Feasibility of a novel algorithm for automated reconstruction of the left atrial anatomy based on intracardiac echocardiography

Clinical value of artificial intelligence 3D echocardiography in evaluating left atrial volume and pulmonary vein structure in patients with atrial fibrillation

OBJECTIVE

To explore the clinical value of 3D Echocardiography (3DE) in evaluating the changes of left atrial volume and pulmonary vein structure in patients with Atrial Fibrillation (AF).

METHODS

Clinical data were collected from 54 AF patients. Left Atrial Anteroposterior Diameter (LADap), Left Atrial left and right Diameter (LADml), and Left Atrial upper and lower Diameter (LADsi) were measured; the maximum Left Atrial Volume (LAVmax), minimum Left Atrial Volume (LAVmin), left atrial presystolic volume (LAVpre), and Cross-Sectional Area (CSA) of each pulmonary vein were analyzed. Passive Ejection Fraction (LAPEF) was calculated. The differences in left atrial volume and pulmonary vein structure between patients with AF and healthy people were compared, and the correlation between the indexes was analyzed. The diagnostic value of the above indicators for AF patients was analyzed.

RESULTS

LADap, LADml, LADsi, LAVmax, LAVmin, LAVpre, LAPEF, LSPV CSA, LIPV CSA, RSPV CSA, and RIPV CSA of AF patients were significantly higher. There was a significant positive correlation between left atrial diameter and pulmonary vein structure. There was a significant positive correlation between left atrial volume and pulmonary vein structure. There was a negative correlation between LAPEF and pulmonary vein structure. LADap, LADml, LADsi, LAVmax, LAVmin, LAVpre, LAPEF, LSPV CSA, LIPV CSA, RSPV CSA, and RIPV CSA had a diagnostic value for AF patients.

CONCLUSION

3DE is applicable for evaluating left atrial volume and pulmonary vein structure in patients with AF.

Intracardiac Echocardiography: An Invaluable Tool in Electrophysiological Interventions for Atrial Fibrillation and Supraventricular Tachycardia

Researchers have investigated ways to develop optimal imaging techniques to increase the safety and effectiveness of electrophysiological (EP) procedures. Intracardiac echocardiography (ICE) is an advanced imaging tool that can directly visualize cardiac anatomical structures in high resolution, assess tissue heterogeneity and arrhythmogenic substrates, locate intracardiac catheters, monitor catheter-tissue contact and ablation injury in real-time, excluding intracardiac thrombi, and quickly detect procedural complications. Additionally, real-time imaging via ICE can be integrated with a three-dimensional (3D) electroanatomical mapping (EAM) system to reconstruct cardiac anatomy. This technique also promotes the development of zero-radiation EP procedures. Many EP studies and procedures have implemented ICE because it has several advantages over fluoroscopy and transesophageal echocardiography (TEE). ICE-guided EP procedures can be performed under conscious sedation; esophageal intubation and additional anesthesiologists are not required. Atrial fibrillation (AF) and supraventricular tachycardias (SVT) are the most common tachyarrhythmias in clinical settings. A comprehensive understanding of critical anatomical structures, such as the atrial septum, fossa ovalis (FO), and great heart vessels, is needed for the successful catheter ablation of these arrhythmias.

Feasibility of three-dimensional artificial intelligence algorithm integration with intracardiac echocardiography for left atrial imaging during atrial fibrillation catheter ablation

AIMS

Intracardiac echocardiography (ICE) is a useful but operator-dependent tool for left atrial (LA) anatomical rendering during atrial fibrillation (AF) ablation. The CARTOSOUND FAM Module, a new deep learning (DL) imaging algorithm, has the potential to overcome this limitation. This study aims to evaluate feasibility of the algorithm compared to cardiac computed tomography (CT) in patients undergoing AF ablation.

METHODS AND RESULTS

In 28 patients undergoing AF ablation, baseline patient information was recorded, and three-dimensional (3D) shells of LA body and anatomical structures [LA appendage/left superior pulmonary vein/left inferior pulmonary vein/right superior pulmonary vein/right inferior pulmonary vein (RIPV)] were reconstructed using the DL algorithm. The selected ultrasound frames were gated to end-expiration and max LA volume. Ostial diameters of these structures and carina-to-carina distance between left and right pulmonary veins were measured and compared with CT measurements. Anatomical accuracy of the DL algorithm was evaluated by three independent electrophysiologists using a three-anchor scale for LA anatomical structures and a five-anchor scale for LA body. Ablation-related characteristics were summarized. The algorithm generated 3D reconstruction of LA anatomies, and two-dimensional contours overlaid on ultrasound input frames. Average calculation time for LA reconstruction was 65 s. Mean ostial diameters and carina-to-carina distance were all comparable to CT without statistical significance. Ostial diameters and carina-to-carina distance also showed moderate to high correlation (r = 0.52-0.75) except for RIPV (r = 0.20). Qualitative ratings showed good agreement without between-rater differences. Average procedure time was 143.7 ± 43.7 min, with average radiofrequency time 31.6 ± 10.2 min. All patients achieved ablation success, and no immediate complications were observed.

