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Li L, Camps J, Jenny Wang Z, Beetz M, Banerjee A, Rodriguez B, Grau V. Toward Enabling Cardiac Digital Twins of Myocardial Infarction Using Deep Computational Models for Inverse Inference. IEEE TRANSACTIONS ON MEDICAL IMAGING 2024; 43:2466-2478. [PMID: 38373128 DOI: 10.1109/tmi.2024.3367409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/21/2024]
Abstract
Cardiac digital twins (CDTs) have the potential to offer individualized evaluation of cardiac function in a non-invasive manner, making them a promising approach for personalized diagnosis and treatment planning of myocardial infarction (MI). The inference of accurate myocardial tissue properties is crucial in creating a reliable CDT of MI. In this work, we investigate the feasibility of inferring myocardial tissue properties from the electrocardiogram (ECG) within a CDT platform. The platform integrates multi-modal data, such as cardiac MRI and ECG, to enhance the accuracy and reliability of the inferred tissue properties. We perform a sensitivity analysis based on computer simulations, systematically exploring the effects of infarct location, size, degree of transmurality, and electrical activity alteration on the simulated QRS complex of ECG, to establish the limits of the approach. We subsequently present a novel deep computational model, comprising a dual-branch variational autoencoder and an inference model, to infer infarct location and distribution from the simulated QRS. The proposed model achieves mean Dice scores of 0.457 ±0.317 and 0.302 ±0.273 for the inference of left ventricle scars and border zone, respectively. The sensitivity analysis enhances our understanding of the complex relationship between infarct characteristics and electrophysiological features. The in silico experimental results show that the model can effectively capture the relationship for the inverse inference, with promising potential for clinical application in the future. The code is available at https://github.com/lileitech/MI_inverse_inference.
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Trayanova NA, Lyon A, Shade J, Heijman J. Computational modeling of cardiac electrophysiology and arrhythmogenesis: toward clinical translation. Physiol Rev 2024; 104:1265-1333. [PMID: 38153307 DOI: 10.1152/physrev.00017.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 12/19/2023] [Accepted: 12/21/2023] [Indexed: 12/29/2023] Open
Abstract
The complexity of cardiac electrophysiology, involving dynamic changes in numerous components across multiple spatial (from ion channel to organ) and temporal (from milliseconds to days) scales, makes an intuitive or empirical analysis of cardiac arrhythmogenesis challenging. Multiscale mechanistic computational models of cardiac electrophysiology provide precise control over individual parameters, and their reproducibility enables a thorough assessment of arrhythmia mechanisms. This review provides a comprehensive analysis of models of cardiac electrophysiology and arrhythmias, from the single cell to the organ level, and how they can be leveraged to better understand rhythm disorders in cardiac disease and to improve heart patient care. Key issues related to model development based on experimental data are discussed, and major families of human cardiomyocyte models and their applications are highlighted. An overview of organ-level computational modeling of cardiac electrophysiology and its clinical applications in personalized arrhythmia risk assessment and patient-specific therapy of atrial and ventricular arrhythmias is provided. The advancements presented here highlight how patient-specific computational models of the heart reconstructed from patient data have achieved success in predicting risk of sudden cardiac death and guiding optimal treatments of heart rhythm disorders. Finally, an outlook toward potential future advances, including the combination of mechanistic modeling and machine learning/artificial intelligence, is provided. As the field of cardiology is embarking on a journey toward precision medicine, personalized modeling of the heart is expected to become a key technology to guide pharmaceutical therapy, deployment of devices, and surgical interventions.
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Affiliation(s)
- Natalia A Trayanova
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, Maryland, United States
| | - Aurore Lyon
- Department of Biomedical Engineering, CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
- Division of Heart and Lungs, Department of Medical Physiology, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Julie Shade
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, Maryland, United States
| | - Jordi Heijman
- Department of Cardiology, CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
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Arnold R, Prassl AJ, Neic A, Thaler F, Augustin CM, Gsell MAF, Gillette K, Manninger M, Scherr D, Plank G. pyCEPS: A cross-platform electroanatomic mapping data to computational model conversion platform for the calibration of digital twin models of cardiac electrophysiology. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 254:108299. [PMID: 38959599 DOI: 10.1016/j.cmpb.2024.108299] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 06/10/2024] [Accepted: 06/19/2024] [Indexed: 07/05/2024]
Abstract
BACKGROUND AND OBJECTIVE Data from electro-anatomical mapping (EAM) systems are playing an increasingly important role in computational modeling studies for the patient-specific calibration of digital twin models. However, data exported from commercial EAM systems are challenging to access and parse. Converting to data formats that are easily amenable to be viewed and analyzed with commonly used cardiac simulation software tools such as openCARP remains challenging. We therefore developed an open-source platform, pyCEPS, for parsing and converting clinical EAM data conveniently to standard formats widely adopted within the cardiac modeling community. METHODS AND RESULTS pyCEPS is an open-source Python-based platform providing the following functions: (i) access and interrogate the EAM data exported from clinical mapping systems; (ii) efficient browsing of EAM data to preview mapping procedures, electrograms (EGMs), and electro-cardiograms (ECGs); (iii) conversion to modeling formats according to the openCARP standard, to be amenable to analysis with standard tools and advanced workflows as used for in silico EAM data. Documentation and training material to facilitate access to this complementary research tool for new users is provided. We describe the technological underpinnings and demonstrate the capabilities of pyCEPS first, and showcase its use in an exemplary modeling application where we use clinical imaging data to build a patient-specific anatomical model. CONCLUSION With pyCEPS we offer an open-source framework for accessing EAM data, and converting these to cardiac modeling standard formats. pyCEPS provides the core functionality needed to integrate EAM data in cardiac modeling research. We detail how pyCEPS could be integrated into model calibration workflows facilitating the calibration of a computational model based on EAM data.
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Affiliation(s)
- Robert Arnold
- Gottfried Schatz Research Center, Division of Medical Physics and Biophysics, Medical University of Graz, Graz, Austria
| | - Anton J Prassl
- Gottfried Schatz Research Center, Division of Medical Physics and Biophysics, Medical University of Graz, Graz, Austria
| | | | - Franz Thaler
- Gottfried Schatz Research Center, Division of Medical Physics and Biophysics, Medical University of Graz, Graz, Austria; Institute of Computer Graphics and Vision, Graz University of Technology, Graz, Austria
| | - Christoph M Augustin
- Gottfried Schatz Research Center, Division of Medical Physics and Biophysics, Medical University of Graz, Graz, Austria
| | - Matthias A F Gsell
- Gottfried Schatz Research Center, Division of Medical Physics and Biophysics, Medical University of Graz, Graz, Austria
| | - Karli Gillette
- Gottfried Schatz Research Center, Division of Medical Physics and Biophysics, Medical University of Graz, Graz, Austria; BioTechMed-Graz, Graz, Austria
| | - Martin Manninger
- Division of Cardiology, Department of Medicine, Medical University of Graz, Graz, Austria
| | - Daniel Scherr
- Division of Cardiology, Department of Medicine, Medical University of Graz, Graz, Austria
| | - Gernot Plank
- Gottfried Schatz Research Center, Division of Medical Physics and Biophysics, Medical University of Graz, Graz, Austria; NumeriCor GmbH, Graz, Austria; BioTechMed-Graz, Graz, Austria.
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Salvador M, Kong F, Peirlinck M, Parker DW, Chubb H, Dubin AM, Marsden AL. Digital twinning of cardiac electrophysiology for congenital heart disease. J R Soc Interface 2024; 21:20230729. [PMID: 38835246 DOI: 10.1098/rsif.2023.0729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 03/15/2024] [Indexed: 06/06/2024] Open
Abstract
In recent years, blending mechanistic knowledge with machine learning has had a major impact in digital healthcare. In this work, we introduce a computational pipeline to build certified digital replicas of cardiac electrophysiology in paediatric patients with congenital heart disease. We construct the patient-specific geometry by means of semi-automatic segmentation and meshing tools. We generate a dataset of electrophysiology simulations covering cell-to-organ level model parameters and using rigorous mathematical models based on differential equations. We previously proposed Branched Latent Neural Maps (BLNMs) as an accurate and efficient means to recapitulate complex physical processes in a neural network. Here, we employ BLNMs to encode the parametrized temporal dynamics of in silico 12-lead electrocardiograms (ECGs). BLNMs act as a geometry-specific surrogate model of cardiac function for fast and robust parameter estimation to match clinical ECGs in paediatric patients. Identifiability and trustworthiness of calibrated model parameters are assessed by sensitivity analysis and uncertainty quantification.
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Affiliation(s)
- Matteo Salvador
- Institute for Computational and Mathematical Engineering, Stanford University , Stanford, CA, USA
- Cardiovascular Institute, Stanford University , Stanford, CA, USA
- Pediatric Cardiology, Stanford University , Stanford, CA, USA
| | - Fanwei Kong
- Institute for Computational and Mathematical Engineering, Stanford University , Stanford, CA, USA
- Cardiovascular Institute, Stanford University , Stanford, CA, USA
- Pediatric Cardiology, Stanford University , Stanford, CA, USA
| | - Mathias Peirlinck
- Department of Biomechanical Engineering, Delft University of Technology , Delft, The Netherlands
| | - David W Parker
- Stanford Research Computing Center, Stanford University , Stanford, CA, USA
| | - Henry Chubb
- Pediatric Cardiology, Stanford University , Stanford, CA, USA
| | - Anne M Dubin
- Pediatric Cardiology, Stanford University , Stanford, CA, USA
| | - Alison L Marsden
- Institute for Computational and Mathematical Engineering, Stanford University , Stanford, CA, USA
- Cardiovascular Institute, Stanford University , Stanford, CA, USA
- Pediatric Cardiology, Stanford University , Stanford, CA, USA
- Department of Bioengineering, Stanford University , Stanford, CA, USA
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Gsell MAF, Neic A, Bishop MJ, Gillette K, Prassl AJ, Augustin CM, Vigmond EJ, Plank G. ForCEPSS-A framework for cardiac electrophysiology simulations standardization. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 251:108189. [PMID: 38728827 DOI: 10.1016/j.cmpb.2024.108189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 04/04/2024] [Accepted: 04/17/2024] [Indexed: 05/12/2024]
Abstract
BACKGROUND AND OBJECTIVE Simulation of cardiac electrophysiology (CEP) is an important research tool that is increasingly being adopted in industrial and clinical applications. Typical workflows for CEP simulation consist of a sequence of processing stages starting with building an anatomical model and then calibrating its electrophysiological properties to match observable data. While the calibration stages are common and generalizable, most CEP studies re-implement these steps in complex and highly variable workflows. This lack of standardization renders the execution of computational CEP studies in an efficient, robust, and reproducible manner a significant challenge. Here, we propose ForCEPSS as an efficient and robust, yet flexible, software framework for standardizing CEP simulation studies. METHODS AND RESULTS Key processing stages of CEP simulation studies are identified and implemented in a standardized workflow that builds on openCARP1 Plank et al. (2021) and the Python-based carputils2 framework. Stages include (i) the definition and initialization of action potential phenotypes, (ii) the tissue scale calibration of conduction properties, (iii) the functional initialization to approximate a limit cycle corresponding to the dynamic reference state according to an experimental protocol, and, (iv) the execution of the CEP study where the electrophysiological response to a perturbation of the limit cycle is probed. As an exemplar application, we employ ForCEPSS to prepare a CEP study according to the Virtual Arrhythmia Risk Prediction protocol used for investigating the arrhythmogenic risk of developing infarct-related ventricular tachycardia (VT) in ischemic cardiomyopathy patients. We demonstrate that ForCEPSS enables a fully automated execution of all stages of this complex protocol. CONCLUSION ForCEPSS offers a novel comprehensive, standardized, and automated CEP simulation workflow. The high degree of automation accelerates the execution of CEP simulation studies, reduces errors, improves robustness, and makes CEP studies reproducible. Verification of simulation studies within the CEP modeling community is thus possible. As such, ForCEPSS makes an important contribution towards increasing transparency, standardization, and reproducibility of in silico CEP experiments.
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Affiliation(s)
- Matthias A F Gsell
- Gottfried Schatz Research Center, Division of Medical Physics and Biophysics, Medical University of Graz, Graz, Austria
| | | | | | - Karli Gillette
- Gottfried Schatz Research Center, Division of Medical Physics and Biophysics, Medical University of Graz, Graz, Austria
| | - Anton J Prassl
- Gottfried Schatz Research Center, Division of Medical Physics and Biophysics, Medical University of Graz, Graz, Austria
| | - Christoph M Augustin
- Gottfried Schatz Research Center, Division of Medical Physics and Biophysics, Medical University of Graz, Graz, Austria; BioTechMed-Graz, Graz, Austria
| | - Edward J Vigmond
- Liryc Cardiac Modeling Institute, Fondation Bordeaux University, Bordeaux, France; CNRS, Bordeaux INP, IMB, University of Bordeaux, Bordeaux, France
| | - Gernot Plank
- Gottfried Schatz Research Center, Division of Medical Physics and Biophysics, Medical University of Graz, Graz, Austria; BioTechMed-Graz, Graz, Austria.
