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Identifying the Location of an Accessory Pathway in Pre-Excitation Syndromes Using an Artificial Intelligence-Based

Thomas Senoner1, Bernhard Pfeifer2,3, Fabian Barbieri1

  • 1University Clinic of Internal Medicine III (Cardiology and Angiology), Medical University Innsbruck, 6020 Innsbruck, Austria.

Journal of Clinical Medicine
|October 13, 2021
PubMed
Summary

An artificial intelligence (AI) algorithm accurately predicts accessory pathway (AP) location in Wolff-Parkinson-White (WPW) syndrome using 12-lead ECGs. This AI tool offers a non-invasive alternative to electrophysiologic studies for precise AP localization.

Keywords:
Wolff–Parkinson–White syndromeaccessory pathwaysalgorithmsartificial intelligencecardiac electrophysiologycatheter ablation

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Area of Science:

  • Cardiology
  • Artificial Intelligence in Medicine
  • Medical Diagnostics

Background:

  • Accurate localization of accessory pathways (APs) in Wolff-Parkinson-White (WPW) syndrome is crucial for successful ablation.
  • Current methods like invasive electrophysiologic studies carry risks, while ECG-based methods have limited accuracy.
  • Developing a non-invasive, accurate tool for AP localization is a significant clinical need.

Purpose of the Study:

  • To develop and validate an artificial intelligence (AI)-based algorithm, locAP AI, for precise localization of APs in WPW syndrome using 12-lead ECGs.
  • To compare the diagnostic accuracy of the AI algorithm against established ECG-based algorithms.
  • To assess the potential of AI in improving non-invasive diagnosis of WPW syndrome.

Main Methods:

  • A neural network-based AI algorithm (locAP AI) was developed to predict AP location from delta-wave polarity on 12-lead ECGs.
  • The study included 357 WPW syndrome patients who underwent successful catheter ablation.
  • LocAP AI was trained and validated, and its performance was compared with Arruda, Milstein, and Fitzpatrick ECG-based algorithms.

Main Results:

  • LocAP AI achieved a high accuracy of 85.7% in identifying the correct AP location among 14 possibilities.
  • Established algorithms showed lower predictive accuracies: Arruda (53.2%), Milstein (65.6%), and Fitzpatrick (44.7%).
  • At comparable resolutions, locAP AI demonstrated superior accuracy (95.0%-95.6%) compared to traditional methods (p < 0.001).

Conclusions:

  • The developed AI-based algorithm (locAP AI) demonstrates excellent accuracy in predicting accessory pathway location in WPW syndrome.
  • This AI tool offers a highly accurate, non-invasive method for AP localization, surpassing existing ECG-based algorithms.
  • LocAP AI provides a higher resolution of possible anatomical locations, enhancing diagnostic capabilities for WPW syndrome.