Validation of Automated Low Voltage Area Measurement Using CARTONET in Atrial Fibrillation Patients

Yuncong Mao; Tarif Bitar; Henning Horlitz; Syed Yusuf Ali; Don Yungher; Marc Horlitz; Natalia Trayanova; Florian Stöckigt; Yazan Mohsen

doi:10.1111/pace.70313

Validation of Automated Low Voltage Area Measurement Using CARTONET in Atrial Fibrillation Patients

Pacing Clin Electrophysiol. 2026 Jun 9. doi: 10.1111/pace.70313. Online ahead of print.

Authors

Yuncong Mao^{1

2}, Tarif Bitar^{3

4}, Henning Horlitz³, Syed Yusuf Ali¹, Don Yungher⁵, Marc Horlitz^{3

4}, Natalia Trayanova¹, Florian Stöckigt^{3

6}, Yazan Mohsen¹

Affiliations

¹ Department of Biomedical Engineering, Johns Hopkins Medicine, Baltimore, Maryland, USA.
² Stanford School of Medicine, Palo Alto, California, USA.
³ Department of Cardiology, Krankenhaus Porz am Rhein, Cologne, Germany.
⁴ Department of Cardiology, Faculty of Health, School of Medicine, University Witten/Herdecke, Witten, Germany.
⁵ Biosense Webster Ltd., Haifa Technology Center, Haifa, Israel.
⁶ Department of Internal Medicine II, University Hospital Bonn, Bonn, Germany.

PMID: 42265887
DOI: 10.1111/pace.70313

Abstract

Background: Low-voltage areas (LVAs) detected via electroanatomic mapping (EAM) are key indicators of atrial remodeling and predictors of atrial fibrillation (AF) recurrence following pulmonary vein isolation (PVI). However, manual LVA quantification is time-intensive, operator-dependent, and prone to variability.

Objective: To validate the accuracy, consistency, and efficiency of automatic LVA quantification function of CARTONET-a cloud-based automated platform for LVA quantification-by comparing it with conventional manual analysis in AF patients.

Methods: This retrospective study included 100 patients who underwent PVI for AF at a single center between January 2022 and December 2023. Manual LVA measurements were performed by experts using CARTO3. LVA quantification and anatomical segmentation were performed using CARTONET. LVA was stratified into four categories: low (<5%), mild (5%-20%), moderate (20%-35%), and severe (>35%). Statistical analyses included Spearman's rank correlation (ρ), Bland-Altman agreement, and confusion matrices for categorical classification, with subgroup analysis based on segmentation errors and map point density. The number and the locations of segmentation errors were quantified.

Results: CARTONET demonstrated strong correlation with manual measurements across all LA regions (ρ = 0.928-0.983, p < 0.01). Significant differences were observed in anterior and posterior walls (p < 0.01), especially in low-density maps. In high-density maps, no significant differences were observed (p ≥ 0.07). Bland-Altman analysis showed minimal bias (mean difference: -0.8%), and 96% of values fell within limits of agreement. The confusion matrix showed that categorical agreement for LVA stratification exceeded 90%. Segmentation errors were most common at the LSPV but did not significantly impact LVA quantification.

Conclusion: CARTONET offers reliable, efficient LVA quantification with high concordance to manual methods. Its performance in high-quality maps supports its use in clinical and research settings, reducing operator workload and promoting standardized substrate evaluation.

Keywords: CARTONET; atrial fibrillation; catheter ablation; electroanatomical mapping.

Publication types

Review