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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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Camps J, Berg LA, Wang ZJ, Sebastian R, Riebel LL, Doste R, Zhou X, Sachetto R, Coleman J, Lawson B, Grau V, Burrage K, Bueno-Orovio A, Weber Dos Santos R, Rodriguez B. Digital twinning of the human ventricular activation sequence to Clinical 12-lead ECGs and magnetic resonance imaging using realistic Purkinje networks for in silico clinical trials. Med Image Anal 2024; 94:103108. [PMID: 38447244 DOI: 10.1016/j.media.2024.103108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 02/06/2024] [Accepted: 02/13/2024] [Indexed: 03/08/2024]
Abstract
Cardiac in silico clinical trials can virtually assess the safety and efficacy of therapies using human-based modelling and simulation. These technologies can provide mechanistic explanations for clinically observed pathological behaviour. Designing virtual cohorts for in silico trials requires exploiting clinical data to capture the physiological variability in the human population. The clinical characterisation of ventricular activation and the Purkinje network is challenging, especially non-invasively. Our study aims to present a novel digital twinning pipeline that can efficiently generate and integrate Purkinje networks into human multiscale biventricular models based on subject-specific clinical 12-lead electrocardiogram and magnetic resonance recordings. Essential novel features of the pipeline are the human-based Purkinje network generation method, personalisation considering ECG R wave progression as well as QRS morphology, and translation from reduced-order Eikonal models to equivalent biophysically-detailed monodomain ones. We demonstrate ECG simulations in line with clinical data with clinical image-based multiscale models with Purkinje in four control subjects and two hypertrophic cardiomyopathy patients (simulated and clinical QRS complexes with Pearson's correlation coefficients > 0.7). Our methods also considered possible differences in the density of Purkinje myocardial junctions in the Eikonal-based inference as regional conduction velocities. These differences translated into regional coupling effects between Purkinje and myocardial models in the monodomain formulation. In summary, we demonstrate a digital twin pipeline enabling simulations yielding clinically consistent ECGs with clinical CMR image-based biventricular multiscale models, including personalised Purkinje in healthy and cardiac disease conditions.
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Affiliation(s)
- Julia Camps
- University of Oxford, Oxford, United Kingdom.
| | | | | | | | | | - Ruben Doste
- University of Oxford, Oxford, United Kingdom
| | - Xin Zhou
- University of Oxford, Oxford, United Kingdom
| | - Rafael Sachetto
- Universidade Federal de São João del Rei, São João del Rei, MG, Brazil
| | | | - Brodie Lawson
- Queensland University of Technology, Brisbane, Australia
| | | | - Kevin Burrage
- University of Oxford, Oxford, United Kingdom; Queensland University of Technology, Brisbane, Australia
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Harnod Z, Lin C, Yang HW, Wang ZW, Huang HL, Lin TY, Huang CY, Lin LY, Young HWV, Lo MT. A transferable in-silico augmented ischemic model for virtual myocardial perfusion imaging and myocardial infarction detection. Med Image Anal 2024; 93:103087. [PMID: 38244290 DOI: 10.1016/j.media.2024.103087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 03/03/2023] [Accepted: 01/08/2024] [Indexed: 01/22/2024]
Abstract
This paper proposes an innovative approach to generate a generalized myocardial ischemia database by modeling the virtual electrophysiology of the heart and the 12-lead electrocardiography projected by the in-silico model can serve as a ready-to-use database for automatic myocardial infarction/ischemia (MI) localization and classification. Although the virtual heart can be created by an established technique combining the cell model with personalized heart geometry to observe the spatial propagation of depolarization and repolarization waves, we developed a strategy based on the clinical pathophysiology of MI to generate a heterogeneous database with a generic heart while maintaining clinical relevance and reduced computational complexity. First, the virtual heart is simplified into 11 regions that match the types and locations, which can be diagnosed by 12-lead ECG; the major arteries were divided into 3-5 segments from the upstream to the downstream based on the general anatomy. Second, the stenosis or infarction of the major or minor coronary artery branches can cause different perfusion drops and infarct sizes. We simulated the ischemic sites in different branches of the arteries by meandering the infarction location to elaborate on possible ECG representations, which alters the infraction's size and changes the transmembrane potential (TMP) of the myocytes associated with different levels of perfusion drop. A total of 8190 different case combinations of cardiac potentials with ischemia and MI were simulated, and the corresponding ECGs were generated by forward calculations. Finally, we trained and validated our in-silico database with a sparse representation classification (SRC) and tested the transferability of the model on the real-world Physikalisch Technische Bundesanstalt (PTB) database. The overall accuracies for localizing the MI region on the PTB data achieved 0.86, which is only 2% drop compared to that derived from the simulated database (0.88). In summary, we have shown a proof-of-concept for transferring an in-silico model to real-world database to compensate for insufficient data.
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Affiliation(s)
- Zeus Harnod
- Department of Biomedical Sciences and Engineering, National Central University, Taoyuan, Taiwan
| | - Chen Lin
- Department of Biomedical Sciences and Engineering, National Central University, Taoyuan, Taiwan
| | - Hui-Wen Yang
- Division of Sleep Medicine, Department of Medicine, Harvard Medical School, Boston, USA
| | - Zih-Wen Wang
- Department of Biomedical Sciences and Engineering, National Central University, Taoyuan, Taiwan
| | - Han-Luen Huang
- Department of Cardiology, Hsinchu Cathay General Hospital, Hsinchu, Taiwan
| | - Tse-Yu Lin
- Department of Biomedical Sciences and Engineering, National Central University, Taoyuan, Taiwan
| | - Chun-Yao Huang
- Department of Internal Medicine, Taipei Medical University Hospital, Taipei, Taiwan
| | - Lian-Yu Lin
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Hsu-Wen V Young
- Department of Electronic Engineering, Chung Yuan Christian University, Taoyuan, Taiwan.
| | - Men-Tzung Lo
- Department of Biomedical Sciences and Engineering, National Central University, Taoyuan, Taiwan.
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Kabus D, De Coster T, de Vries AAF, Pijnappels DA, Dierckx H. Fast creation of data-driven low-order predictive cardiac tissue excitation models from recorded activation patterns. Comput Biol Med 2024; 169:107949. [PMID: 38199206 DOI: 10.1016/j.compbiomed.2024.107949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 12/07/2023] [Accepted: 01/01/2024] [Indexed: 01/12/2024]
Abstract
Excitable systems give rise to important phenomena such as heat waves, epidemics and cardiac arrhythmias. Understanding, forecasting and controlling such systems requires reliable mathematical representations. For cardiac tissue, computational models are commonly generated in a reaction-diffusion framework based on detailed measurements of ionic currents in dedicated single-cell experiments. Here, we show that recorded movies at the tissue-level of stochastic pacing in a single variable are sufficient to generate a mathematical model. Via exponentially weighed moving averages, we create additional state variables, and use simple polynomial regression in the augmented state space to quantify excitation wave dynamics. A spatial gradient-sensing term replaces the classical diffusion as it is more robust to noise. Our pipeline for model creation is demonstrated for an in-silico model and optical voltage mapping recordings of cultured human atrial myocytes and only takes a few minutes. Our findings have the potential for widespread generation, use and on-the-fly refinement of personalised computer models for non-linear phenomena in biology and medicine, such as predictive cardiac digital twins.
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Affiliation(s)
- Desmond Kabus
- Department of Mathematics, KU Leuven Campus Kortrijk (KULAK), Etienne Sabbelaan 53, 8500, Kortrijk, Belgium; Laboratory of Experimental Cardiology, Leiden University Medical Center (LUMC), Albinusdreef 2, 2333 ZA, Leiden, The Netherlands
| | - Tim De Coster
- Laboratory of Experimental Cardiology, Leiden University Medical Center (LUMC), Albinusdreef 2, 2333 ZA, Leiden, The Netherlands
| | - Antoine A F de Vries
- Laboratory of Experimental Cardiology, Leiden University Medical Center (LUMC), Albinusdreef 2, 2333 ZA, Leiden, The Netherlands
| | - Daniël A Pijnappels
- Laboratory of Experimental Cardiology, Leiden University Medical Center (LUMC), Albinusdreef 2, 2333 ZA, Leiden, The Netherlands
| | - Hans Dierckx
- Department of Mathematics, KU Leuven Campus Kortrijk (KULAK), Etienne Sabbelaan 53, 8500, Kortrijk, Belgium.
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Qian S, Ugurlu D, Fairweather E, Strocchi M, Toso LD, Deng Y, Plank G, Vigmond E, Razavi R, Young A, Lamata P, Bishop M, Niederer S. Developing Cardiac Digital Twins at Scale: Insights from Personalised Myocardial Conduction Velocity. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.12.05.23299435. [PMID: 38106072 PMCID: PMC10723499 DOI: 10.1101/2023.12.05.23299435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Large-cohort studies using cardiovascular imaging and diagnostic datasets have assessed cardiac anatomy, function, and outcomes, but typically do not reveal underlying biological mechanisms. Cardiac digital twins (CDTs) provide personalized physics- and physiology-constrained in-silico representations, enabling inference of multi-scale properties tied to these mechanisms. We constructed 3464 anatomically-accurate CDTs using cardiac magnetic resonance images from UK biobank and personalised their myocardial conduction velocities (CVs) from electrocardiograms (ECG), through an automated framework. We found well-known sex-specific differences in QRS duration were fully explained by myocardial anatomy, as CV remained consistent across sexes. Conversely, significant associations of CV with ageing and increased BMI suggest myocardial tissue remodelling. Novel associations were observed with left ventricular ejection fraction and mental-health phenotypes, through a phenome-wide association study, and CV was also linked with adverse clinical outcomes. Our study highlights the utility of population-based CDTs in assessing intersubject variability and uncovering strong links with mental health.
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11
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Engelhardt E, Elzenheimer E, Hoffmann J, Meledeth C, Frey N, Schmidt G. Non-Invasive Electroanatomical Mapping: A State-Space Approach for Myocardial Current Density Estimation. Bioengineering (Basel) 2023; 10:1432. [PMID: 38136023 PMCID: PMC10741003 DOI: 10.3390/bioengineering10121432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Revised: 12/05/2023] [Accepted: 12/13/2023] [Indexed: 12/24/2023] Open
Abstract
Electroanatomical mapping is a method for creating a model of the electrophysiology of the human heart. Medical professionals routinely locate and ablate the site of origin of cardiac arrhythmias with invasive catheterization. Non-invasive localization takes the form of electrocardiographic (ECG) or magnetocardiographic (MCG) imaging, where the goal is to reconstruct the electrical activity of the human heart. Non-invasive alternatives to catheter electroanatomical mapping would reduce patients' risks and open new venues for treatment planning and prevention. This work introduces a new system state-based method for estimating the electrical activity of the human heart from MCG measurements. Our model enables arbitrary propagation paths and velocities. A Kalman filter optimally estimates the current densities under the given measurements and model parameters. In an outer optimization loop, these model parameters are then optimized via gradient descent. This paper aims to establish the foundation for future research by providing a detailed mathematical explanation of the algorithm. We demonstrate the feasibility of our method through a simplified one-layer simulation. Our results show that the algorithm can learn the propagation paths from the magnetic measurements. A threshold-based segmentation into healthy and pathological tissue yields a DICE score of 0.84, a recall of 0.77, and a precision of 0.93.
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Affiliation(s)
- Erik Engelhardt
- Department of Electrical Information Engineering, Faculty of Engineering, Kiel University, Kaiserstr. 2, 24143 Kiel, Germany; (E.E.); (E.E.)
| | - Eric Elzenheimer
- Department of Electrical Information Engineering, Faculty of Engineering, Kiel University, Kaiserstr. 2, 24143 Kiel, Germany; (E.E.); (E.E.)
| | - Johannes Hoffmann
- Department of Electrical Information Engineering, Faculty of Engineering, Kiel University, Kaiserstr. 2, 24143 Kiel, Germany; (E.E.); (E.E.)
| | - Christy Meledeth
- Internal Medicine 1—Cardiology and Internal Intensive Care Medicine, Med Campus III, Kepler University Hospital, Krankenhausstraße 9, 4021 Linz, Austria;
| | - Norbert Frey
- Department of Internal Medicine III (Cardiology, Angiology and Pneumonology), University Medical Center Heidelberg, Im Neuenheimer Feld 410, 69120 Heidelberg, Germany;
| | - Gerhard Schmidt
- Department of Electrical Information Engineering, Faculty of Engineering, Kiel University, Kaiserstr. 2, 24143 Kiel, Germany; (E.E.); (E.E.)
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Dumas M, Fay AF, Charpentier E, Matricon J. [Digital twins in healthcare: State of the art and potential use cases in a hospital setting]. Med Sci (Paris) 2023; 39:953-957. [PMID: 38108726 DOI: 10.1051/medsci/2023178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2023] Open
Abstract
The field of modeling and simulation including digital twins (DT) is getting a lot of traction in the health sector, with various benefits being outlined. Indeed, DT are designed to support the transformation of healthcare, ranging from the medical act itself to the organization of healthcare systems and hospitals, also including medical research. However, the technology is still in its infancy and needs to be evaluated according to medical standards. First and foremost, their clinical value remains to be assessed, taking into account the limits associated with AI tools. The aim of this review is to provide an overview of the use cases being tested in hospital settings. We go back to the definition of the DT concept and provide a summary of current studies. Finally, we outline the challenges to be taken into account in order to provide a framework for the deployment of DT tools, guaranteeing the trust and safety of those involved, especially of patients.
