Machine Learning Identification of Pro-arrhythmic Structures in Cardiac Fibrosis

Abstract

Cardiac fibrosis and other scarring of the heart, arising from conditions ranging from myocardial infarction to ageing, promotes dangerous arrhythmias by blocking the healthy propagation of cardiac excitation. Owing to the complexity of the dynamics of electrical signalling in the heart, however, the connection between different arrangements of blockage and various arrhythmic consequences remains poorly understood. Where a mechanism defies traditional understanding, machine learning can be invaluable for enabling accurate prediction of quantities of interest (measures of arrhythmic risk) in terms of predictor variables (such as the arrangement or pattern of obstructive scarring). In this study, we simulate the propagation of the action potential (AP) in tissue affected by fibrotic changes and hence detect sites that initiate re-entrant activation patterns. By separately considering multiple different stimulus regimes, we directly observe and quantify the sensitivity of re-entry formation to activation sequence in the fibrotic region. Then, by extracting the fibrotic structures around locations that both do and do not initiate re-entries, we use neural networks to determine to what extent re-entry initiation is predictable, and over what spatial scale conduction heterogeneities appear to act to produce this effect. We find that structural information within about 0.5 mm of a given point is sufficient to predict structures that initiate re-entry with more than 90% accuracy.

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“…AI application in clinical environments is exponentially increasing and leading toward new diagnostic and treatment techniques ( Feeny et al, 2020 ; Sánchez de la Nava et al, 2021 ). This trend has also been implemented in the electrophysiology field ( Muffoletto et al, 2021 ; Siontis and Friedman, 2021 ), in which the use of algorithms has been used for detecting or evaluating proarrhythmicity ( Shao et al, 2018 ; Halfar et al, 2021 ), classifying different rhythms ( Wasserlauf et al, 2019 ) or automatizing tasks as segmentation ( Yang et al, 2017 ). Moreover, its use in safety pharmacology could be applied to analyze all the data produced by in silico simulations.…”

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“…AI application on clinical environments is exponentially increasing and leading towards new diagnostic and treatment techniques (Feeny et al, 2020;. This trend has also been implemented in the electrophysiology field (Muffoletto et al, 2021;Siontis and Friedman, 2021), in which the use of algorithms has been used for detecting or evaluating proarrhythmicity (Shao et al, 2018;Halfar et al, 2021), classifying different rhythms (Wasserlauf et al, 2019) or automatizing tasks as segmentation (Yang et al, 2017). Moreover, its use in safety pharmacology could be applied to analyze all the data produced by in silico simulations.…”