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Sung E, Kyranakis S, Daimee UA, Engels M, Prakosa A, Zhou S, Nazarian S, Zimmerman SL, Chrispin J, Trayanova NA. Evaluation of a deep learning-enabled automated computational heart modelling workflow for personalized assessment of ventricular arrhythmias. J Physiol 2024; 602:4625-4644. [PMID: 37060278 DOI: 10.1113/jp284125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 04/12/2023] [Indexed: 04/16/2023] Open
Abstract
Personalized, 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. KEY 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.
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Affiliation(s)
- Eric Sung
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, MD, USA
| | - Stephen Kyranakis
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, MD, USA
| | - Usama A Daimee
- Division of Cardiology, Department of Medicine, Johns Hopkins Hospital, Baltimore, MD, USA
| | - Marc Engels
- Division of Cardiology, Department of Medicine, Johns Hopkins Hospital, Baltimore, MD, USA
| | - Adityo Prakosa
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, MD, USA
| | - Shijie Zhou
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, MD, USA
| | - Saman Nazarian
- Division of Cardiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Stefan L Zimmerman
- Department of Radiology and Radiological Sciences, Johns Hopkins Hospital, Baltimore, MD, USA
| | - Jonathan Chrispin
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Division of Cardiology, Department of Medicine, Johns Hopkins Hospital, Baltimore, MD, USA
| | - Natalia A Trayanova
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, MD, USA
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Cluitmans MJM, Plank G, Heijman J. Digital twins for cardiac electrophysiology: state of the art and future challenges. Herzschrittmacherther Elektrophysiol 2024; 35:118-123. [PMID: 38607554 PMCID: PMC11161534 DOI: 10.1007/s00399-024-01014-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Accepted: 03/04/2024] [Indexed: 04/13/2024]
Abstract
Cardiac arrhythmias remain a major cause of death and disability. Current antiarrhythmic therapies are effective to only a limited extent, likely in large part due to their mechanism-independent approach. Precision cardiology aims to deliver targeted therapy for an individual patient to maximize efficacy and minimize adverse effects. In-silico digital twins have emerged as a promising strategy to realize the vision of precision cardiology. While there is no uniform definition of a digital twin, it typically employs digital tools, including simulations of mechanistic computer models, based on patient-specific clinical data to understand arrhythmia mechanisms and/or make clinically relevant predictions. Digital twins have become part of routine clinical practice in the setting of interventional cardiology, where commercially available services use digital twins to non-invasively determine the severity of stenosis (computed tomography-based fractional flow reserve). Although routine clinical application has not been achieved for cardiac arrhythmia management, significant progress towards digital twins for cardiac electrophysiology has been made in recent years. At the same time, significant technical and clinical challenges remain. This article provides a short overview of the history of digital twins for cardiac electrophysiology, including recent applications for the prediction of sudden cardiac death risk and the tailoring of rhythm control in atrial fibrillation. The authors highlight the current challenges for routine clinical application and discuss how overcoming these challenges may allow digital twins to enable a significant precision medicine-based advancement in cardiac arrhythmia management.
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Affiliation(s)
- Matthijs J M Cluitmans
- Department of Cardiology, Cardiovascular Research Institute Maastricht, Faculty of Health, Medicine, and Life Sciences, Maastricht University, Maastricht, The Netherlands
| | - Gernot Plank
- Gottfried Schatz Research Center, Division of Medical Physics & Biophysics, Medical University of Graz, Neue Stiftingtalstraße 6, 8010, Graz, Austria
| | - Jordi Heijman
- Department of Cardiology, Cardiovascular Research Institute Maastricht, Faculty of Health, Medicine, and Life Sciences, Maastricht University, Maastricht, The Netherlands.
- Gottfried Schatz Research Center, Division of Medical Physics & Biophysics, Medical University of Graz, Neue Stiftingtalstraße 6, 8010, Graz, Austria.
