Evaluation of a deep Learning-enabled automated computational heart modeling workflow for personalized assessment of ventricular arrhythmias.

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Abstract

Personalized, image-based computational heart modeling is a powerful technology that can be used to improve patient-specific arrhythmia risk stratification and ventricular tachycardia (VT) ablation targeting. However, most state-of-the-art methods still require manual interactions by expert users. The goal of this study is to evaluate the feasibility of an automated, deep learning-based workflow for reconstructing personalized computational electrophysiological heart models to guide patient-specific treatment of VT. Contrast-enhanced computed tomography (CE-CT) images with expert ventricular myocardium segmentations were acquired from 111 patients across 5 cohorts from 3 different institutions. A deep convolutional neural network (CNN) for segmenting left ventricular myocardium from CE-CT was developed, trained, and evaluated. From both CNN-based and expert segmentations in a subset of patients, personalized electrophysiological heart models were reconstructed, and rapid pacing was used to induce VTs. CNN-based and expert segmentations were more concordant in the middle myocardium than in the heart’s base or apex. Wavefront propagation during pacing was similar between CNN-based and original heart models. Between most sets of heart models, VT inducibility was the same, the number of induced VTs was strongly correlated, and VT circuits co-localized. Our results demonstrate that personalized computational heart models reconstructed from deep learning-based segmentations even with a small training set size can predict similar VT inducibility and circuit locations as those from expertly-derived heart models. Hence, a user-independent, automated framework for simulating arrhythmias in personalized heart models could feasibly be used in clinical settings to aid VT risk stratification and guide VT ablation therapy. KEY POINTS: Personalized electrophysiological heart modeling can aid in patient-specific ventricular tachycardia (VT) risk stratification and VT ablation targeting. Current state-of-the-art, image-based heart models for VT prediction require expert-dependent, manual interactions that may not be accessible across clinical settings. In this study, we develop an automated, deep learning-based workflow for reconstructing personalized heart models capable of simulating arrhythmias and compare its predictions with that of expert-generated heart models. The number and location of VTs was similar between heart models generated from the deep learning-based workflow and expert-generated heart models. These results demonstrate the feasibility of using an automated computational heart modeling workflow to aid in VT therapeutics and has implications for generalizing personalized computational heart technology to a broad range of clinical centers. Abstract figure legend In this study, we evaluate whether an automated, deep learning-based computational electrophysiological heart models can predict similar arrhythmias as those of expert, manually-derived heart models. First, we build a deep neural network to automatically segment contrast-enhanced CT scans and demonstrate that predicted imaging metrics are comparable to that of manual segmentations. Second, electrophysiological heart models reconstructed from these automated segmentations predict similar wavefront propagation and VT circuits as those of expert-reconstructed heart models. This work represents an advancement towards construction of a user-independent, computational framework to aid in VT risk stratification and guide VT ablation. CT: computed tomography, VT: ventricular tachycardia. This article is protected by copyright. All rights reserved.This article is protected by copyright. All rights reserved.