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

Article Cardiac & Cardiovascular Systems

Visualization and mapping of the right phrenic nerve by intracardiac echocardiography during atrial fibrillation ablation

Xinmeng Liu et al.

Summary: This study aimed to evaluate the feasibility of real-time visualization and mapping of the right phrenic nerve (RPN) by using intracardiac echocardiography (ICE) during atrial fibrillation (AF) ablation. The RPN was successfully visualized by ICE in 92% of patients and its distance from the superior vena cava (SVC) was found to have a positive linear correlation with its pacing capture threshold. Guiding SVC isolation by visualizing RPN with ICE was safe and resulted in no RPN injuries.

EUROPACE (2023)

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Article Cardiac & Cardiovascular Systems

An artificial intelligence-based model for prediction of atrial fibrillation from single-lead sinus rhythm electrocardiograms facilitating screening

Tove Hygrell et al.

Summary: The study aimed to observe the accuracy of an artificial intelligence-based network in predicting paroxysmal atrial fibrillation from a normal sinus rhythm single-lead ECG. The algorithm performed well in predicting paroxysmal AF in a wide age range, but had lower performance in age-homogeneous groups.

EUROPACE (2023)

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Article Cardiac & Cardiovascular Systems

AFA-Recur: an ESC EORP AFA-LT registry machine-learning web calculator predicting atrial fibrillation recurrence after ablation

Andrea Saglietto et al.

Summary: This study aimed to develop a machine-learning scoring system based on easily accessible clinical variables from the ESC-EHRA Atrial Fibrillation Ablation Long-Term Registry to predict the probability of 1-year arrhythmic recurrence after catheter ablation for AF patients. Among four different supervised machine-learning models, the random forest model achieved the best performance with a 19-variable version, outperforming existing scores. The final calibrated model, available as a web calculator, showed significantly better predictive performance.

EUROPACE (2023)

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Article Cardiac & Cardiovascular Systems

Hybrid machine learning to localize atrial flutter substrates using the surface 12-lead electrocardiogram

Giorgio Luongo et al.

Summary: This study demonstrates the potential of using machine learning algorithms to identify the location of atrial flutter mechanisms based on non-invasive signals, which could aid in planning and tailoring individualized treatments for atrial flutter.

EUROPACE (2022)

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Article Cardiac & Cardiovascular Systems

Budget impact analysis of a machine learning algorithm to predict high risk of atrial fibrillation among primary care patients

Tomasz Szymanski et al.

Summary: This study investigated whether the use of an AF risk prediction algorithm could improve AF detection in primary care and assessed the associated budget impact. The results showed that implementing the algorithm alongside standard opportunistic screening could close the AF detection gap, prevent strokes, and substantially reduce healthcare costs.

EUROPACE (2022)

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Article Cardiac & Cardiovascular Systems

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

Finn Akerstrom et al.

Summary: This study aimed to determine the feasibility of the Cartosoundfam module in routine clinical setting for LA mapping/catheter ablation. The results showed that the automated algorithm correctly identified LA anatomical structures in all patients, with a 69% anatomical accuracy in terms of PV antrum alignment.

PACE-PACING AND CLINICAL ELECTROPHYSIOLOGY (2022)

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Article Cardiac & Cardiovascular Systems

Integrated 3D intracardiac ultrasound imaging with detailed pulmonary vein delineation guided fluoroless ablation of atrial fibrillation

Bor Antolic et al.

Summary: This study demonstrated that fluoroless ablation of atrial fibrillation guided by the ICE/EAM automatic integration system is a feasible, safe, and acutely effective method for treating symptomatic AF. This approach can achieve successful PVI without the use of fluoroscopy, making it a valuable option for AF treatment.

PACE-PACING AND CLINICAL ELECTROPHYSIOLOGY (2021)

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Article Cardiac & Cardiovascular Systems

Intracardiac echocardiography guided nonocclusive balloon cryothermal applications to achieve antral isolation during pulmonary vein isolation

Moussa Saleh et al.

Summary: Nonocclusive cryoballoon applications are commonly required to achieve antral isolation for atrial fibrillation, especially in cases where left PVs > 19.4 +/- 2.9 mm, right superior PV > 20.2 +/- 4.7mm, funnel-shaped PVs, and right PVs not converging to a carina. The use of ICE can help accurately locate the balloon and predict the level of isolation by voltage mapping.

JOURNAL OF INTERVENTIONAL CARDIAC ELECTROPHYSIOLOGY (2021)

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Review Cardiac & Cardiovascular Systems