Evidence map›Paper›PMID 37477946›Full record

ArticleEuropace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology2023

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

Luigi Di Biase, Fengwei Zou, Aung N Lin, Vito Grupposo, Jacopo Marazzato, Nicola Tarantino, Domenico Della Rocca, Sanghamitra Mohanty, Andrea Natale, Majd Al Deen Alhuarrat and 7 more

Abstract read
In one paragraph

Article in Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

24 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Intracardiac Echocardiography during Invasive Electrophysiological Procedures: a Scientific Statement of the European Heart Rhythm Association of the ESC, and the European Association of Percutaneous Cardiovascular Interventions of the ESC.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2026
    Guideline
  2. Women with persistent atrial fibrillation need more than pulmonary vein isolation: personalised extra-pulmonary vein ablation strategy vs. pulmonary vein isolation alone in the TAILORED-AF trial.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2025
    Trial
  3. Review
  4. Artificial intelligence-intracardiac echocardiography-guided pulsed field ablation of atrial fibrillation with variable-loop circular catheter.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2026
    Article
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  9. Atrial fibrillation ablation using three-dimensional artificial intelligence module integration with intracardiac echocardiography.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2026
    Article
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  17. The Role of Imaging in Ventricular Tachycardia Ablation.Diagnostics (Basel, Switzerland) · 2025
    Review
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  19. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors.

Luigi Di BiaseMontefiore-Einstein Center for Heart & Vascular Care, Department of Cardiology, Montefiore Medical Center, Albert Einstein College of Medicine, 111 E 210th street, Bronx, NY, USA.ORCID 0000-0001-6508-4047
Fengwei ZouMontefiore-Einstein Center for Heart & Vascular Care, Department of Cardiology, Montefiore Medical Center, Albert Einstein College of Medicine, 111 E 210th street, Bronx, NY, USA.ORCID 0000-0001-6355-8843
Aung N LinMontefiore-Einstein Center for Heart & Vascular Care, Department of Cardiology, Montefiore Medical Center, Albert Einstein College of Medicine, 111 E 210th street, Bronx, NY, USA.
Vito GrupposoBiosense Webster, Inc., Irvine, CA, USA.
Jacopo MarazzatoMontefiore-Einstein Center for Heart & Vascular Care, Department of Cardiology, Montefiore Medical Center, Albert Einstein College of Medicine, 111 E 210th street, Bronx, NY, USA.ORCID 0000-0003-1205-5925
Nicola TarantinoMontefiore-Einstein Center for Heart & Vascular Care, Department of Cardiology, Montefiore Medical Center, Albert Einstein College of Medicine, 111 E 210th street, Bronx, NY, USA.
Domenico Della RoccaSt. David's Medical Center, Texas Cardiac Arrhythmia Institute, Austin, TX, USA.ORCID 0000-0003-3837-3462
Sanghamitra MohantySt. David's Medical Center, Texas Cardiac Arrhythmia Institute, Austin, TX, USA.
Andrea NataleSt. David's Medical Center, Texas Cardiac Arrhythmia Institute, Austin, TX, USA.ORCID 0000-0002-5487-0728
Majd Al Deen AlhuarratMontefiore-Einstein Center for Heart & Vascular Care, Department of Cardiology, Montefiore Medical Center, Albert Einstein College of Medicine, 111 E 210th street, Bronx, NY, USA.ORCID 0000-0003-2016-1496
Guy HaimanBiosense Webster, Inc., Irvine, CA, USA.ORCID 0009-0001-8767-1098
David HaimovichBiosense Webster, Inc., Irvine, CA, USA.ORCID 0000-0002-7883-2339
Richard A MatthewBiosense Webster, Inc., Irvine, CA, USA.ORCID 0009-0006-8877-3850
Jaclyn AlcazarBiosense Webster, Inc., Irvine, CA, USA.ORCID 0009-0008-3921-0335
Graça CostaBiosense Webster, Inc., Irvine, CA, USA.ORCID 0009-0007-9161-9173
Roy UrmanBiosense Webster, Inc., Irvine, CA, USA.
Xiaodong ZhangMontefiore-Einstein Center for Heart & Vascular Care, Department of Cardiology, Montefiore Medical Center, Albert Einstein College of Medicine, 111 E 210th street, Bronx, NY, USA.ORCID 0000-0002-2856-943X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimsIntracardiac 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

resultsIn 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.

conclusionDL 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.

Indexed as

Atrial FibrillationCatheter AblationPulmonary VeinsAlgorithmsArtificial IntelligenceEchocardiographyFeasibility StudiesHeart AtriaHumansImaging, Three-DimensionalArtificial intelligenceAtrial fibrillationCatheter ablationDeep learningIntracardiac echocardiography

Identifiers

PMID37477946
PMCPMC10403247

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.