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Bhagirath P, Campos FO, Zaidi HA, Chen Z, Elliott M, Gould J, Kemme MJB, Wilde AAM, Götte MJW, Postema PG, Prassl AJ, Neic A, Plank G, Rinaldi CA, Bishop MJ. Predicting postinfarct ventricular tachycardia by integrating cardiac MRI and advanced computational reentrant pathway analysis. Heart Rhythm 2024:S1547-5271(24)02507-4. [PMID: 38670247 DOI: 10.1016/j.hrthm.2024.04.077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Revised: 03/26/2024] [Accepted: 04/15/2024] [Indexed: 04/28/2024]
Abstract
BACKGROUND Implantable cardiac defibrillator (ICD) implantation can protect against sudden cardiac death after myocardial infarction. However, improved risk stratification for device requirement is still needed. OBJECTIVE The purpose of this study was to improve assessment of postinfarct ventricular electropathology and prediction of appropriate ICD therapy by combining late gadolinium enhancement (LGE) and advanced computational modeling. METHODS ADAS 3D LV (ADAS LV Medical, Barcelona, Spain) and custom-made software were used to generate 3-dimensional patient-specific ventricular models in a prospective cohort of patients with a myocardial infarction (N = 40) having undergone LGE imaging before ICD implantation. Corridor metrics and 3-dimensional surface features were computed from LGE images. The Virtual Induction and Treatment of Arrhythmias (VITA) framework was applied to patient-specific models to comprehensively probe the vulnerability of the scar substrate to sustaining reentrant circuits. Imaging and VITA metrics, related to the numbers of induced ventricular tachycardias and their corresponding round trip times (RTTs), were compared with ICD therapy during follow-up. RESULTS Patients with an event (n = 17) had a larger interface between healthy myocardium and scar and higher VITA metrics. Cox regression analysis demonstrated a significant independent association with an event: interface (hazard ratio [HR] 2.79; 95% confidence interval [CI] 1.44-5.44; P < .01), unique ventricular tachycardias (HR 1.67; 95% CI 1.04-2.68; P = .03), mean RTT (HR 2.14; 95% CI 1.11-4.12; P = .02), and maximum RTT (HR 2.13; 95% CI 1.19-3.81; P = .01). CONCLUSION A detailed quantitative analysis of LGE-based scar maps, combined with advanced computational modeling, can accurately predict ICD therapy and could facilitate the early identification of high-risk patients in addition to left ventricular ejection fraction.
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Affiliation(s)
- Pranav Bhagirath
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Department of Cardiology, Amsterdam University Medical Center, Amsterdam, The Netherlands.
| | - Fernando O Campos
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Hassan A Zaidi
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Zhong Chen
- Department of Cardiology, Royal Brompton & Harefield NHS Foundation Trust, London, United Kingdom
| | - Mark Elliott
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Department of Cardiology, St. Thomas' Hospital, London, United Kingdom
| | - Justin Gould
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Department of Cardiology, St. Thomas' Hospital, London, United Kingdom
| | - Michiel J B Kemme
- Department of Cardiology, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Arthur A M Wilde
- Department of Cardiology, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Marco J W Götte
- Department of Cardiology, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Pieter G Postema
- Department of Cardiology, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Anton J Prassl
- Gottfried Schatz Research Center, Division of Biophysics, Medical University of Graz, Graz, Austria
| | | | - Gernot Plank
- Gottfried Schatz Research Center, Division of Biophysics, Medical University of Graz, Graz, Austria; NumeriCor GmbH, Graz, Austria
| | | | - Martin J Bishop
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
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Stanciulescu LA, Vatasescu R. Ventricular Tachycardia Catheter Ablation: Retrospective Analysis and Prospective Outlooks-A Comprehensive Review. Biomedicines 2024; 12:266. [PMID: 38397868 PMCID: PMC10886924 DOI: 10.3390/biomedicines12020266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Revised: 01/16/2024] [Accepted: 01/23/2024] [Indexed: 02/25/2024] Open
Abstract
Ventricular tachycardia is a potentially life-threatening arrhythmia associated with an overall high morbi-mortality, particularly in patients with structural heart disease. Despite their pivotal role in preventing sudden cardiac death, implantable cardioverter-defibrillators, although a guideline-based class I recommendation, are unable to prevent arrhythmic episodes and significantly alter the quality of life by delivering recurrent therapies. From open-heart surgical ablation to the currently widely used percutaneous approach, catheter ablation is a safe and effective procedure able to target the responsible re-entry myocardial circuit from both the endocardium and the epicardium. There are four main mapping strategies, activation, entrainment, pace, and substrate mapping, each of them with their own advantages and limitations. The contemporary guideline-based recommendations for VT ablation primarily apply to patients experiencing antiarrhythmic drug ineffectiveness or those intolerant to the pharmacological treatment. Although highly effective in most cases of scar-related VTs, the traditional approach may sometimes be insufficient, especially in patients with nonischemic cardiomyopathies, where circuits may be unmappable using the classic techniques. Alternative methods have been proposed, such as stereotactic arrhythmia radioablation or radiotherapy ablation, surgical ablation, needle ablation, transarterial coronary ethanol ablation, and retrograde coronary venous ethanol ablation, with promising results. Further studies are needed in order to prove the overall efficacy of these methods in comparison to standard radiofrequency delivery. Nevertheless, as the field of cardiac electrophysiology continues to evolve, it is important to acknowledge the role of artificial intelligence in both the pre-procedural planning and the intervention itself.