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Affiliation(s)
- Mathilde Dumas
- Cellule innovation du pôle transfert et innovation, Direction de la recherche clinique et de l'innovation (DRCI), Assistance Publique - Hôpitaux de Paris (AP-HP), Paris, France
| | - Anne-Florence Fay
- Cellule innovation du pôle transfert et innovation, Direction de la recherche clinique et de l'innovation (DRCI), Assistance Publique - Hôpitaux de Paris (AP-HP), Paris, France
| | - Emmanuel Charpentier
- Cellule innovation du pôle transfert et innovation, Direction de la recherche clinique et de l'innovation (DRCI), Assistance Publique - Hôpitaux de Paris (AP-HP), Paris, France
| | - Julien Matricon
- Cellule innovation du pôle transfert et innovation, Direction de la recherche clinique et de l'innovation (DRCI), Assistance Publique - Hôpitaux de Paris (AP-HP), Paris, France
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13
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Meijer C, Uh HW, el Bouhaddani S. Digital Twins in Healthcare: Methodological Challenges and Opportunities. J Pers Med 2023; 13:1522. [PMID: 37888133 PMCID: PMC10608065 DOI: 10.3390/jpm13101522] [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: 08/16/2023] [Revised: 10/14/2023] [Accepted: 10/15/2023] [Indexed: 10/28/2023] Open
Abstract
One of the most promising advancements in healthcare is the application of digital twin technology, offering valuable applications in monitoring, diagnosis, and development of treatment strategies tailored to individual patients. Furthermore, digital twins could also be helpful in finding novel treatment targets and predicting the effects of drugs and other chemical substances in development. In this review article, we consider digital twins as virtual counterparts of real human patients. The primary aim of this narrative review is to give an in-depth look into the various data sources and methodologies that contribute to the construction of digital twins across several healthcare domains. Each data source, including blood glucose levels, heart MRI and CT scans, cardiac electrophysiology, written reports, and multi-omics data, comes with different challenges regarding standardization, integration, and interpretation. We showcase how various datasets and methods are used to overcome these obstacles and generate a digital twin. While digital twin technology has seen significant progress, there are still hurdles in the way to achieving a fully comprehensive patient digital twin. Developments in non-invasive and high-throughput data collection, as well as advancements in modeling and computational power will be crucial to improve digital twin systems. We discuss a few critical developments in light of the current state of digital twin technology. Despite challenges, digital twin research holds great promise for personalized patient care and has the potential to shape the future of healthcare innovation.
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Affiliation(s)
| | | | - Said el Bouhaddani
- Department Data Science & Biostatistics, Julius Center, UMC Utrecht, 3584 CX Utrecht, The Netherlands (H.-W.U.)
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14
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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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15
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Azzolin L, Eichenlaub M, Nagel C, Nairn D, Sánchez J, Unger L, Arentz T, Westermann D, Dössel O, Jadidi A, Loewe A. AugmentA: Patient-specific augmented atrial model generation tool. Comput Med Imaging Graph 2023; 108:102265. [PMID: 37392493 DOI: 10.1016/j.compmedimag.2023.102265] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 01/07/2023] [Accepted: 06/03/2023] [Indexed: 07/03/2023]
Abstract
Digital twins of patients' hearts are a promising tool to assess arrhythmia vulnerability and to personalize therapy. However, the process of building personalized computational models can be challenging and requires a high level of human interaction. We propose a patient-specific Augmented Atria generation pipeline (AugmentA) as a highly automated framework which, starting from clinical geometrical data, provides ready-to-use atrial personalized computational models. AugmentA identifies and labels atrial orifices using only one reference point per atrium. If the user chooses to fit a statistical shape model to the input geometry, it is first rigidly aligned with the given mean shape before a non-rigid fitting procedure is applied. AugmentA automatically generates the fiber orientation and finds local conduction velocities by minimizing the error between the simulated and clinical local activation time (LAT) map. The pipeline was tested on a cohort of 29 patients on both segmented magnetic resonance images (MRI) and electroanatomical maps of the left atrium. Moreover, the pipeline was applied to a bi-atrial volumetric mesh derived from MRI. The pipeline robustly integrated fiber orientation and anatomical region annotations in 38.4 ± 5.7 s. In conclusion, AugmentA offers an automated and comprehensive pipeline delivering atrial digital twins from clinical data in procedural time.
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Affiliation(s)
- Luca Azzolin
- Institute of Biomedical Engineering at Karlsruhe Institute of Technology, Karlsruhe, Germany.
| | - Martin Eichenlaub
- University Heart Center Freiburg-Bad Krozingen, Bad Krozingen, Germany
| | - Claudia Nagel
- Institute of Biomedical Engineering at Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Deborah Nairn
- Institute of Biomedical Engineering at Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Jorge Sánchez
- Institute of Biomedical Engineering at Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Laura Unger
- Institute of Biomedical Engineering at Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Thomas Arentz
- University Heart Center Freiburg-Bad Krozingen, Bad Krozingen, Germany
| | - Dirk Westermann
- University Heart Center Freiburg-Bad Krozingen, Bad Krozingen, Germany
| | - Olaf Dössel
- Institute of Biomedical Engineering at Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Amir Jadidi
- University Heart Center Freiburg-Bad Krozingen, Bad Krozingen, Germany
| | - Axel Loewe
- Institute of Biomedical Engineering at Karlsruhe Institute of Technology, Karlsruhe, Germany
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16
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Sigawi T, Ilan Y. Using Constrained-Disorder Principle-Based Systems to Improve the Performance of Digital Twins in Biological Systems. Biomimetics (Basel) 2023; 8:359. [PMID: 37622964 PMCID: PMC10452845 DOI: 10.3390/biomimetics8040359] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 08/04/2023] [Accepted: 08/07/2023] [Indexed: 08/26/2023] Open
Abstract
Digital twins are computer programs that use real-world data to create simulations that predict the performance of processes, products, and systems. Digital twins may integrate artificial intelligence to improve their outputs. Models for dealing with uncertainties and noise are used to improve the accuracy of digital twins. Most currently used systems aim to reduce noise to improve their outputs. Nevertheless, biological systems are characterized by inherent variability, which is necessary for their proper function. The constrained-disorder principle defines living systems as having a disorder as part of their existence and proper operation while kept within dynamic boundaries. In the present paper, we review the role of noise in complex systems and its use in bioengineering. We describe the use of digital twins for medical applications and current methods for dealing with noise and uncertainties in modeling. The paper presents methods to improve the accuracy and effectiveness of digital twin systems by continuously implementing variability signatures while simultaneously reducing unwanted noise in their inputs and outputs. Accounting for the noisy internal and external environments of complex biological systems is necessary for the future design of improved, more accurate digital twins.
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Affiliation(s)
| | - Yaron Ilan
- Department of Medicine, Hadassah Medical Center, Faculty of Medicine, Hebrew University, Jerusalem P.O. Box 12000, Israel;
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17
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Gillette K, Gsell MAF, Nagel C, Bender J, Winkler B, Williams SE, Bär M, Schäffter T, Dössel O, Plank G, Loewe A. MedalCare-XL: 16,900 healthy and pathological synthetic 12 lead ECGs from electrophysiological simulations. Sci Data 2023; 10:531. [PMID: 37553349 PMCID: PMC10409805 DOI: 10.1038/s41597-023-02416-4] [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: 03/29/2023] [Accepted: 07/25/2023] [Indexed: 08/10/2023] Open
Abstract
Mechanistic cardiac electrophysiology models allow for personalized simulations of the electrical activity in the heart and the ensuing electrocardiogram (ECG) on the body surface. As such, synthetic signals possess known ground truth labels of the underlying disease and can be employed for validation of machine learning ECG analysis tools in addition to clinical signals. Recently, synthetic ECGs were used to enrich sparse clinical data or even replace them completely during training leading to improved performance on real-world clinical test data. We thus generated a novel synthetic database comprising a total of 16,900 12 lead ECGs based on electrophysiological simulations equally distributed into healthy control and 7 pathology classes. The pathological case of myocardial infraction had 6 sub-classes. A comparison of extracted features between the virtual cohort and a publicly available clinical ECG database demonstrated that the synthetic signals represent clinical ECGs for healthy and pathological subpopulations with high fidelity. The ECG database is split into training, validation, and test folds for development and objective assessment of novel machine learning algorithms.
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Affiliation(s)
- Karli Gillette
- Gottfried Schatz Research Center: Division of Medical Physics and Biophysics, Medical University of Graz, Graz, Austria
- BioTechMed-Graz, Graz, Austria
| | - Matthias A F Gsell
- Gottfried Schatz Research Center: Division of Medical Physics and Biophysics, Medical University of Graz, Graz, Austria
| | - Claudia Nagel
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | - Jule Bender
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | - Benjamin Winkler
- Physikalisch-Technische Bundesanstalt, National Metrology Institute, Berlin, Germany
| | - Steven E Williams
- King's College London, London, United Kingdom
- University of Edinburgh, Edinburgh, United Kingdom
| | - Markus Bär
- Physikalisch-Technische Bundesanstalt, National Metrology Institute, Berlin, Germany
| | - Tobias Schäffter
- Physikalisch-Technische Bundesanstalt, National Metrology Institute, Berlin, Germany
- King's College London, London, United Kingdom
- Biomedical Engineering, Technische Universität Berlin, Einstein Centre Digital Future, Berlin, Germany
| | - Olaf Dössel
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | - Gernot Plank
- Gottfried Schatz Research Center: Division of Medical Physics and Biophysics, Medical University of Graz, Graz, Austria.
- BioTechMed-Graz, Graz, Austria.
| | - Axel Loewe
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany.
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18
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Li L, Ding W, Huang L, Zhuang X, Grau V. Multi-modality cardiac image computing: A survey. Med Image Anal 2023; 88:102869. [PMID: 37384950 DOI: 10.1016/j.media.2023.102869] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 05/01/2023] [Accepted: 06/12/2023] [Indexed: 07/01/2023]
Abstract
Multi-modality cardiac imaging plays a key role in the management of patients with cardiovascular diseases. It allows a combination of complementary anatomical, morphological and functional information, increases diagnosis accuracy, and improves the efficacy of cardiovascular interventions and clinical outcomes. Fully-automated processing and quantitative analysis of multi-modality cardiac images could have a direct impact on clinical research and evidence-based patient management. However, these require overcoming significant challenges including inter-modality misalignment and finding optimal methods to integrate information from different modalities. This paper aims to provide a comprehensive review of multi-modality imaging in cardiology, the computing methods, the validation strategies, the related clinical workflows and future perspectives. For the computing methodologies, we have a favored focus on the three tasks, i.e., registration, fusion and segmentation, which generally involve multi-modality imaging data, either combining information from different modalities or transferring information across modalities. The review highlights that multi-modality cardiac imaging data has the potential of wide applicability in the clinic, such as trans-aortic valve implantation guidance, myocardial viability assessment, and catheter ablation therapy and its patient selection. Nevertheless, many challenges remain unsolved, such as missing modality, modality selection, combination of imaging and non-imaging data, and uniform analysis and representation of different modalities. There is also work to do in defining how the well-developed techniques fit in clinical workflows and how much additional and relevant information they introduce. These problems are likely to continue to be an active field of research and the questions to be answered in the future.
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Affiliation(s)
- Lei Li
- Department of Engineering Science, University of Oxford, Oxford, UK.
| | - Wangbin Ding
- College of Physics and Information Engineering, Fuzhou University, Fuzhou, China
| | - Liqin Huang
- College of Physics and Information Engineering, Fuzhou University, Fuzhou, China
| | - Xiahai Zhuang
- School of Data Science, Fudan University, Shanghai, China
| | - Vicente Grau
- Department of Engineering Science, University of Oxford, Oxford, UK
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19
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Dokuchaev A, Chumarnaya T, Bazhutina A, Khamzin S, Lebedeva V, Lyubimtseva T, Zubarev S, Lebedev D, Solovyova O. Combination of personalized computational modeling and machine learning for optimization of left ventricular pacing site in cardiac resynchronization therapy. Front Physiol 2023; 14:1162520. [PMID: 37497440 PMCID: PMC10367108 DOI: 10.3389/fphys.2023.1162520] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 06/26/2023] [Indexed: 07/28/2023] Open
Abstract
Introduction: The 30-50% non-response rate to cardiac resynchronization therapy (CRT) calls for improved patient selection and optimized pacing lead placement. The study aimed to develop a novel technique using patient-specific cardiac models and machine learning (ML) to predict an optimal left ventricular (LV) pacing site (ML-PS) that maximizes the likelihood of LV ejection fraction (LVEF) improvement in a given CRT candidate. To validate the approach, we evaluated whether the distance DPS between the clinical LV pacing site (ref-PS) and ML-PS is associated with improved response rate and magnitude. Materials and methods: We reviewed retrospective data for 57 CRT recipients. A positive response was defined as a more than 10% LVEF improvement. Personalized models of ventricular activation and ECG were created from MRI and CT images. The characteristics of ventricular activation during intrinsic rhythm and biventricular (BiV) pacing with ref-PS were derived from the models and used in combination with clinical data to train supervised ML classifiers. The best logistic regression model classified CRT responders with a high accuracy of 0.77 (ROC AUC = 0.84). The LR classifier, model simulations and Bayesian optimization with Gaussian process regression were combined to identify an optimal ML-PS that maximizes the ML-score of CRT response over the LV surface in each patient. Results: The optimal ML-PS improved the ML-score by 17 ± 14% over the ref-PS. Twenty percent of the non-responders were reclassified as positive at ML-PS. Selection of positive patients with a max ML-score >0.5 demonstrated an improved clinical response rate. The distance DPS was shorter in the responders. The max ML-score and DPS were found to be strong predictors of CRT response (ROC AUC = 0.85). In the group with max ML-score > 0.5 and DPS< 30 mm, the response rate was 83% compared to 14% in the rest of the cohort. LVEF improvement in this group was higher than in the other patients (16 ± 8% vs. 7 ± 8%). Conclusion: A new technique combining clinical data, personalized heart modelling and supervised ML demonstrates the potential for use in clinical practice to assist in optimizing patient selection and predicting optimal LV pacing lead position in HF candidates for CRT.
