Evaluation of a deep learning‐enabled automated computational heart modelling workflow for personalized assessment of ventricular arrhythmias

Author:

Sung Eric12ORCID,Kyranakis Stephen12ORCID,Daimee Usama A.3,Engels Marc3,Prakosa Adityo12,Zhou Shijie12,Nazarian Saman4,Zimmerman Stefan L.5,Chrispin Jonathan13,Trayanova Natalia A.12ORCID

Affiliation:

1. Department of Biomedical Engineering Johns Hopkins University Baltimore MD USA

2. Alliance for Cardiovascular Diagnostic and Treatment Innovation Johns Hopkins University Baltimore MD USA

3. Division of Cardiology Department of Medicine Johns Hopkins Hospital Baltimore MD USA

4. Division of Cardiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA

5. Department of Radiology and Radiological Sciences Johns Hopkins Hospital Baltimore MD USA

Abstract

AbstractPersonalized, image‐based computational heart modelling 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 five cohorts from three 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. imageKey points Personalized electrophysiological heart modelling 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 modelling workflow to aid in VT therapeutics and has implications for generalizing personalized computational heart technology to a broad range of clinical centres.

Funder

National Heart, Lung, and Blood Institute

Publisher

Wiley

Subject

Physiology

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Artificial Intelligence and Machine Learning in Electrophysiology—a Short Review;Current Treatment Options in Cardiovascular Medicine;2023-09-04

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