CABANA trial showed that catheter ablation reduced AF recurrence to a greater extent than medications. However, some of patients who underwent this procedure still experience relapse. Here, we present an innovative way to identify this subgroup using an artificial intelligence (AI) -assisted coronary sinus electrogram. Hypothesis Our hypothesis is that credible features in the electrogram can be extracted by AI for prediction, therefore rigorous drug administration, close follow-up or potential second procedure can be applied to these patients. Methods 67 patients from two independent hospitals (SPH & ZSH) with non-valvular persistent AF undergoing circumferential pulmonary vein isolation were enrolled in this study, 23 of which experienced recurrence 6 months after the procedure. We collected standard 2.5-second fragments of coronary sinus electrogram from ENSITE NAVX (SPH) and Carto (ZSH)system before the ablation started. A total of 1429 fragments were obtained and a transfer learning-based ResNet model was employed in our study. Fragments from ZSH were used for training and SPH for validation of deep convolutional neural networks (DCNN). The AI model performance was evaluated by accuracy, recall, precision, F-Measure and AUC. Results The prediction accuracy of the DCNN in single center reached 96%, while that in different ablation systems reached 74.3%. Also, the algorithm yielded values for the AUC, recall, precision and F-Measure of 0.76, 86.1%, 95.9% and 0.78, respectively, which shows satisfactory classification results and extensibility in different cardiology centers and brands of electroanatomic mapping instruments. Conclusions Our work has revealed the potential intrinsic correlation between coronary sinus electrical activity and AF recurrence using DCNN-based model. Moreover, the DCNN model we developed shows great prospects in the relapse prediction for personalized post-procedural management. Funding Acknowledgement Type of funding source: Foundation. Main funding source(s): The National Natural Science Foundation of China