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Affiliation(s)
- Laura Adina Stanciulescu
- Cardio-Thoracic Department, "Carol Davila" University of Medicine and Pharmacy, 050474 Bucharest, Romania
- Cardiology Department, Clinical Emergency Hospital, 014461 Bucharest, Romania
| | - Radu Vatasescu
- Cardio-Thoracic Department, "Carol Davila" University of Medicine and Pharmacy, 050474 Bucharest, Romania
- Cardiology Department, Clinical Emergency Hospital, 014461 Bucharest, Romania
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Africa PC, Piersanti R, Regazzoni F, Bucelli M, Salvador M, Fedele M, Pagani S, Dede' L, Quarteroni A. lifex-ep: a robust and efficient software for cardiac electrophysiology simulations. BMC Bioinformatics 2023; 24:389. [PMID: 37828428 PMCID: PMC10571323 DOI: 10.1186/s12859-023-05513-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 10/02/2023] [Indexed: 10/14/2023] Open
Abstract
BACKGROUND Simulating the cardiac function requires the numerical solution of multi-physics and multi-scale mathematical models. This underscores the need for streamlined, accurate, and high-performance computational tools. Despite the dedicated endeavors of various research teams, comprehensive and user-friendly software programs for cardiac simulations, capable of accurately replicating both normal and pathological conditions, are still in the process of achieving full maturity within the scientific community. RESULTS This work introduces [Formula: see text]-ep, a publicly available software for numerical simulations of the electrophysiology activity of the cardiac muscle, under both normal and pathological conditions. [Formula: see text]-ep employs the monodomain equation to model the heart's electrical activity. It incorporates both phenomenological and second-generation ionic models. These models are discretized using the Finite Element method on tetrahedral or hexahedral meshes. Additionally, [Formula: see text]-ep integrates the generation of myocardial fibers based on Laplace-Dirichlet Rule-Based Methods, previously released in Africa et al., 2023, within [Formula: see text]-fiber. As an alternative, users can also choose to import myofibers from a file. This paper provides a concise overview of the mathematical models and numerical methods underlying [Formula: see text]-ep, along with comprehensive implementation details and instructions for users. [Formula: see text]-ep features exceptional parallel speedup, scaling efficiently when using up to thousands of cores, and its implementation has been verified against an established benchmark problem for computational electrophysiology. We showcase the key features of [Formula: see text]-ep through various idealized and realistic simulations conducted in both normal and pathological scenarios. Furthermore, the software offers a user-friendly and flexible interface, simplifying the setup of simulations using self-documenting parameter files. CONCLUSIONS [Formula: see text]-ep provides easy access to cardiac electrophysiology simulations for a wide user community. It offers a computational tool that integrates models and accurate methods for simulating cardiac electrophysiology within a high-performance framework, while maintaining a user-friendly interface. [Formula: see text]-ep represents a valuable tool for conducting in silico patient-specific simulations.
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Affiliation(s)
- Pasquale Claudio Africa
- MOX, Department of Mathematics, Politecnico di Milano, Milano, Italy
- mathLab, Mathematics Area, SISSA International School for Advanced Studies, Trieste, Italy
| | - Roberto Piersanti
- MOX, Department of Mathematics, Politecnico di Milano, Milano, Italy.