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Affiliation(s)
- Arsenii Dokuchaev
- Institute of Immunology and Physiology, Ural Branch of the Russian Academy of Sciences, Ekaterinburg, Russia
| | - Tatiana Chumarnaya
- Institute of Immunology and Physiology, Ural Branch of the Russian Academy of Sciences, Ekaterinburg, Russia
- Laboratory of Mathematical Modeling in Physiology and Medicine Based on Supercomputers, Ural Federal University, Ekaterinburg, Russia
| | - Anastasia Bazhutina
- Institute of Immunology and Physiology, Ural Branch of the Russian Academy of Sciences, Ekaterinburg, Russia
- Laboratory of Mathematical Modeling in Physiology and Medicine Based on Supercomputers, Ural Federal University, Ekaterinburg, Russia
| | - Svyatoslav Khamzin
- Institute of Immunology and Physiology, Ural Branch of the Russian Academy of Sciences, Ekaterinburg, Russia
| | | | - Tamara Lyubimtseva
- Institute of Immunology and Physiology, Ural Branch of the Russian Academy of Sciences, Ekaterinburg, Russia
- Almazov National Medical Research Centre, Saint Petersburg, Russia
| | - Stepan Zubarev
- Institute of Immunology and Physiology, Ural Branch of the Russian Academy of Sciences, Ekaterinburg, Russia
- Almazov National Medical Research Centre, Saint Petersburg, Russia
| | - Dmitry Lebedev
- Institute of Immunology and Physiology, Ural Branch of the Russian Academy of Sciences, Ekaterinburg, Russia
- Almazov National Medical Research Centre, Saint Petersburg, Russia
| | - Olga Solovyova
- Institute of Immunology and Physiology, Ural Branch of the Russian Academy of Sciences, Ekaterinburg, Russia
- Laboratory of Mathematical Modeling in Physiology and Medicine Based on Supercomputers, Ural Federal University, Ekaterinburg, Russia
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20
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Rodero C, Baptiste TMG, Barrows RK, Keramati H, Sillett CP, Strocchi M, Lamata P, Niederer SA. A systematic review of cardiac in-silico clinical trials. PROGRESS IN BIOMEDICAL ENGINEERING (BRISTOL, ENGLAND) 2023; 5:032004. [PMID: 37360227 PMCID: PMC10286106 DOI: 10.1088/2516-1091/acdc71] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 05/26/2023] [Accepted: 06/07/2023] [Indexed: 06/28/2023]
Abstract
Computational models of the heart are now being used to assess the effectiveness and feasibility of interventions through in-silico clinical trials (ISCTs). As the adoption and acceptance of ISCTs increases, best practices for reporting the methodology and analysing the results will emerge. Focusing in the area of cardiology, we aim to evaluate the types of ISCTs, their analysis methods and their reporting standards. To this end, we conducted a systematic review of cardiac ISCTs over the period of 1 January 2012-1 January 2022, following the preferred reporting items for systematic reviews and meta-analysis (PRISMA). We considered cardiac ISCTs of human patient cohorts, and excluded studies of single individuals and those in which models were used to guide a procedure without comparing against a control group. We identified 36 publications that described cardiac ISCTs, with most of the studies coming from the US and the UK. In 75% of the studies, a validation step was performed, although the specific type of validation varied between the studies. ANSYS FLUENT was the most commonly used software in 19% of ISCTs. The specific software used was not reported in 14% of the studies. Unlike clinical trials, we found a lack of consistent reporting of patient demographics, with 28% of the studies not reporting them. Uncertainty quantification was limited, with sensitivity analysis performed in only 19% of the studies. In 97% of the ISCTs, no link was provided to provide easy access to the data or models used in the study. There was no consistent naming of study types with a wide range of studies that could potentially be considered ISCTs. There is a clear need for community agreement on minimal reporting standards on patient demographics, accepted standards for ISCT cohort quality control, uncertainty quantification, and increased model and data sharing.
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Affiliation(s)
- Cristobal Rodero
- Cardiac Electro-Mechanics Research Group (CEMRG), National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Cardiac Electro-Mechanics Research Group (CEMRG), Department of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Cardiac Modelling and Imaging Biomarkers (CMIB), Department of Biomedical Engineering and Imaging Sciences Department, King’s College London, London, United Kingdom
| | - Tiffany M G Baptiste
- Cardiac Electro-Mechanics Research Group (CEMRG), National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Cardiac Electro-Mechanics Research Group (CEMRG), Department of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Rosie K Barrows
- Cardiac Electro-Mechanics Research Group (CEMRG), National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Cardiac Electro-Mechanics Research Group (CEMRG), Department of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Hamed Keramati
- Cardiac Electro-Mechanics Research Group (CEMRG), National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Cardiac Electro-Mechanics Research Group (CEMRG), Department of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Cardiac Modelling and Imaging Biomarkers (CMIB), Department of Biomedical Engineering and Imaging Sciences Department, King’s College London, London, United Kingdom
| | - Charles P Sillett
- Cardiac Electro-Mechanics Research Group (CEMRG), National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Cardiac Electro-Mechanics Research Group (CEMRG), Department of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Marina Strocchi
- Cardiac Electro-Mechanics Research Group (CEMRG), National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Cardiac Electro-Mechanics Research Group (CEMRG), Department of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Pablo Lamata
- Cardiac Modelling and Imaging Biomarkers (CMIB), Department of Biomedical Engineering and Imaging Sciences Department, King’s College London, London, United Kingdom
| | - Steven A Niederer
- Cardiac Electro-Mechanics Research Group (CEMRG), National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Cardiac Electro-Mechanics Research Group (CEMRG), Department of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Turing Research and Innovation Cluster in Digital Twins (TRIC: DT), The Alan Turing Institute, London, United Kingdom
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21
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Strodthoff N, Mehari T, Nagel C, Aston PJ, Sundar A, Graff C, Kanters JK, Haverkamp W, Dössel O, Loewe A, Bär M, Schaeffter T. PTB-XL+, a comprehensive electrocardiographic feature dataset. Sci Data 2023; 10:279. [PMID: 37179420 PMCID: PMC10183020 DOI: 10.1038/s41597-023-02153-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Accepted: 04/12/2023] [Indexed: 05/15/2023] Open
Abstract
Machine learning (ML) methods for the analysis of electrocardiography (ECG) data are gaining importance, substantially supported by the release of large public datasets. However, these current datasets miss important derived descriptors such as ECG features that have been devised in the past hundred years and still form the basis of most automatic ECG analysis algorithms and are critical for cardiologists' decision processes. ECG features are available from sophisticated commercial software but are not accessible to the general public. To alleviate this issue, we add ECG features from two leading commercial algorithms and an open-source implementation supplemented by a set of automatic diagnostic statements from a commercial ECG analysis software in preprocessed format. This allows the comparison of ML models trained on clinically versus automatically generated label sets. We provide an extensive technical validation of features and diagnostic statements for ML applications. We believe this release crucially enhances the usability of the PTB-XL dataset as a reference dataset for ML methods in the context of ECG data.
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Affiliation(s)
| | - Temesgen Mehari
- Physikalisch-Technische Bundesanstalt, Berlin, Germany
- Fraunhofer Heinrich Hertz Institute, Berlin, Germany
| | - Claudia Nagel
- Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Philip J Aston
- National Physical Laboratory, Teddington, UK
- University of Surrey, Guildford, UK
| | | | | | | | | | - Olaf Dössel
- Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Axel Loewe
- Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Markus Bär
- Physikalisch-Technische Bundesanstalt, Berlin, Germany
| | - Tobias Schaeffter
- Physikalisch-Technische Bundesanstalt, Berlin, Germany.
- TU Berlin, Berlin, Germany.
- King's College London, London, UK.
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22
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Hernández-Romero I, Molero R, Fambuena-Santos C, Herrero-Martín C, Climent AM, Guillem MS. Electrocardiographic imaging in the atria. Med Biol Eng Comput 2023; 61:879-896. [PMID: 36370321 PMCID: PMC9988819 DOI: 10.1007/s11517-022-02709-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 10/26/2022] [Indexed: 11/13/2022]
Abstract
The inverse problem of electrocardiography or electrocardiographic imaging (ECGI) is a technique for reconstructing electrical information about cardiac surfaces from noninvasive or non-contact recordings. ECGI has been used to characterize atrial and ventricular arrhythmias. Although it is a technology with years of progress, its development to characterize atrial arrhythmias is challenging. Complications can arise when trying to describe the atrial mechanisms that lead to abnormal propagation patterns, premature or tachycardic beats, and reentrant arrhythmias. This review addresses the various ECGI methodologies, regularization methods, and post-processing techniques used in the atria, as well as the context in which they are used. The current advantages and limitations of ECGI in the fields of research and clinical diagnosis of atrial arrhythmias are outlined. In addition, areas where ECGI efforts should be concentrated to address the associated unsatisfied needs from the atrial perspective are discussed.
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Affiliation(s)
| | - Rubén Molero
- ITACA, Universitat Politècnica de València, Valencia, Spain
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23
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Bucelli M, Zingaro A, Africa PC, Fumagalli I, Dede' L, Quarteroni A. A mathematical model that integrates cardiac electrophysiology, mechanics, and fluid dynamics: Application to the human left heart. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2023; 39:e3678. [PMID: 36579792 DOI: 10.1002/cnm.3678] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 12/13/2022] [Accepted: 12/19/2022] [Indexed: 06/17/2023]
Abstract
We propose a mathematical and numerical model for the simulation of the heart function that couples cardiac electrophysiology, active and passive mechanics and hemodynamics, and includes reduced models for cardiac valves and the circulatory system. Our model accounts for the major feedback effects among the different processes that characterize the heart function, including electro-mechanical and mechano-electrical feedback as well as force-strain and force-velocity relationships. Moreover, it provides a three-dimensional representation of both the cardiac muscle and the hemodynamics, coupled in a fluid-structure interaction (FSI) model. By leveraging the multiphysics nature of the problem, we discretize it in time with a segregated electrophysiology-force generation-FSI approach, allowing for efficiency and flexibility in the numerical solution. We employ a monolithic approach for the numerical discretization of the FSI problem. We use finite elements for the spatial discretization of partial differential equations. We carry out a numerical simulation on a realistic human left heart model, obtaining results that are qualitatively and quantitatively in agreement with physiological ranges and medical images.
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Affiliation(s)
- Michele Bucelli
- MOX, Dipartimento di Matematica, Politecnico di Milano, Milan, Italy
| | - Alberto Zingaro
- MOX, Dipartimento di Matematica, Politecnico di Milano, Milan, Italy
| | | | - Ivan Fumagalli
- MOX, Dipartimento di Matematica, Politecnico di Milano, Milan, Italy
| | - Luca Dede'
- MOX, Dipartimento di Matematica, Politecnico di Milano, Milan, Italy
| | - Alfio Quarteroni
- MOX, Dipartimento di Matematica, Politecnico di Milano, Milan, Italy
- Institute of Mathematics, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
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24
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Rinné S, Oertli A, Nagel C, Tomsits P, Jenewein T, Kääb S, Kauferstein S, Loewe A, Beckmann BM, Decher N. Functional Characterization of a Spectrum of Novel Romano-Ward Syndrome KCNQ1 Variants. Int J Mol Sci 2023; 24:ijms24021350. [PMID: 36674868 PMCID: PMC9865342 DOI: 10.3390/ijms24021350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 12/20/2022] [Accepted: 12/23/2022] [Indexed: 01/13/2023] Open
Abstract
The KCNQ1 gene encodes the α-subunit of the cardiac voltage-gated potassium (Kv) channel KCNQ1, also denoted as Kv7.1 or KvLQT1. The channel assembles with the ß-subunit KCNE1, also known as minK, to generate the slowly activating cardiac delayed rectifier current IKs, a key regulator of the heart rate dependent adaptation of the cardiac action potential duration (APD). Loss-of-function variants in KCNQ1 cause the congenital Long QT1 (LQT1) syndrome, characterized by delayed cardiac repolarization and a QT interval prolongation in the surface electrocardiogram (ECG). Autosomal dominant loss-of-function variants in KCNQ1 result in the LQT syndrome called Romano-Ward syndrome (RWS), while autosomal recessive variants affecting function, lead to Jervell and Lange-Nielsen syndrome (JLNS), associated with deafness. The aim of this study was the characterization of novel KCNQ1 variants identified in patients with RWS to widen the spectrum of known LQT1 variants, and improve the interpretation of the clinical relevance of variants in the KCNQ1 gene. We functionally characterized nine human KCNQ1 variants using the voltage-clamp technique in Xenopus laevis oocytes, from which we report seven novel variants. The functional data was taken as input to model surface ECGs, to subsequently compare the functional changes with the clinically observed QTc times, allowing a further interpretation of the severity of the different LQTS variants. We found that the electrophysiological properties of the variants correlate with the severity of the clinically diagnosed phenotype in most cases, however, not in all. Electrophysiological studies combined with in silico modelling approaches are valuable components for the interpretation of the pathogenicity of KCNQ1 variants, but assessing the clinical severity demands the consideration of other factors that are included, for example in the Schwartz score.
