Key result
Generating a patient-specific Purkinje network driven by clinical measurements reduced mean absolute errors in activation time compared to a non-patient-specific network in 3 subjects.
Why the study?
Does a patient-specific Purkinje network generation strategy reduce mean absolute errors in activation time compared to a non-patient-specific network in computational models?
Population
3 subjects
Comparison
Patient-specific Purkinje network generation… vs Non-patient-specific Purkinje network
Authors
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May refine computational models of cardiac activation; leaves open clinical translation beyond small cohorts.
Does a patient-specific Purkinje network generation strategy reduce mean absolute errors in activation time compared to a non-patient-specific network in computational models?
Generating patient-specific Purkinje networks driven by clinical measurements reduces activation time errors in computational models of pathological propagations.
Palamara et al. (2014) studied Pathological propagations (scar-related conduction problems and Wolff-Parkinson-White syndrome) (n=3). Patient-specific Purkinje network generation vs. Non-patient-specific network was evaluated on Mean absolute errors in the activation time. Generating a patient-specific Purkinje network driven by clinical measurements reduced mean absolute errors in activation time compared to a non-patient-specific network in 3 subjects.
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