| | | | - Michele Bucelli
- MOX, Department of Mathematics, Politecnico di Milano, Milano, Italy
| | - Matteo Salvador
- MOX, Department of Mathematics, Politecnico di Milano, Milano, Italy
- Institute for Computational and Mathematical Engineering, Stanford University, Stanford, California, USA
| | - Marco Fedele
- MOX, Department of Mathematics, Politecnico di Milano, Milano, Italy
| | - Stefano Pagani
- MOX, Department of Mathematics, Politecnico di Milano, Milano, Italy
| | - Luca Dede'
- MOX, Department of Mathematics, Politecnico di Milano, Milano, Italy
| | - Alfio Quarteroni
- MOX, Department of Mathematics, Politecnico di Milano, Milano, Italy
- Institute of Mathematics, École Polytechnique Fédérale de Lausanne, Lausanne, Professor emeritus, Switzerland
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Bhagirath P, Campos FO, Postema PG, Kemme MJB, Wilde AAM, Prassl AJ, Neic A, Rinaldi CA, Götte MJW, Plank G, Bishop MJ. Arrhythmogenic vulnerability of re-entrant pathways in post-infarct ventricular tachycardia assessed by advanced computational modelling. Europace 2023; 25:euad198. [PMID: 37421339 PMCID: PMC10481251 DOI: 10.1093/europace/euad198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 04/26/2023] [Accepted: 06/21/2023] [Indexed: 07/10/2023] Open
Abstract
AIMS Substrate assessment of scar-mediated ventricular tachycardia (VT) is frequently performed using late gadolinium enhancement (LGE) images. Although this provides structural information about critical pathways through the scar, assessing the vulnerability of these pathways for sustaining VT is not possible with imaging alone.This study evaluated the performance of a novel automated re-entrant pathway finding algorithm to non-invasively predict VT circuit and inducibility. METHODS Twenty post-infarct VT-ablation patients were included for retrospective analysis. Commercially available software (ADAS3D left ventricular) was used to generate scar maps from 2D-LGE images using the default 40-60 pixel-signal-intensity (PSI) threshold. In addition, algorithm sensitivity for altered thresholds was explored using PSI 45-55, 35-65, and 30-70. Simulations were performed on the Virtual Induction and Treatment of Arrhythmias (VITA) framework to identify potential sites of block and assess their vulnerability depending on the automatically computed round-trip-time (RTT). Metrics, indicative of substrate complexity, were correlated with VT-recurrence during follow-up. RESULTS Total VTs (85 ± 43 vs. 42 ± 27) and unique VTs (9 ± 4 vs. 5 ± 4) were significantly higher in patients with- compared to patients without recurrence, and were predictive of recurrence with area under the curve of 0.820 and 0.770, respectively. VITA was robust to scar threshold variations with no significant impact on total and unique VTs, and mean RTT between the four models. Simulation metrics derived from PSI 45-55 model had the highest number of parameters predictive for post-ablation VT-recurrence. CONCLUSION Advanced computational metrics can non-invasively and robustly assess VT substrate complexity, which may aid personalized clinical planning and decision-making in the treatment of post-infarction VT.
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Affiliation(s)
- Pranav Bhagirath
- School of Biomedical Engineering and Imaging Sciences, King's College London, 4th Floor, Lambeth Wing, St. Thomas' Hospital, London SE1 7EH, UK
- Department of Cardiology, St Thomas' Hospital, London SE1 7EH, UK
| | - Fernando O Campos
- School of Biomedical Engineering and Imaging Sciences, King's College London, 4th Floor, Lambeth Wing, St. Thomas' Hospital, London SE1 7EH, UK
| | - Pieter G Postema
- Department of Cardiology, Amsterdam University Medical Center, Amsterdam, the Netherlands
| | - Michiel J B Kemme
- Department of Cardiology, Amsterdam University Medical Center, Amsterdam, the Netherlands
| | - Arthur A M Wilde
- Department of Cardiology, Amsterdam University Medical Center, Amsterdam, the Netherlands
| | - Anton J Prassl
- Gottfried Schatz Research Center, Division of Biophysics, Medical University of Graz, Graz, Austria
| | - Aurel Neic
- Gottfried Schatz Research Center, Division of Biophysics, Medical University of Graz, Graz, Austria
| | | | - Marco J W Götte
- Department of Cardiology, Amsterdam University Medical Center, Amsterdam, the Netherlands
| | - Gernot Plank
- Gottfried Schatz Research Center, Division of Biophysics, Medical University of Graz, Graz, Austria
| | - Martin J Bishop
- School of Biomedical Engineering and Imaging Sciences, King's College London, 4th Floor, Lambeth Wing, St. Thomas' Hospital, London SE1 7EH, UK
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