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Artificial Intelligence to Predict Major Arrhythmic Events Based on Left Ventricular Electroanatomic Mapping Data.

Yari Valeri1,2, Paolo Compagnucci1,2, Marialucia Narducci3

  • 1Department of Biomedical Sciences and Public Health, Marche Polytechnic University, 60121 Ancona, Italy.

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|May 4, 2026
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Summary
This summary is machine-generated.

Artificial intelligence (AI) analysis of electroanatomic mapping (EAM) data improves prediction of major arrhythmic events (MAEs). Quantitative EAM features, particularly conduction heterogeneity (GR), significantly enhance risk stratification beyond traditional methods.

Keywords:
artificial intelligencedeep learningelectroanatomic mappingleft ventriclelinear regressionmachine learningmajor arrhythmic eventssupport vector machineventricular arrhythmia

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Area of Science:

  • Cardiology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Electroanatomic mapping (EAM) offers detailed spatial and electrogram data.
  • Prognostic value of quantitative EAM features using AI remains underexplored.
  • Need for advanced methods to predict major arrhythmic events (MAEs).

Purpose of the Study:

  • To evaluate if AI analysis of CARTO system EAM exports improves MAE prediction.
  • To identify key quantitative EAM features for risk stratification.
  • To compare AI-driven EAM analysis with conventional predictors.

Main Methods:

  • Retrospective, multicenter cohort study of 248 patients undergoing left ventricular EAM.
  • Quantitative EAM descriptors transformed into derived metrics (GR, LAT, VLT, Scar Areas).
  • Machine learning and deep learning models trained and validated; performance metrics assessed.

Main Results:

  • AI-processed EAM features combined with clinical data achieved high discriminatory performance (AUC up to 0.92).
  • High specificity (≈0.97-0.998) with modest sensitivity (≈0.39-0.58).
  • Local activation heterogeneity (GR) was the most significant EAM predictor; VLT, LAT, and Scar Areas also contributed.

Conclusions:

  • AI-driven quantitative EAM analysis enhances MAE risk stratification beyond conventional methods.
  • Local conduction heterogeneity (GR) is a dominant EAM predictor.
  • Prospective validation and real-time integration of AI in EAM platforms are recommended.