Machine learning differentiates right posterior from right anterior accessory pathways using 6-lead electrocardiograms in dogs with ventricular preexcitation

Authors

Romain Pariaut (ORCID: https://orcid.org/0000-0002-3149-9890)
Wyatt H. Flanders
N. Sydney Möise (ORCID: https://orcid.org/0000-0002-3132-8602)
Roberto A. Santilli (ORCID: https://orcid.org/0000-0001-5937-2050)
M. Perego (ORCID: https://orcid.org/0000-0002-5083-1297)
Parminder S. Basran (ORCID: https://orcid.org/0000-0002-1573-1549)

Institutions

Cornell University (US)
New York State College of Veterinary Medicine (US)

Publication Details

Journal: Journal of the American Veterinary Medical Association
Published: 2025-11-12
DOI: https://doi.org/10.2460/javma.25.07.0453
Primary Topic: Cardiovascular Conditions and Treatments
Type: article
Field-Weighted Citation Impact: 0.00

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article

Machine learning differentiates right posterior from right anterior accessory pathways using 6-lead electrocardiograms in dogs with ventricular preexcitation

Romain Pariaut, Wyatt H. Flanders, N. Sydney Möise, Roberto A. Santilli et al.

Nov 12, 2025

Journal of the American Veterinary Medical Association

Cardiovascular Conditions and Treatments

article

Machine learning differentiates right posterior from right anterior accessory pathways using 6-lead electrocardiograms in dogs with ventricular preexcitation

Romain Pariaut, Wyatt H. Flanders, N. Sydney Möise, Roberto A. Santilli, M. Perego, Parminder S. Basran

November 12, 2025article en

Abstract

No abstract available for this paper.

Journal of the American Veterinary Medical Association

Cornell University (US), New York State College of Veterinary Medicine (US)

Openalex Percentile: Top 51%

Cardiovascular Conditions and Treatments

DOI Source OpenAlex

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