A deep learning model to predict recurrence of atrial fibrillation after pulmonary vein isolation

Abstract

Background and Objectives The efficacy of radiofrequency catheter ablation (RFCA) in atrial fibrillation (AF) is well established. The standard approach to RFCA in AF is pulmonary vein isolation (PVI). However, a large proportion of patients experiences recurrence of atrial tachyarrhythmia. The purpose of this study is to find out whether the AI model can assess AF recurrence in patients who underwent PVI. Materials and methods This study was a retrospective cohort study that enrolled consecutive patients who underwent catheter ablation for symptomatic, drug-refractory AF and PVI. We developed an AI algorithm to predict recurrence of AF after PVI using patient demographics and three-dimensional (3D) reconstructed left atrium (LA) images. Results We included 527 consecutive patients in the study. The overall mean LA diameter was 42.0 ± 6.8 mm, and the mean LA volume calculated using 3D reconstructed images was 151.1 ± 46.7 ml. During the follow-up period, atrial tachyarrhythmia recurred in 158 patients. The area under the curve (AUC) of the AI model based on a convolutional neural network (including 3D reconstruction images) was 0.61 (95% confidence interval [CI] 0.53–0.74) using the test dataset. The total test accuracy was 66.3% (57.0–75.6), and the sensitivity was 53.3% (34.8–71.9). The specificity was 73.2% (51.8–75.0), and the F1 score was 52.5% 34.5–66.7). Conclusion In this study, we developed an AI algorithm to predict recurrence of AF after catheter ablation of PVI using individual reconstructed LA images. This AI model was unable to predict recurrence of AF overwhelmingly; therefore, further large-scale study is needed.

Exploring interpretability in deep learning prediction of successful ablation therapy for atrial fibrillation

“…Predicting RFCA outcomes from imaging data is a challenging task, as shown by Kim et al, who predicted AF recurrence post-RFCA with a 0.61 accuracy from a CNN which used a combination of MRI data and patient demographics ( Kim et al, 2020 ). Moreover, Roney et al applied machine learning to predict in silico AF recurrence after multiple ablation strategies ( Roney et al, 2018 ; Roney et al, 2020 ).…”

“…With the recent rise of artificial intelligence (AI), machine and deep learning (DL) have been applied to patient medical imaging data and computational cardiac modelling with the aim to develop more effective RFCA treatments. The applications of AI include predicting AF reoccurrence post-RFCA and the origins of AF triggers and ablation ( Kim et al, 2020 ; Liu et al, 2020 ; Firouznia et al, 2021 ; Roney et al, 2022 ). Furthermore, Luongo et al have applied machine learning to predict AF ablation targets, but used 12-lead ECG data instead of medical imaging ( Luongo et al, 2021 ).…”

The Effect of Discharge Mode on the Distribution of Myocardial Pulsed Electric Field—A Simulation Study for Pulsed Field Ablation of Atrial Fibrillation

“…Atrial fibrillation (AF) is a common arrhythmia in which the incidence rate increases with age [1,2]. To date, ablation has become one of the most effective methods for the treatment of AF, and circular pulmonary vein vestibular isolation is its basic operation [3]. The forms of ablation energy include radio frequency, freezing, and pulsed electric field.…”