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Affiliation(s)
- Susanne Rinné
- Institute of Physiology and Pathophysiology, Vegetative Physiology, University of Marburg, 35037 Marburg, Germany
| | - Annemarie Oertli
- Institute of Physiology and Pathophysiology, Vegetative Physiology, University of Marburg, 35037 Marburg, Germany
| | - Claudia Nagel
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, Germany
| | - Philipp Tomsits
- Department of Medicine I, University Hospital, LMU Munich, 80802 Munich, Germany
- Deutsches Zentrum für Herz-Kreislauferkrankungen (DZHK), Partner Site Munich, 80636 Munich, Germany
- Member of the European Reference Network for Rare, Low Prevalance and Complex Diseases of the Heart (ERN GUARD-Heart), 81377 Munich, Germany
- Institute of Surgical Research at the Walter-Brendel-Centre of Experimental Medicine, University Hospital, LMU Munich, Marchioninistrasse 27, 81377 Munich, Germany
| | - Tina Jenewein
- Institute of Legal Medicine, Goethe University, University Hospital Frankfurt, 60590 Frankfurt, Germany
- Institute for Transfusion Medicine and Immunohematology, German Red Cross Blood Service Baden-Württemberg-Hessen, Goethe University Frankfurt, 60528 Frankfurt, Germany
| | - Stefan Kääb
- Department of Medicine I, University Hospital, LMU Munich, 80802 Munich, Germany
- Deutsches Zentrum für Herz-Kreislauferkrankungen (DZHK), Partner Site Munich, 80636 Munich, Germany
- Member of the European Reference Network for Rare, Low Prevalance and Complex Diseases of the Heart (ERN GUARD-Heart), 81377 Munich, Germany
| | - Silke Kauferstein
- Institute of Legal Medicine, Goethe University, University Hospital Frankfurt, 60590 Frankfurt, Germany
- Deutsches Zentrum für Herz-Kreislauferkrankungen (DZHK), Partner Site Frankfurt, 60596 Frankfurt, Germany
| | - Axel Loewe
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, Germany
| | - Britt Maria Beckmann
- Department of Medicine I, University Hospital, LMU Munich, 80802 Munich, Germany
- Institute of Legal Medicine, Goethe University, University Hospital Frankfurt, 60590 Frankfurt, Germany
| | - Niels Decher
- Institute of Physiology and Pathophysiology, Vegetative Physiology, University of Marburg, 35037 Marburg, Germany
- Correspondence: ; Tel.: +49-(0)6421-28-62148
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25
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Narayan A, Liu Z, Bergquist JA, Charlebois C, Rampersad S, Rupp L, Brooks D, White D, Tate J, MacLeod RS. UncertainSCI: Uncertainty quantification for computational models in biomedicine and bioengineering. Comput Biol Med 2023; 152:106407. [PMID: 36521358 PMCID: PMC9812870 DOI: 10.1016/j.compbiomed.2022.106407] [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: 08/16/2022] [Revised: 11/07/2022] [Accepted: 12/03/2022] [Indexed: 12/12/2022]
Abstract
BACKGROUND Computational biomedical simulations frequently contain parameters that model physical features, material coefficients, and physiological effects, whose values are typically assumed known a priori. Understanding the effect of variability in those assumed values is currently a topic of great interest. A general-purpose software tool that quantifies how variation in these parameters affects model outputs is not broadly available in biomedicine. For this reason, we developed the 'UncertainSCI' uncertainty quantification software suite to facilitate analysis of uncertainty due to parametric variability. METHODS We developed and distributed a new open-source Python-based software tool, UncertainSCI, which employs advanced parameter sampling techniques to build polynomial chaos (PC) emulators that can be used to predict model outputs for general parameter values. Uncertainty of model outputs is studied by modeling parameters as random variables, and model output statistics and sensitivities are then easily computed from the emulator. Our approaches utilize modern, near-optimal techniques for sampling and PC construction based on weighted Fekete points constructed by subsampling from a suitably randomized candidate set. RESULTS Concentrating on two test cases-modeling bioelectric potentials in the heart and electric stimulation in the brain-we illustrate the use of UncertainSCI to estimate variability, statistics, and sensitivities associated with multiple parameters in these models. CONCLUSION UncertainSCI is a powerful yet lightweight tool enabling sophisticated probing of parametric variability and uncertainty in biomedical simulations. Its non-intrusive pipeline allows users to leverage existing software libraries and suites to accurately ascertain parametric uncertainty in a variety of applications.
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Affiliation(s)
- Akil Narayan
- Scientific Computing and Imaging Institute, University of Utah, 72 Central Campus Dr, Salt Lake City, UT, 84112, United States; Department of Mathematics, University of Utah, 72 Central Campus Dr, Salt Lake City, UT, 84112, United States.
| | - Zexin Liu
- Scientific Computing and Imaging Institute, University of Utah, 72 Central Campus Dr, Salt Lake City, UT, 84112, United States; Department of Mathematics, University of Utah, 72 Central Campus Dr, Salt Lake City, UT, 84112, United States
| | - Jake A Bergquist
- Scientific Computing and Imaging Institute, University of Utah, 72 Central Campus Dr, Salt Lake City, UT, 84112, United States; Department of Biomedical Engineering, University of Utah, 72 Central Campus Dr, Salt Lake City, UT, 84112, United States
| | - Chantel Charlebois
- Scientific Computing and Imaging Institute, University of Utah, 72 Central Campus Dr, Salt Lake City, UT, 84112, United States; Department of Biomedical Engineering, University of Utah, 72 Central Campus Dr, Salt Lake City, UT, 84112, United States
| | - Sumientra Rampersad
- Department of Physics, University of Massachusetts Boston, Boston, MA, USA; Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, USA
| | - Lindsay Rupp
- Scientific Computing and Imaging Institute, University of Utah, 72 Central Campus Dr, Salt Lake City, UT, 84112, United States; Department of Biomedical Engineering, University of Utah, 72 Central Campus Dr, Salt Lake City, UT, 84112, United States
| | - Dana Brooks
- Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, USA
| | - Dan White
- Scientific Computing and Imaging Institute, University of Utah, 72 Central Campus Dr, Salt Lake City, UT, 84112, United States; Department of Biomedical Engineering, University of Utah, 72 Central Campus Dr, Salt Lake City, UT, 84112, United States
| | - Jess Tate
- Scientific Computing and Imaging Institute, University of Utah, 72 Central Campus Dr, Salt Lake City, UT, 84112, United States; Department of Biomedical Engineering, University of Utah, 72 Central Campus Dr, Salt Lake City, UT, 84112, United States
| | - Rob S MacLeod
- Scientific Computing and Imaging Institute, University of Utah, 72 Central Campus Dr, Salt Lake City, UT, 84112, United States; Department of Biomedical Engineering, University of Utah, 72 Central Campus Dr, Salt Lake City, UT, 84112, United States
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26
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Baumgartner C. The world's first digital cell twin in cancer electrophysiology: a digital revolution in cancer research? JOURNAL OF EXPERIMENTAL & CLINICAL CANCER RESEARCH : CR 2022; 41:298. [PMID: 36221111 PMCID: PMC9552501 DOI: 10.1186/s13046-022-02507-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Accepted: 09/30/2022] [Indexed: 11/07/2022]
Abstract
Background The introduction of functional in-silico models, in addition to in-vivo tumor models, opens up new and unlimited possibilities in cancer research and drug development. The world's first digital twin of the A549 cell's electrophysiology in the human lung adenocarcinoma, unveiled in 2021, enables the investigation and evaluation of new research hypotheses about modulating the function of ion channels in the cell membrane, which are important for better understanding cancer development and progression, as well as for developing new drugs and predicting treatments. Main body The developed A549 in-silico model allows virtual simulations of the cell’s rhythmic oscillation of the membrane potential, which can trigger the transition between cell cycle phases. It is able to predict the promotion or interruption of cell cycle progression provoked by targeted activation and inactivation of ion channels, resulting in abnormal hyper- or depolarization of the membrane potential, a potential key signal for the known cancer hallmarks. For example, model simulations of blockade of transient receptor potential cation channels (TRPC6), which are highly expressed during S-G2/M transition, result in a strong hyperpolarization of the cell’s membrane potential that can suppress or bypass the depolarization required for the S-G2/M transition, allowing for possible cell cycle arrest and inhibition of mitosis. All simulated research hypotheses could be verified by experimental studies. Short conclusion Functional, non-phenomenological digital twins, ranging from single cells to cell–cell interactions to 3D tissue models, open new avenues for modern cancer research through "dry lab" approaches that optimally complement established in-vivo and in-vitro methods.
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Affiliation(s)
- Christian Baumgartner
- grid.410413.30000 0001 2294 748XInstitute of Health Care Engineering With European Testing Center of Medical Devices, Computational Cancer Electrophysiology Lab, Graz University of Technology, 8010 Graz, Austria
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27
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Galappaththige S, Gray RA, Costa CM, Niederer S, Pathmanathan P. Credibility assessment of patient-specific computational modeling using patient-specific cardiac modeling as an exemplar. PLoS Comput Biol 2022; 18:e1010541. [PMID: 36215228 PMCID: PMC9550052 DOI: 10.1371/journal.pcbi.1010541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 09/02/2022] [Indexed: 11/07/2022] Open
Abstract
Reliable and robust simulation of individual patients using patient-specific models (PSMs) is one of the next frontiers for modeling and simulation (M&S) in healthcare. PSMs, which form the basis of digital twins, can be employed as clinical tools to, for example, assess disease state, predict response to therapy, or optimize therapy. They may also be used to construct virtual cohorts of patients, for in silico evaluation of medical product safety and/or performance. Methods and frameworks have recently been proposed for evaluating the credibility of M&S in healthcare applications. However, such efforts have generally been motivated by models of medical devices or generic patient models; how best to evaluate the credibility of PSMs has largely been unexplored. The aim of this paper is to understand and demonstrate the credibility assessment process for PSMs using patient-specific cardiac electrophysiological (EP) modeling as an exemplar. We first review approaches used to generate cardiac PSMs and consider how verification, validation, and uncertainty quantification (VVUQ) apply to cardiac PSMs. Next, we execute two simulation studies using a publicly available virtual cohort of 24 patient-specific ventricular models, the first a multi-patient verification study, the second investigating the impact of uncertainty in personalized and non-personalized inputs in a virtual cohort. We then use the findings from our analyses to identify how important characteristics of PSMs can be considered when assessing credibility with the approach of the ASME V&V40 Standard, accounting for PSM concepts such as inter- and intra-user variability, multi-patient and “every-patient” error estimation, uncertainty quantification in personalized vs non-personalized inputs, clinical validation, and others. The results of this paper will be useful to developers of cardiac and other medical image based PSMs, when assessing PSM credibility. Patient-specific models are computational models that have been personalized using data from a patient. After decades of research, recent computational, data science and healthcare advances have opened the door to the fulfilment of the enormous potential of such models, from truly personalized medicine to efficient and cost-effective testing of new medical products. However, reliability (credibility) of patient-specific models is key to their success, and there are currently no general guidelines for evaluating credibility of patient-specific models. Here, we consider how frameworks and model evaluation activities that have been developed for generic (not patient-specific) computational models, can be extended to patient specific models. We achieve this through a detailed analysis of the activities required to evaluate cardiac electrophysiological models, chosen as an exemplar field due to its maturity and the complexity of such models. This is the first paper on the topic of reliability of patient-specific models and will help pave the way to reliable and trusted patient-specific modeling across healthcare applications.
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Affiliation(s)
- Suran Galappaththige
- Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland, United States of America
| | - Richard A. Gray
- Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland, United States of America
| | - Caroline Mendonca Costa
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | - Steven Niederer
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | - Pras Pathmanathan
- Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland, United States of America
- * E-mail:
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28
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Gillette K, Gsell MAF, Strocchi M, Grandits T, Neic A, Manninger M, Scherr D, Roney CH, Prassl AJ, Augustin CM, Vigmond EJ, Plank G. A personalized real-time virtual model of whole heart electrophysiology. Front Physiol 2022; 13:907190. [PMID: 36213235 PMCID: PMC9539798 DOI: 10.3389/fphys.2022.907190] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 08/15/2022] [Indexed: 01/12/2023] Open
Abstract
Computer models capable of representing the intrinsic personal electrophysiology (EP) of the heart in silico are termed virtual heart technologies. When anatomy and EP are tailored to individual patients within the model, such technologies are promising clinical and industrial tools. Regardless of their vast potential, few virtual technologies simulating the entire organ-scale EP of all four-chambers of the heart have been reported and widespread clinical use is limited due to high computational costs and difficulty in validation. We thus report on the development of a novel virtual technology representing the electrophysiology of all four-chambers of the heart aiming to overcome these limitations. In our previous work, a model of ventricular EP embedded in a torso was constructed from clinical magnetic resonance image (MRI) data and personalized according to the measured 12 lead electrocardiogram (ECG) of a single subject under normal sinus rhythm. This model is then expanded upon to include whole heart EP and a detailed representation of the His-Purkinje system (HPS). To test the capacities of the personalized virtual heart technology to replicate standard clinical morphological ECG features under such conditions, bundle branch blocks within both the right and the left ventricles under two different conduction velocity settings are modeled alongside sinus rhythm. To ensure clinical viability, model generation was completely automated and simulations were performed using an efficient real-time cardiac EP simulator. Close correspondence between the measured and simulated 12 lead ECG was observed under normal sinus conditions and all simulated bundle branch blocks manifested relevant clinical morphological features.
