ReviewHerzschrittmachertherapie & Elektrophysiologie2026
[Wolff-Parkinson-White syndrome : Comparison of different algorithms].
Review in Herzschrittmachertherapie & Elektrophysiologie, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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.
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.
Who cites it
1 citing paper in PubMed.
- Artificial intelligence in pediatric arrhythmias: current landscape, unique challenges, and translational perspectives.Frontiers in pediatrics · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWolff-Parkinson-White (WPW) syndrome is characterised by accessory pathways that bypass the normal atrioventricular conduction system. Precise preprocedural localisation is pivotal for optimising ablation strategy, minimising complications, and reducing radiation exposure.
methodsThis review systematically analyses ECG-based algorithms for accessory pathway localisation, including classical and recent rule-based approaches, as well as modern deep learning models.
resultsClassical algorithms showed variable accuracy ranging from 72% (Milstein) to 92% (D'Avila, St. George). Modern rule-based algorithms demonstrate significantly improved performance: EASY-WPW achieved 93% accuracy (sensitivity 92%, specificity 99%), and SMART-WPW reached 97% (sensitivity 96%, specificity 100%) using a 12-location clock-face model. DL approaches achieved an 84% accuracy with AUROC 0.92, significantly outperforming classical algorithms (Milstein AUROC 0.81, Arruda AUROC 0.80). The DL model enables automatic analysis, reduces interobserver variability, and identifies parahisian pathways and locations requiring transseptal puncture. Both EASY-WPW and SMART-WPW showed excellent results in pediatric populations.
conclusionsBoth validated ECG algorithms and deep learning models represent valuable tools for preinterventional planning in patients with WPW syndrome. Modern rule-based algorithms offer excellent diagnostic accuracy with sensitivities and specificities exceeding 90%. The integration of artificial intelligence (AI) and multimodal approaches promises further improvements in accessory pathway localisation.
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Identifiers
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Registered trials
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.