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. 2025 Nov;68(8):1683-1693.
doi: 10.1007/s10840-025-02083-y. Epub 2025 Jun 6.

AI-guided spatiotemporal dispersion mapping for individualized ablation in an all-comer cohort with atrial fibrillation

Emanuel Heil  1   2 , Nikolaos Dagres  1 , Leif-Hendrick Boldt  1   2 , Abdul Parwani  1   2 , Florian Blaschke  1   2 , Doreen Schoeppenthau  1 , Philipp Attanasio  1   2 , Robert Hättasch  1 , Verena Tscholl  1   2 , Gerhard Hindricks  1   2 , Jin-Hong Gerds-Li  1 , Felix Hohendanner  3   4
Affiliations

AI-guided spatiotemporal dispersion mapping for individualized ablation in an all-comer cohort with atrial fibrillation

Emanuel Heil et al. J Interv Card Electrophysiol. 2025 Nov.

Abstract

Background: Artificial intelligence (AI)-guided spatiotemporal dispersion (stD) mapping has been shown to improve outcomes in patients with persistent atrial fibrillation (AF). However, the relationship between stD mapping and markers of atrial cardiomyopathy, dispersion patterns in paroxysmal versus persistent AF, stability of dispersion regions, and stD-guided ablation-related outcomes in all-comer cohorts remain elusive.

Methods: In this retrospective single-center analysis, AF patients underwent high-density electroanatomical mapping alongside multiple instances of stD mapping using VOLTA AF Explorer software. Pulmonary vein isolation (PVI) and targeted ablation of left atrial dispersion regions were performed. Clinical, echocardiographic, biomarker, and low-voltage area (LVA) data were collected as markers of left atrial remodeling.

Results: stD mapping identified dispersion in 92% of patients. Mean time since AF diagnosis was 7 ± 1 years. Overall, 58% of patients showed dispersion exclusively co-localizing with low-voltage areas, while 42% had dispersion extending into intermediate or normal voltage regions. Dispersion burden correlated strongly with LVA extent and other remodeling markers such as NT-proBNP and LAVI. Persistent AF patients exhibited a significantly higher number of dispersion sites compared to paroxysmal AF. Dispersion patterns remained largely consistent before and after cardioversion in persistent AF, with the posterior left atrial wall emerging as a common hotspot. At follow-up, AF recurred in 33% of paroxysmal and 60% of persistent AF patients who had dispersion ablation limited to the left atrium. Despite these recurrences, most patients reported an improvement in symptomatic burden.

Conclusion: AI-guided stD mapping effectively identifies atrial remodeling beyond classical voltage-derived substrate, supporting its potential as a useful adjunctive tool in AF characterization.

Keywords: Artificial intelligence; Atrial fibrillation; Catheter ablation; Spatiotemporal dispersion; Substrate mapping.

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Conflict of interest statement

Declarations. Competing interests: The authors declare that they have no competing interests relevant to the content of this article.

Figures

Fig. 1
Fig. 1
A Protocol used for voltage and dispersion mapping and ablation with RF-energy in patients with paroxysmal and persistent atrial fibrillation. B Retrospective clinical characteristics of the respective patient groups
Fig. 2
Fig. 2
A 3D electroanatomic mapping (SR) example of paroxysmal (left) and persistent AF patients. Dispersion highlighted by black circle (full dispersion) for paroxysmal and white/blue points for persistent AF. B Quantitative analysis of dispersion points and left atrial low voltage areas in paroxysmal and persistent AF patients (top) as well as relation between LAVI and LAP in the cohort stratified by % of LVA (bottom). C Quantification of LVA and dispersion points in paroxysmal and persistent AF (left). Association of dispersion with LVA and mapping time for persistent AF dispersion map #1 and dispersion map #2 (right)
Fig. 3
Fig. 3
A. Quantitative distribution of LVA (i.e. LA voltage 0.5 mV), LV strain and LAVI in no dispersion vs dispersion positive patients. B. AF recurrence after PVI(+) in paroxysmal and persistent AF patients. C. Number of previous LA ablations in the cohort of paroxysmal and persistent AF patients. D. % of patients reporting the respective EHRA classification symptoms before ablation and at the end of retrospective follow-up
Fig. 4
Fig. 4
A. Example 3D electroanatomic map of a patient with paroxysmal AF, blue and white dots highlight regions of full and intermediate dispersion, respectively. Distribution of full dispersion (B) and all dispersion points (C) in patients with paroxysmal AF, blue and white dots highlight regions of full and intermediate dispersion, respectively
Fig. 5
Fig. 5
Example distribution of all dispersion points in a patient with persistent AF during the 1st (A) and 2nd (C) dispersion map. B. Voltage map in the example patient. D and E. Quantitative distribution of dispersion points at the anterior wall, the roof and the posterior wall in all patients with persistent atrial fibrillation during 1st and 2nd dispersion maps, blue and white dots highlight regions of full and intermediate dispersion, respectively
See this image and copyright information in PMC

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