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Affiliation(s)
- Karli Gillette
- Gottfried Schatz Research Center—Biophysics, Medical University of Graz, Graz, Austria
- BioTechMed-Graz, Graz, Austria
| | - Matthias A. F. Gsell
- Gottfried Schatz Research Center—Biophysics, Medical University of Graz, Graz, Austria
- NAWI Graz, Institute of Mathematics and Scientific Computing, University of Graz, Graz, Austria
| | | | - Thomas Grandits
- Gottfried Schatz Research Center—Biophysics, Medical University of Graz, Graz, Austria
- NAWI Graz, Institute of Mathematics and Scientific Computing, University of Graz, Graz, Austria
| | | | - Martin Manninger
- Division of Cardiology, Department of Internal Medicine, Medical University of Graz, Graz, Austria
| | - Daniel Scherr
- Division of Cardiology, Department of Internal Medicine, Medical University of Graz, Graz, Austria
| | | | - Anton J. Prassl
- Gottfried Schatz Research Center—Biophysics, Medical University of Graz, Graz, Austria
- BioTechMed-Graz, Graz, Austria
| | - Christoph M. Augustin
- Gottfried Schatz Research Center—Biophysics, Medical University of Graz, Graz, Austria
- BioTechMed-Graz, Graz, Austria
| | | | - Gernot Plank
- Gottfried Schatz Research Center—Biophysics, Medical University of Graz, Graz, Austria
- BioTechMed-Graz, Graz, Austria
- *Correspondence: Gernot Plank,
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29
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Bai J, Lu Y, Wang H, Zhao J. How synergy between mechanistic and statistical models is impacting research in atrial fibrillation. Front Physiol 2022; 13:957604. [PMID: 36111152 PMCID: PMC9468674 DOI: 10.3389/fphys.2022.957604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 08/08/2022] [Indexed: 11/13/2022] Open
Abstract
Atrial fibrillation (AF) with multiple complications, high morbidity and mortality, and low cure rates, has become a global public health problem. Although significant progress has been made in the treatment methods represented by anti-AF drugs and radiofrequency ablation, the therapeutic effect is not as good as expected. The reason is mainly because of our lack of understanding of AF mechanisms. This field has benefited from mechanistic and (or) statistical methodologies. Recent renewed interest in digital twin techniques by synergizing between mechanistic and statistical models has opened new frontiers in AF analysis. In the review, we briefly present findings that gave rise to the AF pathophysiology and current therapeutic modalities. We then summarize the achievements of digital twin technologies in three aspects: understanding AF mechanisms, screening anti-AF drugs and optimizing ablation strategies. Finally, we discuss the challenges that hinder the clinical application of the digital twin heart. With the rapid progress in data reuse and sharing, we expect their application to realize the transition from AF description to response prediction.
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Affiliation(s)
- Jieyun Bai
- Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Information Technology, Jinan University, Guangzhou, China
- College of Information Science and Technology, Jinan University, Guangzhou, China
- *Correspondence: Jieyun Bai, ; Jichao Zhao,
| | - Yaosheng Lu
- Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Information Technology, Jinan University, Guangzhou, China
- College of Information Science and Technology, Jinan University, Guangzhou, China
| | - Huijin Wang
- College of Information Science and Technology, Jinan University, Guangzhou, China
| | - Jichao Zhao
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
- *Correspondence: Jieyun Bai, ; Jichao Zhao,
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30
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Coorey G, Figtree GA, Fletcher DF, Snelson VJ, Vernon ST, Winlaw D, Grieve SM, McEwan A, Yang JYH, Qian P, O'Brien K, Orchard J, Kim J, Patel S, Redfern J. The health digital twin to tackle cardiovascular disease-a review of an emerging interdisciplinary field. NPJ Digit Med 2022; 5:126. [PMID: 36028526 PMCID: PMC9418270 DOI: 10.1038/s41746-022-00640-7] [Citation(s) in RCA: 43] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Accepted: 06/24/2022] [Indexed: 11/16/2022] Open
Abstract
Potential benefits of precision medicine in cardiovascular disease (CVD) include more accurate phenotyping of individual patients with the same condition or presentation, using multiple clinical, imaging, molecular and other variables to guide diagnosis and treatment. An approach to realising this potential is the digital twin concept, whereby a virtual representation of a patient is constructed and receives real-time updates of a range of data variables in order to predict disease and optimise treatment selection for the real-life patient. We explored the term digital twin, its defining concepts, the challenges as an emerging field, and potentially important applications in CVD. A mapping review was undertaken using a systematic search of peer-reviewed literature. Industry-based participants and patent applications were identified through web-based sources. Searches of Compendex, EMBASE, Medline, ProQuest and Scopus databases yielded 88 papers related to cardiovascular conditions (28%, n = 25), non-cardiovascular conditions (41%, n = 36), and general aspects of the health digital twin (31%, n = 27). Fifteen companies with a commercial interest in health digital twin or simulation modelling had products focused on CVD. The patent search identified 18 applications from 11 applicants, of which 73% were companies and 27% were universities. Three applicants had cardiac-related inventions. For CVD, digital twin research within industry and academia is recent, interdisciplinary, and established globally. Overall, the applications were numerical simulation models, although precursor models exist for the real-time cyber-physical system characteristic of a true digital twin. Implementation challenges include ethical constraints and clinical barriers to the adoption of decision tools derived from artificial intelligence systems.
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Affiliation(s)
- Genevieve Coorey
- University of Sydney, Faculty of Medicine and Health, Sydney, NSW, Australia. .,The George Institute for Global Health, Sydney, NSW, Australia.
| | - Gemma A Figtree
- University of Sydney, Faculty of Medicine and Health, Sydney, NSW, Australia.,Kolling Institute of Medical Research, Royal North Shore Hospital, Sydney, NSW, Australia
| | - David F Fletcher
- University of Sydney, School of Chemical and Biomolecular Engineering, Sydney, NSW, Australia
| | - Victoria J Snelson
- University of Sydney, Faculty of Medicine and Health, Sydney, NSW, Australia.,University of Sydney, Charles Perkins Centre, Sydney, NSW, Australia
| | - Stephen Thomas Vernon
- Kolling Institute of Medical Research, Royal North Shore Hospital, Sydney, NSW, Australia.,Department of Cardiology, Royal North Shore Hospital, Sydney, NSW, Australia
| | - David Winlaw
- Cincinnati Children's Hospital Medical Cente, Cincinnati, OH, USA
| | - Stuart M Grieve
- University of Sydney, Faculty of Medicine and Health, Sydney, NSW, Australia.,University of Sydney, Charles Perkins Centre, Sydney, NSW, Australia
| | - Alistair McEwan
- The University of Sydney, School of Biomedical Engineering, Sydney, NSW, Australia
| | - Jean Yee Hwa Yang
- University of Sydney, Charles Perkins Centre, Sydney, NSW, Australia
| | - Pierre Qian
- University of Sydney, Faculty of Medicine and Health, Sydney, NSW, Australia.,Westmead Applied Research Centre, Westmead Hospital, Sydney, NSW, Australia
| | - Kieran O'Brien
- Siemens Healthcare Pty Ltd; and Centre for Advanced Imaging, University of Queensland, Brisbane, QLD, Australia
| | - Jessica Orchard
- University of Sydney, Charles Perkins Centre, Sydney, NSW, Australia
| | - Jinman Kim
- University of Sydney, School of Computer Science, Sydney, NSW, Australia
| | - Sanjay Patel
- University of Sydney, Faculty of Medicine and Health, Sydney, NSW, Australia.,Royal Prince Alfred Hospital, Sydney, NSW, Australia.,Heart Research Institute, Sydney, NSW, Australia
| | - Julie Redfern
- University of Sydney, Faculty of Medicine and Health, Sydney, NSW, Australia
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31
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Campos FO, Neic A, Mendonca Costa C, Whitaker J, O'Neill M, Razavi R, Rinaldi CA, DanielScherr, Niederer SA, Plank G, Bishop MJ. An automated near-real time computational method for induction and treatment of scar-related ventricular tachycardias. Med Image Anal 2022; 80:102483. [PMID: 35667328 PMCID: PMC10114098 DOI: 10.1016/j.media.2022.102483] [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: 08/30/2021] [Revised: 04/22/2022] [Accepted: 05/20/2022] [Indexed: 02/05/2023]
Abstract
Catheter ablation is currently the only curative treatment for scar-related ventricular tachycardias (VTs). However, not only are ablation procedures long, with relatively high risk, but success rates are punitively low, with frequent VT recurrence. Personalized in-silico approaches have the opportunity to address these limitations. However, state-of-the-art reaction diffusion (R-D) simulations of VT induction and subsequent circuits used for in-silico ablation target identification require long execution times, along with vast computational resources, which are incompatible with the clinical workflow. Here, we present the Virtual Induction and Treatment of Arrhythmias (VITA), a novel, rapid and fully automated computational approach that uses reaction-Eikonal methodology to induce VT and identify subsequent ablation targets. The rationale for VITA is based on finding isosurfaces associated with an activation wavefront that splits in the ventricles due to the presence of an isolated isthmus of conduction within the scar; once identified, each isthmus may be assessed for their vulnerability to sustain a reentrant circuit, and the corresponding exit site automatically identified for potential ablation targeting. VITA was tested on a virtual cohort of 7 post-infarcted porcine hearts and the results compared to R-D simulations. Using only a standard desktop machine, VITA could detect all scar-related VTs, simulating activation time maps and ECGs (for clinical comparison) as well as computing ablation targets in 48 minutes. The comparable VTs probed by the R-D simulations took 68.5 hours on 256 cores of high-performance computing infrastructure. The set of lesions computed by VITA was shown to render the ventricular model VT-free. VITA could be used in near real-time as a complementary modality aiding in clinical decision-making in the treatment of post-infarction VTs.
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Affiliation(s)
- Fernando O Campos
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
| | | | - Caroline Mendonca Costa
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - John Whitaker
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Guy's and St. Thomas' NHS Foundation Trust, Cardiovascular Directorate
| | - Mark O'Neill
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Guy's and St. Thomas' NHS Foundation Trust, Cardiovascular Directorate
| | - Reza Razavi
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Christopher A Rinaldi
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Guy's and St. Thomas' NHS Foundation Trust, Cardiovascular Directorate
| | - DanielScherr
- Division of Cardiology, Department of Internal Medicine, Medical University of Graz, Austria
| | - Steven A Niederer
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Gernot Plank
- Gottfried Schatz Research Center Biophysics, Medical University of Graz, Graz, Austria; BioTechMed-Graz, Graz, Austria
| | - Martin J Bishop
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
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Moss R, Wülfers EM, Lewetag R, Hornyik T, Perez-Feliz S, Strohbach T, Menza M, Krafft A, Odening KE, Seemann G. A computational model of rabbit geometry and ECG: Optimizing ventricular activation sequence and APD distribution. PLoS One 2022; 17:e0270559. [PMID: 35771854 PMCID: PMC9246225 DOI: 10.1371/journal.pone.0270559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Accepted: 06/13/2022] [Indexed: 11/19/2022] Open
Abstract
Computational modeling of electrophysiological properties of the rabbit heart is a commonly used way to enhance and/or complement findings from classic lab work on single cell or tissue levels. Yet, thus far, there was no possibility to extend the scope to include the resulting body surface potentials as a way of validation or to investigate the effect of certain pathologies. Based on CT imaging, we developed the first openly available computational geometrical model not only of the whole heart but also the complete torso of the rabbit. Additionally, we fabricated a 32-lead ECG-vest to record body surface potential signals of the aforementioned rabbit. Based on the developed geometrical model and the measured signals, we then optimized the activation sequence of the ventricles, recreating the functionality of the Purkinje network, and we investigated different apico-basal and transmural gradients in action potential duration. Optimization of the activation sequence resulted in an average root mean square error between measured and simulated signal of 0.074 mV/ms for all leads. The best-fit T-Wave, compared to measured data (0.038 mV/ms), resulted from incorporating an action potential duration gradient from base to apex with a respective shortening of 20 ms and a transmural gradient with a shortening of 15 ms from endocardium to epicardium. By making our model and measured data openly available, we hope to give other researchers the opportunity to verify their research, as well as to create the possibility to investigate the impact of electrophysiological alterations on body surface signals for translational research.
