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Journal of the American Heart Association|August 15, 2023

Explainable Machine Learning to Predict Anchored Reentry Substrate Created by Persistent Atrial Fibrillation Ablation in Computational ModelsSavannah F Bifulco, Fima Macheret, Griffin D Scott, et al.

JACC. Clinical Electrophysiology|September 1, 2023

Comparing Inducibility of Re-Entrant Arrhythmia in Patient-Specific Computational Models to Clinical Atrial Fibrillation PhenotypesFima Macheret, Savannah F Bifulco, Griffin D Scott, et al.

Journal of Cardiovascular Electrophysiology|December 26, 2022

Elevated fibrosis burden as assessed by MRI predicts cryoballoon ablation failurePatrick M Boyle, Sakher Sarairah, Kirsten T Kwan, et al.

Elife|May 4, 2021

Computational modeling identifies embolic stroke of undetermined source patients with potential arrhythmic substrateSavannah F Bifulco, Griffin D Scott, Sakher Sarairah, et al.

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Showing results (1-10 of 4) with videos related to

Sort By:

Pageof 1

Journal of the American Heart Association|August 15, 2023

Explainable Machine Learning to Predict Anchored Reentry Substrate Created by Persistent Atrial Fibrillation Ablation in Computational ModelsSavannah F Bifulco, Fima Macheret, Griffin D Scott, et al.

JACC. Clinical Electrophysiology|September 1, 2023

Comparing Inducibility of Re-Entrant Arrhythmia in Patient-Specific Computational Models to Clinical Atrial Fibrillation PhenotypesFima Macheret, Savannah F Bifulco, Griffin D Scott, et al.

Journal of Cardiovascular Electrophysiology|December 26, 2022

Elevated fibrosis burden as assessed by MRI predicts cryoballoon ablation failurePatrick M Boyle, Sakher Sarairah, Kirsten T Kwan, et al.

Elife|May 4, 2021

Computational modeling identifies embolic stroke of undetermined source patients with potential arrhythmic substrateSavannah F Bifulco, Griffin D Scott, Sakher Sarairah, et al.

Pageof 1