Key result
Support vector machine identifies phase singularity tethering sites with ~95% accuracy.
Why the study?
AF is sustained by re-entrant activation patterns, and ablation strategies target regions supporting these patterns, motivating the characterization of tissue properties that tether them in virtual patient models.
Does a machine learning model using local conduction velocity, action potential duration, and atrial size identify tissue regions that sustain phase singularities in patient-specific models of atrial fibrillation?
Does a machine learning model using local conduction velocity, action potential duration, and atrial size identify tissue regions that sustain phase singularities in patient-specific models of atrial fibrillation?
Machine learning applied to patient-specific virtual models can accurately identify atrial tissue regions that sustain re-entrant activation using local conduction velocity, action potential duration, and atrial size.
Answers are grounded in this paper and cite their sources.
May inform ML-guided AF ablation targeting; leaves open translation from animal models to clinical benefit.
AIMS: Atrial fibrillation (AF) is sustained by re-entrant activation patterns. Ablation strategies have been proposed that target regions of tissue that may support re-entrant activation patterns. We aimed to characterize the tissue properties associated with regions that tether re-entrant activation patterns in a validated virtual patient cohort. METHODS AND RESULTS: Atrial fibrillation patient-specific models (seven paroxysmal and three persistent) were generated and validated against local activation time (LAT) measurements during an S1-S2 pacing protocol from the coronary sinus and high right atrium, respectively. Atrial models were stimulated with burst pacing from three locations in the proximity of each pulmonary vein to initiate re-entrant activation patterns. Five atria exhibited sustained activation patterns for at least 80 s. Models with short maximum action potential durations (APDs) were associated with sustained activation. Phase singularities were mapped across the atria sustained activation patterns. Regions with a low maximum conduction velocity (CV) were associated with tethering of phase singularities. A support vector machine (SVM) was trained on maximum local conduction velocity and action potential duration to identify regions that tether phase singularities. The SVM identified regions of tissue that could support tethering with 91% accuracy. This accuracy increased to 95% when the SVM was also trained on surface area. CONCLUSION: In a virtual patient cohort, local tissue properties, that can be measured (CV) or estimated (APD; using effective refractory period as a surrogate) clinically, identified regions of tissue that tether phase singularities. Combing CV and APD with atrial surface area further improved the accuracy in identifying regions that tether phase singularities.
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Corrado et al. (2020) studied Atrial fibrillation (n=10). Support vector machine classifier using local conduction velocity, action potential duration, and atrial surface area was evaluated on Accuracy in identifying regions of tissue that tether phase singularities. A support vector machine trained on local conduction velocity, action potential duration, and atrial surface area identified regions of tissue that tether phase singularities with 95% accuracy.
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