CONCLUSION

DL algorithm integration with ICE demonstrated considerable accuracy compared to CT and qualitative physician assessment. The feasibility of ICE with this algorithm can potentially further streamline AF ablation workflow.

The Use of Artificial Intelligence for Detecting and Predicting Atrial Arrhythmias Post Catheter Ablation

Catheter ablation (CA) is considered as one of the most effective methods technique for eradicating persistent and abnormal cardiac arrhythmias. Nevertheless, in some cases, these arrhythmias are not treated properly, resulting in their recurrences. If left untreated, they may result in complications such as strokes, heart failure, or death. Until recently, the primary techniques for diagnosing recurrent arrhythmias following CA were the findings predisposing to the changes caused by the arrhythmias on cardiac imaging and electrocardiograms during follow-up visits, or if patients reported having palpitations or chest discomfort after the ablation. However, these follow-ups may be time-consuming and costly, and they may not always determine the root cause of the recurrences. With the introduction of artificial intelligence (AI), these follow-up visits can be effectively shortened, and improved methods for predicting the likelihood of recurring arrhythmias after their ablation procedures can be developed. AI can be divided into two categories: machine learning (ML) and deep learning (DL), the latter of which is a subset of ML. ML and DL models have been used in several studies to demonstrate their ability to predict and identify cardiac arrhythmias using clinical variables, electrophysiological characteristics, and trends extracted from imaging data. AI has proven to be a valuable aid for cardiologists due to its ability to compute massive amounts of data and detect subtle changes in electric signals and cardiac images, which may potentially increase the risk of recurrent arrhythmias after CA. Despite the fact that these studies involving AI have generated promising outcomes comparable to or superior to human intervention, they have primarily focused on atrial fibrillation while atrial flutter (AFL) and atrial tachycardia (AT) were the subjects of relatively few AI studies. Therefore, the aim of this review is to investigate the interaction of AI algorithms, electrophysiological characteristics, imaging data, risk score calculators, and clinical variables in predicting cardiac arrhythmias following an ablation procedure. This review will also discuss the implementation of these algorithms to enable the detection and prediction of AFL and AT recurrences following CA.

Feasibility of a novel algorithm for automated reconstruction of the left atrial anatomy based on intracardiac echocardiography

BACKGROUND

Intracardiac echocardiography (ICE) is frequently used to guide electrophysiology procedures. The novel automated algorithm Cartosoundfam is a model-based algorithm which reconstructs a 3D anatomy of the left atrium (LA) based on a set of 2D intracardiac echocardiography (ICE) frames, without the need to manually annotate ultrasound (US) contours.

OBJECTIVE

The aim of this study was to determine the feasibility of the Cartosoundfam module in routine clinical setting.

METHODS

We included 16 patients undergoing LA mapping/catheter ablation. Two-dimensional US frames were acquired from the right atrium (RA) and the right ventricular outflow tract. The Cartosoundfam map was validated in two steps: (1) identification of anatomical structures (pulmonary veins [PV] and LA body and appendage) by alignment of the ablation catheter to the automated map; and (2) analysis of the automated lesion tags (Visitag) location in relation to the PV antrum of the Cartosoundfam map in nine patients with paroxysmal atrial fibrillation (AF) undergoing first time pulmonary vein isolation (PVI).

RESULTS

Mean 2D US frames per patient were 29 ± 6 and acquisition time was 16 ± 4 min. All anatomical structures were correctly identified in all patients (step 1). In the step 2 validation, the median distance to the map was 2.0 (IQR: 2.4) mm and the majority of the Visitags were classified as satisfactory (69%) but all PV segments had some Visitags classified as unsatisfactory.

CONCLUSION

The automated ICE-based algorithm correctly identified the LA anatomical structures in all patients with a 69% anatomical accuracy of the Visitags alignments to the PV antrum segments.