Deep learning-based workflow for automatic extraction of atria and epicardial adipose tissue on cardiac computed tomography in atrial fibrillation.

BACKGROUND: Preoperative estimation of the volume of the left atrium (LA) and epicardial adipose tissue (EAT) on computed tomography (CT) images is associated with an increased risk of atrial fibrillation (AF) recurrence. We aimed to design a deep learning-based workflow to provide reliable automatic segmentation of the atria, pericardium, and EAT for future applications in the management of AF. METHODS: This study enrolled 157 patients with AF who underwent first-time catheter ablation between January 2015 and December 2017 at Taipei Veterans General Hospital. Three-dimensional (3D) U-Net models of the LA, right atrium (RA), and pericardium were used to develop a pipeline for total, LA-EAT, and RA-EAT automatic segmentation. We defined fat within the pericardium as tissue with attenuation between -190 and -30 HU and quantified the total EAT. Regions between the dilated endocardial boundaries and endocardial walls of the LA or RA within the pericardium were used to detect voxels attributed to fat, thus estimating LA-EAT and RA-EAT. RESULTS: The LA, RA, and pericardium segmentation models achieved Dice coefficients of 0.960 ± 0.010, 0.945 ± 0.013, and 0.967 ± 0.006, respectively. The 3D segmentation models correlated well with the ground truth for the LA, RA, and pericardium ( r = 0.99 and p < 0.001 for all). The Dice coefficients of our proposed method for EAT, LA-EAT, and RA-EAT were 0.870 ± 0.027, 0.846 ± 0.057, and 0.841 ± 0.071, respectively. CONCLUSION: Our proposed workflow for automatic LA, RA, and EAT segmentation using 3D U-Nets on CT images is reliable in patients with AF.

Feasibility of Auto-Quantified Epicardial Adipose Tissue in Predicting Atrial Fibrillation Recurrence After Catheter Ablation.

BACKGROUND: The aim of this study was to build an auto-segmented artificial intelligence model of the atria and epicardial adipose tissue (EAT) on computed tomography (CT) images, and examine the prognostic significance of auto-quantified left atrium (LA) and EAT volumes for AF.Methods and Results: This retrospective study included 334 patients with AF who were referred for catheter ablation (CA) between 2015 and 2017. Atria and EAT volumes were auto-quantified using a pre-trained 3-dimensional (3D) U-Net model from pre-ablation CT images. After adjusting for factors associated with AF, Cox regression analysis was used to examine predictors of AF recurrence. The mean (±SD) age of patients was 56±11 years; 251 (75%) were men, and 79 (24%) had non-paroxysmal AF. Over 2 years of follow-up, 139 (42%) patients experienced recurrence. Diabetes, non-paroxysmal AF, non-pulmonary vein triggers, mitral line ablation, and larger LA, right atrium, and EAT volume indices were linked to increased hazards of AF recurrence. After multivariate adjustment, non-paroxysmal AF (hazard ratio [HR] 0.6; 95% confidence interval [CI] 0.4-0.8; P=0.003) and larger LA-EAT volume index (HR 1.1; 95% CI 1.0-1.2; P=0.009) remained independent predictors of AF recurrence. CONCLUSIONS: LA-EAT volume measured using the auto-quantified 3D U-Net model is feasible for predicting AF recurrence after CA, regardless of AF type.

Electrocardiographic and electrophysiologic characteristics of midseptal accessory pathways.

BACKGROUND: The purpose of the present study was to investigate the electrocardiographic and electrophysiologic characteristics of right midseptal (RMS) and left midseptal (LMS) accessory pathways (APs), and to develop a stepwise algorithm to differentiate RMS from LMS APs. METHODS AND RESULTS: From May 1989 to February 2004, 1591 patients with AP-mediated tachyarrhythmia underwent RF catheter ablation in this institution, and 38 (2.4%) patients had MS APs. The delta wave and precordial QRS transition during sinus rhythm, retrograde P wave during orthodromic tachycardia, and electrophysiologic characteristic and catheter ablation in 30 patients with RMS APs and 8 patients with LMS APs were analyzed. There was no significant difference in electrophysiologic characteristics and catheter ablation between RMS and LMS APs. The polarity of retrograde P wave during orthodromic tachycardia also showed no statistical difference between patients with RMS and LMS APs. The delta wave polarity was positive in leads I, aVL, and V3 to V6 in patients with RMS and LMS APs. Patients with LMS APs had a higher incidence of biphasic delta wave in lead V1 than patients with RMS APs (80% vs. 15%, P=0.012). The distributions of precordial QRS transition were different between RMS APs (leads V2; n = 10, V3; n = 7 and V4; n = 3) and LMS APs (leads V1; n = 1 and V2; n = 4) (P = 0.03). The combination of a delta negative wave in lead V1 or precordial QRS transition in lead V3 or V4 had a sensitivity of 90%, specificity of 80%, positive predictive value of 95%, and negative predictive value of 66% in predicting an RMS AP. CONCLUSIONS: Delta wave polarity in lead V1 and precordial QRS transition may differentiate RMS and LMS APs.