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Affiliation(s)
- Robin Moss
- Institute for Experimental Cardiovascular Medicine, University Heart Center Freiburg ⋅ Bad Krozingen, Medical Center—University of Freiburg, Freiburg, Germany
- Faculty of Medicine, University of Freiburg, Freiburg, Germany
- * E-mail:
| | - Eike M. Wülfers
- Institute for Experimental Cardiovascular Medicine, University Heart Center Freiburg ⋅ Bad Krozingen, Medical Center—University of Freiburg, Freiburg, Germany
- Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Raphaela Lewetag
- Institute for Experimental Cardiovascular Medicine, University Heart Center Freiburg ⋅ Bad Krozingen, Medical Center—University of Freiburg, Freiburg, Germany
- Department of Cardiology and Angiology I, Heart Center University of Freiburg, Medical Faculty, Freiburg, Germany
| | - Tibor Hornyik
- Institute for Experimental Cardiovascular Medicine, University Heart Center Freiburg ⋅ Bad Krozingen, Medical Center—University of Freiburg, Freiburg, Germany
- Translational Cardiology, Department of Cardiology and Department of Physiology, University Hospital Bern, Bern, Switzerland
| | - Stefanie Perez-Feliz
- Institute for Experimental Cardiovascular Medicine, University Heart Center Freiburg ⋅ Bad Krozingen, Medical Center—University of Freiburg, Freiburg, Germany
- Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Department of Cardiology and Angiology I, Heart Center University of Freiburg, Medical Faculty, Freiburg, Germany
| | - Tim Strohbach
- Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Department of Radiology, Medical Physics, University Medical Center Freiburg, Freiburg, Germany
| | - Marius Menza
- Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Department of Radiology, Medical Physics, University Medical Center Freiburg, Freiburg, Germany
| | - Axel Krafft
- Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Department of Radiology, Medical Physics, University Medical Center Freiburg, Freiburg, Germany
| | - Katja E. Odening
- Department of Cardiology and Angiology I, Heart Center University of Freiburg, Medical Faculty, Freiburg, Germany
- Translational Cardiology, Department of Cardiology and Department of Physiology, University Hospital Bern, Bern, Switzerland
| | - Gunnar Seemann
- Institute for Experimental Cardiovascular Medicine, University Heart Center Freiburg ⋅ Bad Krozingen, Medical Center—University of Freiburg, Freiburg, Germany
- Faculty of Medicine, University of Freiburg, Freiburg, Germany
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Sánchez J, Loewe A. A Review of Healthy and Fibrotic Myocardium Microstructure Modeling and Corresponding Intracardiac Electrograms. Front Physiol 2022; 13:908069. [PMID: 35620600 PMCID: PMC9127661 DOI: 10.3389/fphys.2022.908069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 04/21/2022] [Indexed: 11/13/2022] Open
Abstract
Computational simulations of cardiac electrophysiology provide detailed information on the depolarization phenomena at different spatial and temporal scales. With the development of new hardware and software, in silico experiments have gained more importance in cardiac electrophysiology research. For plane waves in healthy tissue, in vivo and in silico electrograms at the surface of the tissue demonstrate symmetric morphology and high peak-to-peak amplitude. Simulations provided insight into the factors that alter the morphology and amplitude of the electrograms. The situation is more complex in remodeled tissue with fibrotic infiltrations. Clinically, different changes including fractionation of the signal, extended duration and reduced amplitude have been described. In silico, numerous approaches have been proposed to represent the pathological changes on different spatial and functional scales. Different modeling approaches can reproduce distinct subsets of the clinically observed electrogram phenomena. This review provides an overview of how different modeling approaches to incorporate fibrotic and structural remodeling affect the electrogram and highlights open challenges to be addressed in future research.
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Hussan JR, Trew ML, Hunter PJ. Simplifying the Process of Going From Cells to Tissues Using Statistical Mechanics. Front Physiol 2022; 13:837027. [PMID: 35399281 PMCID: PMC8990301 DOI: 10.3389/fphys.2022.837027] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 01/31/2022] [Indexed: 11/21/2022] Open
Abstract
The value of digital twins for prototyping controllers or interventions in a sandbox environment are well-established in engineering and physics. However, this is challenging for biophysics trying to seamlessly compose models of multiple spatial and temporal scale behavior into the digital twin. Two challenges stand out as constraining progress: (i) ensuring physical consistency of conservation laws across composite models and (ii) drawing useful and timely clinical and scientific information from conceptually and computationally complex models. Challenge (i) can be robustly addressed with bondgraphs. However, challenge (ii) is exacerbated using this approach. The complexity question can be looked at from multiple angles. First from the perspective of discretizations that reflect underlying biophysics (functional tissue units) and secondly by exploring maximum entropy as the principle guiding multicellular biophysics. Statistical mechanics, long applied to understanding emergent phenomena from atomic physics, coupled with the observation that cellular architecture in tissue is orchestrated by biophysical constraints on metabolism and communication, shows conceptual promise. This architecture along with cell specific properties can be used to define tissue specific network motifs associated with energetic contributions. Complexity can be addressed based on energy considerations and finding mean measures of dependent variables. A probability distribution of the tissue's network motif can be approximated with exponential random graph models. A prototype problem shows how these approaches could be implemented in practice and the type of information that could be extracted.
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Affiliation(s)
- Jagir R Hussan
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Mark L Trew
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Peter J Hunter
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
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Jung A, Gsell MAF, Augustin CM, Plank G. An Integrated Workflow for Building Digital Twins of Cardiac Electromechanics—A Multi-Fidelity Approach for Personalising Active Mechanics. MATHEMATICS 2022; 10:823. [PMID: 35295404 PMCID: PMC7612499 DOI: 10.3390/math10050823] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Personalised computer models of cardiac function, referred to as cardiac digital twins, are envisioned to play an important role in clinical precision therapies of cardiovascular diseases. A major obstacle hampering clinical translation involves the significant computational costs involved in the personalisation of biophysically detailed mechanistic models that require the identification of high-dimensional parameter vectors. An important aspect to identify in electromechanics (EM) models are active mechanics parameters that govern cardiac contraction and relaxation. In this study, we present a novel, fully automated, and efficient approach for personalising biophysically detailed active mechanics models using a two-step multi-fidelity solution. In the first step, active mechanical behaviour in a given 3D EM model is represented by a purely phenomenological, low-fidelity model, which is personalised at the organ scale by calibration to clinical cavity pressure data. Then, in the second step, median traces of nodal cellular active stress, intracellular calcium concentration, and fibre stretch are generated and utilised to personalise the desired high-fidelity model at the cellular scale using a 0D model of cardiac EM. Our novel approach was tested on a cohort of seven human left ventricular (LV) EM models, created from patients treated for aortic coarctation (CoA). Goodness of fit, computational cost, and robustness of the algorithm against uncertainty in the clinical data and variations of initial guesses were evaluated. We demonstrate that our multi-fidelity approach facilitates the personalisation of a biophysically detailed active stress model within only a few (2 to 4) expensive 3D organ-scale simulations—a computational effort compatible with clinical model applications.
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Affiliation(s)
- Alexander Jung
- Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging—Division of Biophysics, Medical University Graz, 8010 Graz, Austria
| | - Matthias A. F. Gsell
- Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging—Division of Biophysics, Medical University Graz, 8010 Graz, Austria
- NAWI Graz, Institute of Mathematics and Scientific Computing, University of Graz, 8010 Graz, Austria
| | - Christoph M. Augustin
- Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging—Division of Biophysics, Medical University Graz, 8010 Graz, Austria
- BioTechMed-Graz, 8010 Graz, Austria
- Correspondence:
| | - Gernot Plank
- Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging—Division of Biophysics, Medical University Graz, 8010 Graz, Austria
- BioTechMed-Graz, 8010 Graz, Austria
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Mendonca Costa C, Gemmell P, Elliott MK, Whitaker J, Campos FO, Strocchi M, Neic A, Gillette K, Vigmond E, Plank G, Razavi R, O'Neill M, Rinaldi CA, Bishop MJ. Determining anatomical and electrophysiological detail requirements for computational ventricular models of porcine myocardial infarction. Comput Biol Med 2022; 141:105061. [PMID: 34915331 PMCID: PMC8819160 DOI: 10.1016/j.compbiomed.2021.105061] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 11/04/2021] [Accepted: 11/20/2021] [Indexed: 12/01/2022]
Abstract
BACKGROUND Computational models of the heart built from cardiac MRI and electrophysiology (EP) data have shown promise for predicting the risk of and ablation targets for myocardial infarction (MI) related ventricular tachycardia (VT), as well as to predict paced activation sequences in heart failure patients. However, most recent studies have relied on low resolution imaging data and little or no EP personalisation, which may affect the accuracy of model-based predictions. OBJECTIVE To investigate the impact of model anatomy, MI scar morphology, and EP personalisation strategies on paced activation sequences and VT inducibility to determine the level of detail required to make accurate model-based predictions. METHODS Imaging and EP data were acquired from a cohort of six pigs with experimentally induced MI. Computational models of ventricular anatomy, incorporating MI scar, were constructed including bi-ventricular or left ventricular (LV) only anatomy, and MI scar morphology with varying detail. Tissue conductivities and action potential duration (APD) were fitted to 12-lead ECG data using the QRS duration and the QT interval, respectively, in addition to corresponding literature parameters. Paced activation sequences and VT induction were simulated. Simulated paced activation and VT inducibility were compared between models and against experimental data. RESULTS Simulations predict that the level of model anatomical detail has little effect on simulated paced activation, with all model predictions comparing closely with invasive EP measurements. However, detailed scar morphology from high-resolution images, bi-ventricular anatomy, and personalized tissue conductivities are required to predict experimental VT outcome. CONCLUSION This study provides clear guidance for model generation based on clinical data. While a representing high level of anatomical and scar detail will require high-resolution image acquisition, EP personalisation based on 12-lead ECG can be readily incorporated into modelling pipelines, as such data is widely available.
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Affiliation(s)
- Caroline Mendonca Costa
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, UK.
| | - Philip Gemmell
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, UK
| | - Mark K Elliott
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, UK
| | - John Whitaker
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, UK
| | - Fernando O Campos
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, UK
| | - Marina Strocchi
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, UK
| | | | - Karli Gillette
- Gottfried Schatz Research Center, Biophysics, Medical University of Graz, Austria; Medical University of Graz, Austria and BioTechMed, Graz, Austria
| | - Edward Vigmond
- Institut de Rythmologie et de modélisation cardiaque (LIRYC), University of Bordeaux, France
| | - Gernot Plank
- Medical University of Graz, Austria and BioTechMed, Graz, Austria
| | - Reza Razavi
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, UK
| | - Mark O'Neill
- Department of Cardiology, Guy's and St Thomas' Hospital, London, UK
| | - Christopher A Rinaldi
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, UK; Department of Cardiology, Guy's and St Thomas' Hospital, London, UK
| | - Martin J Bishop
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, UK
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Modeling the His-Purkinje Effect in Non-invasive Estimation of Endocardial and Epicardial Ventricular Activation. Ann Biomed Eng 2022; 50:343-359. [PMID: 35072885 PMCID: PMC8847268 DOI: 10.1007/s10439-022-02905-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Accepted: 01/01/2022] [Indexed: 01/10/2023]
Abstract
Inverse electrocardiography (iECG) estimates epi- and endocardial electrical activity from body surface potentials maps (BSPM). In individuals at risk for cardiomyopathy, non-invasive estimation of normal ventricular activation may provide valuable information to aid risk stratification to prevent sudden cardiac death. However, multiple simultaneous activation wavefronts initiated by the His-Purkinje system, severely complicate iECG. To improve the estimation of normal ventricular activation, the iECG method should accurately mimic the effect of the His-Purkinje system, which is not taken into account in the previously published multi-focal iECG. Therefore, we introduce the novel multi-wave iECG method and report on its performance. Multi-wave iECG and multi-focal iECG were tested in four patients undergoing invasive electro-anatomical mapping during normal ventricular activation. In each subject, 67-electrode BSPM were recorded and used as input for both iECG methods. The iECG and invasive local activation timing (LAT) maps were compared. Median epicardial inter-map correlation coefficient (CC) between invasive LAT maps and estimated multi-wave iECG versus multi-focal iECG was 0.61 versus 0.31. Endocardial inter-map CC was 0.54 respectively 0.22. Modeling the His-Purkinje system resulted in a physiologically realistic and robust non-invasive estimation of normal ventricular activation, which might enable the early detection of cardiac disease during normal sinus rhythm.
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38
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Moss R, Wülfers EM, Schuler S, Loewe A, Seemann G. A Fully-Coupled Electro-Mechanical Whole-Heart Computational Model: Influence of Cardiac Contraction on the ECG. Front Physiol 2022; 12:778872. [PMID: 34975532 PMCID: PMC8716847 DOI: 10.3389/fphys.2021.778872] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Accepted: 11/17/2021] [Indexed: 01/12/2023] Open
Abstract
The ECG is one of the most commonly used non-invasive tools to gain insights into the electrical functioning of the heart. It has been crucial as a foundation in the creation and validation of in silico models describing the underlying electrophysiological processes. However, so far, the contraction of the heart and its influences on the ECG have mainly been overlooked in in silico models. As the heart contracts and moves, so do the electrical sources within the heart responsible for the signal on the body surface, thus potentially altering the ECG. To illuminate these aspects, we developed a human 4-chamber electro-mechanically coupled whole heart in silico model and embedded it within a torso model. Our model faithfully reproduces measured 12-lead ECG traces, circulatory characteristics, as well as physiological ventricular rotation and atrioventricular valve plane displacement. We compare our dynamic model to three non-deforming ones in terms of standard clinically used ECG leads (Einthoven and Wilson) and body surface potential maps (BSPM). The non-deforming models consider the heart at its ventricular end-diastatic, end-diastolic and end-systolic states. The standard leads show negligible differences during P-Wave and QRS-Complex, yet during T-Wave the leads closest to the heart show prominent differences in amplitude. When looking at the BSPM, there are no notable differences during the P-Wave, but effects of cardiac motion can be observed already during the QRS-Complex, increasing further during the T-Wave. We conclude that for the modeling of activation (P-Wave/QRS-Complex), the associated effort of simulating a complete electro-mechanical approach is not worth the computational cost. But when looking at ventricular repolarization (T-Wave) in standard leads as well as BSPM, there are areas where the signal can be influenced by cardiac motion of the heart to an extent that should not be ignored.
