Artificial Intelligence to Predict Major Arrhythmic Events Based on Left Ventricular Electroanatomic Mapping Data
: AI-driven quantitative analysis of left ventricular EAM exports augments risk stratification for MAEs beyond conventional clinical and binary EAM descriptors. Reflecting local conduction heterogeneity, GR emerged as the dominant EAM predictor. Prospective validation in larger, disease-specific cohorts and real-time integration within EAM platforms are warranted.
Authors
- Maria Lucia Narducci (ORCID: https://orcid.org/0000-0001-8682-0322)
- Yari Valeri (ORCID: https://orcid.org/0000-0002-0454-0739)
- Giuseppe Sgarito (ORCID: https://orcid.org/0000-0001-6003-6054)
- Francesca Campanelli
- Paolo Compagnucci (ORCID: https://orcid.org/0000-0003-1924-6548)
- Vincenzo Schillaci (ORCID: https://orcid.org/0000-0002-3016-2583)
- Francesco Piva (ORCID: https://orcid.org/0000-0003-1850-2482)
- Sergio Conti (ORCID: https://orcid.org/0000-0001-6683-6781)
- Gemma Pelargonio (ORCID: https://orcid.org/0000-0003-3236-3458)
- P Veri
- E Pecorari
- Leonardo D’Angelo (ORCID: https://orcid.org/0009-0006-2118-893X)
- M Casella
- Isabel Concetti
- Antonio Dello Russo
- Giuliano Santagata
- Roberto Scacciavillani
- Martina Apicella
- Giovanni Volpato
- Francesco Solimene
Institutions
- Università Cattolica del Sacro Cuore (IT)
- Marche Polytechnic University (IT)
- University of Iowa (US)
- Agostino Gemelli University Polyclinic (IT)
- Mercy Clinic Neurology (US)
- Maria Cecilia Hospital (IT)
- University of the Sacred Heart (JP)
- Istituti di Ricovero e Cura a Carattere Scientifico (IT)
- Istituto Mediterraneo per i Trapianti e Terapie ad Alta Specializzazione (IT)
Publication Details
- Journal
- Journal of Clinical Medicine
- Published
- 2026-04-17
- DOI
- https://doi.org/10.3390/jcm15083078
- Primary Topic
- Cardiac electrophysiology and arrhythmias
- Type
- article
- Field-Weighted Citation Impact
- 0.00