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Affiliation(s)
- Robin Moss
- Institute for Experimental Cardiovascular Medicine, University Heart Center Freiburg - Bad Krozingen, Medical Center-University of Freiburg, Freiburg, Germany.,Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Eike Moritz Wülfers
- Institute for Experimental Cardiovascular Medicine, University Heart Center Freiburg - Bad Krozingen, Medical Center-University of Freiburg, Freiburg, Germany.,Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Steffen Schuler
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | - Axel Loewe
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | - Gunnar Seemann
- Institute for Experimental Cardiovascular Medicine, University Heart Center Freiburg - Bad Krozingen, Medical Center-University of Freiburg, Freiburg, Germany.,Faculty of Medicine, University of Freiburg, Freiburg, Germany
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39
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Gillette K, Gsell MAF, Bouyssier J, Prassl AJ, Neic A, Vigmond EJ, Plank G. Automated Framework for the Inclusion of a His-Purkinje System in Cardiac Digital Twins of Ventricular Electrophysiology. Ann Biomed Eng 2021; 49:3143-3153. [PMID: 34431016 PMCID: PMC8671274 DOI: 10.1007/s10439-021-02825-9] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Accepted: 06/26/2021] [Indexed: 11/28/2022]
Abstract
Personalized models of cardiac electrophysiology (EP) that match clinical observation with high fidelity, referred to as cardiac digital twins (CDTs), show promise as a tool for tailoring cardiac precision therapies. Building CDTs of cardiac EP relies on the ability of models to replicate the ventricular activation sequence under a broad range of conditions. Of pivotal importance is the His-Purkinje system (HPS) within the ventricles. Workflows for the generation and incorporation of HPS models are needed for use in cardiac digital twinning pipelines that aim to minimize the misfit between model predictions and clinical data such as the 12 lead electrocardiogram (ECG). We thus develop an automated two stage approach for HPS personalization. A fascicular-based model is first introduced that modulates the endocardial Purkinje network. Only emergent features of sites of earliest activation within the ventricular myocardium and a fast-conducting sub-endocardial layer are accounted for. It is then replaced by a topologically realistic Purkinje-based representation of the HPS. Feasibility of the approach is demonstrated. Equivalence between both HPS model representations is investigated by comparing activation patterns and 12 lead ECGs under both sinus rhythm and right-ventricular apical pacing. Predominant ECG morphology is preserved by both HPS models under sinus conditions, but elucidates differences during pacing.
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Affiliation(s)
- Karli Gillette
- Gottfried Schatz Research Center Biophysics, Medical University of Graz, Graz, Austria
- BioTechMed-Graz, Graz, Austria
| | - Matthias A F Gsell
- Gottfried Schatz Research Center Biophysics, Medical University of Graz, Graz, Austria
| | - Julien Bouyssier
- IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, Pessac-Bordeaux, France
| | - Anton J Prassl
- Gottfried Schatz Research Center Biophysics, Medical University of Graz, Graz, Austria
| | | | - Edward J Vigmond
- IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, Pessac-Bordeaux, France
| | - Gernot Plank
- Gottfried Schatz Research Center Biophysics, Medical University of Graz, Graz, Austria.
- BioTechMed-Graz, Graz, Austria.
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40
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Augustin CM, Gsell MA, Karabelas E, Willemen E, Prinzen FW, Lumens J, Vigmond EJ, Plank G. A computationally efficient physiologically comprehensive 3D-0D closed-loop model of the heart and circulation. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2021; 386:114092. [PMID: 34630765 PMCID: PMC7611781 DOI: 10.1016/j.cma.2021.114092] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
Computer models of cardiac electro-mechanics (EM) show promise as an effective means for the quantitative analysis of clinical data and, potentially, for predicting therapeutic responses. To realize such advanced applications methodological key challenges must be addressed. Enhanced computational efficiency and robustness is crucial to facilitate, within tractable time frames, model personalization, the simulation of prolonged observation periods under a broad range of conditions, and physiological completeness encompassing therapy-relevant mechanisms is needed to endow models with predictive capabilities beyond the mere replication of observations. Here, we introduce a universal feature-complete cardiac EM modeling framework that builds on a flexible method for coupling a 3D model of bi-ventricular EM to the physiologically comprehensive 0D CircAdapt model representing atrial mechanics and closed-loop circulation. A detailed mathematical description is given and efficiency, robustness, and accuracy of numerical scheme and solver implementation are evaluated. After parameterization and stabilization of the coupled 3D-0D model to a limit cycle under baseline conditions, the model's ability to replicate physiological behaviors is demonstrated, by simulating the transient response to alterations in loading conditions and contractility, as induced by experimental protocols used for assessing systolic and diastolic ventricular properties. Mechanistic completeness and computational efficiency of this novel model render advanced applications geared towards predicting acute outcomes of EM therapies feasible.
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Affiliation(s)
- Christoph M. Augustin
- Gottfried Schatz Research Center: Division of Biophysics, Medical University of Graz, Graz, Austria
| | - Matthias A.F. Gsell
- Gottfried Schatz Research Center: Division of Biophysics, Medical University of Graz, Graz, Austria
| | - Elias Karabelas
- Gottfried Schatz Research Center: Division of Biophysics, Medical University of Graz, Graz, Austria
| | - Erik Willemen
- Department of Biomedical Engineering, CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, Netherlands
| | - Frits W. Prinzen
- Department of Biomedical Engineering, CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, Netherlands
| | - Joost Lumens
- Department of Biomedical Engineering, CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, Netherlands
| | - Edward J. Vigmond
- IHU Liryc, Electrophysiology and Heart Modeling Institute, fondation Bordeaux Université, Pessac-Bordeaux, France
| | - Gernot Plank
- Gottfried Schatz Research Center: Division of Biophysics, Medical University of Graz, Graz, Austria
- BioTechMed-Graz, Graz, Austria
- Correspondence to: Gottfried Schatz Research Center: Division of Biophysics, Medical University of Graz, Neue Stiftingtalstrasse 6/IV, Graz 8010, Austria. (G. Plank)
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Meister F, Passerini T, Audigier C, Lluch È, Mihalef V, Ashikaga H, Maier A, Halperin H, Mansi T. Extrapolation of Ventricular Activation Times From Sparse Electroanatomical Data Using Graph Convolutional Neural Networks. Front Physiol 2021; 12:694869. [PMID: 34733172 PMCID: PMC8558498 DOI: 10.3389/fphys.2021.694869] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Accepted: 09/15/2021] [Indexed: 11/25/2022] Open
Abstract
Electroanatomic mapping is the gold standard for the assessment of ventricular tachycardia. Acquiring high resolution electroanatomic maps is technically challenging and may require interpolation methods to obtain dense measurements. These methods, however, cannot recover activation times in the entire biventricular domain. This work investigates the use of graph convolutional neural networks to estimate biventricular activation times from sparse measurements. Our method is trained on more than 15,000 synthetic examples of realistic ventricular depolarization patterns generated by a computational electrophysiology model. Using geometries sampled from a statistical shape model of biventricular anatomy, diverse wave dynamics are induced by randomly sampling scar and border zone distributions, locations of initial activation, and tissue conduction velocities. Once trained, the method accurately reconstructs biventricular activation times in left-out synthetic simulations with a mean absolute error of 3.9 ms ± 4.2 ms at a sampling density of one measurement sample per cm2. The total activation time is matched with a mean error of 1.4 ms ± 1.4 ms. A significant decrease in errors is observed in all heart zones with an increased number of samples. Without re-training, the network is further evaluated on two datasets: (1) an in-house dataset comprising four ischemic porcine hearts with dense endocardial activation maps; (2) the CRT-EPIGGY19 challenge data comprising endo- and epicardial measurements of 5 infarcted and 6 non-infarcted swines. In both setups the neural network recovers biventricular activation times with a mean absolute error of less than 10 ms even when providing only a subset of endocardial measurements as input. Furthermore, we present a simple approach to suggest new measurement locations in real-time based on the estimated uncertainty of the graph network predictions. The model-guided selection of measurement locations allows to reduce by 40% the number of measurements required in a random sampling strategy, while achieving the same prediction error. In all the tested scenarios, the proposed approach estimates biventricular activation times with comparable or better performance than a personalized computational model and significant runtime advantages.
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Affiliation(s)
- Felix Meister
- Pattern Recognition Lab, Friedrich-Alexander University, Erlangen, Germany
- Digital Technology and Innovation, Siemens Healthineers, Erlangen, Germany
| | - Tiziano Passerini
- Digital Technology and Innovation, Siemens Healthineers, Princeton, NJ, United States
| | - Chloé Audigier
- Digital Technology and Innovation, Siemens Healthineers, Erlangen, Germany
| | - Èric Lluch
- Digital Technology and Innovation, Siemens Healthineers, Erlangen, Germany
| | - Viorel Mihalef
- Digital Technology and Innovation, Siemens Healthineers, Princeton, NJ, United States
| | - Hiroshi Ashikaga
- Cardiac Arrhythmia Service, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Andreas Maier
- Pattern Recognition Lab, Friedrich-Alexander University, Erlangen, Germany
| | - Henry Halperin
- Cardiac Arrhythmia Service, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Tommaso Mansi
- Digital Technology and Innovation, Siemens Healthineers, Princeton, NJ, United States
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Rupp LC, Bergquist JA, Zenger B, Gillette K, Narayan A, Tate JD, Plank G, MacLeod RS. The Role of Myocardial Fiber Direction in Epicardial Activation Patterns via Uncertainty Quantification. COMPUTING IN CARDIOLOGY 2021; 48:10.23919/cinc53138.2021.9662950. [PMID: 35449765 PMCID: PMC9020927 DOI: 10.23919/cinc53138.2021.9662950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Fiber structure governs the spread of excitation in the heart; however, little is known about the effects of physiological variability in fiber orientation on epicardial activation. To investigate these effects, we implemented ventricular simulations of activation using rule-based fiber orientations, and robust uncertainty quantification algorithms to capture detailed maps of model sensitivity. Specifically, we implemented polynomial chaos expansion, which allows for robust exploration with reduced computational demand through an emulator function to approximate the underlying forward model. We applied these techniques to examine the activation sequence of the heart in response to both epicardial and endocardial stimuli within the left ventricular free wall and variations in fiber orientation. Our results showed that physiological variation in fiber orientation does not significantly impact the location of activation features, but it does impact the overall spread of activation. Future studies will investigate under which circumstances physiological changes in fiber orientation might alter electrical propagation such that the resulting simulations produce misleading outcomes.
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Affiliation(s)
- Lindsay C Rupp
- Scientific Computing and Imaging Institute, University of Utah, SLC, UT, USA
- Nora Eccles Cardiovascular Research and Training Institute, University of Utah, SLC, UT, USA
- Department of Biomedical Engineering, University of Utah, SLC, UT, USA
| | - Jake A Bergquist
- Scientific Computing and Imaging Institute, University of Utah, SLC, UT, USA
- Nora Eccles Cardiovascular Research and Training Institute, University of Utah, SLC, UT, USA
- Department of Biomedical Engineering, University of Utah, SLC, UT, USA
| | - Brian Zenger
- Scientific Computing and Imaging Institute, University of Utah, SLC, UT, USA
- Nora Eccles Cardiovascular Research and Training Institute, University of Utah, SLC, UT, USA
- Department of Biomedical Engineering, University of Utah, SLC, UT, USA
- School of Medicine, University of Utah, SLC, UT, USA
| | - Karli Gillette
- Institute of Biophysics, Medical University of Graz, Graz, Austria
| | - Akil Narayan
- Scientific Computing and Imaging Institute, University of Utah, SLC, UT, USA
| | - Jess D Tate
- Scientific Computing and Imaging Institute, University of Utah, SLC, UT, USA
| | - Gernot Plank
- Institute of Biophysics, Medical University of Graz, Graz, Austria
| | - Rob S MacLeod
- Scientific Computing and Imaging Institute, University of Utah, SLC, UT, USA
- Nora Eccles Cardiovascular Research and Training Institute, University of Utah, SLC, UT, USA
- Department of Biomedical Engineering, University of Utah, SLC, UT, USA
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43
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Sung E, Etoz S, Zhang Y, Trayanova NA. Whole-heart ventricular arrhythmia modeling moving forward: Mechanistic insights and translational applications. BIOPHYSICS REVIEWS 2021; 2:031304. [PMID: 36281224 PMCID: PMC9588428 DOI: 10.1063/5.0058050] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/19/2023]
Abstract
Ventricular arrhythmias are the primary cause of sudden cardiac death and one of the leading causes of mortality worldwide. Whole-heart computational modeling offers a unique approach for studying ventricular arrhythmias, offering vast potential for developing both a mechanistic understanding of ventricular arrhythmias and clinical applications for treatment. In this review, the fundamentals of whole-heart ventricular modeling and current methods of personalizing models using clinical data are presented. From this foundation, the authors summarize recent advances in whole-heart ventricular arrhythmia modeling. Efforts in gaining mechanistic insights into ventricular arrhythmias are discussed, in addition to other applications of models such as the assessment of novel therapeutics. The review emphasizes the unique benefits of computational modeling that allow for insights that are not obtainable by contemporary experimental or clinical means. Additionally, the clinical impact of modeling is explored, demonstrating how patient care is influenced by the information gained from ventricular arrhythmia models. The authors conclude with future perspectives about the direction of whole-heart ventricular arrhythmia modeling, outlining how advances in neural network methodologies hold the potential to reduce computational expense and permit for efficient whole-heart modeling.
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Affiliation(s)
- Eric Sung
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Sevde Etoz
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Yingnan Zhang
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Natalia A. Trayanova
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, Maryland 21218, USA
- Author to whom correspondence should be addressed: . Tel.: 410-516-4375
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Plank G, Loewe A, Neic A, Augustin C, Huang YL, Gsell MAF, Karabelas E, Nothstein M, Prassl AJ, Sánchez J, Seemann G, Vigmond EJ. The openCARP simulation environment for cardiac electrophysiology. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 208:106223. [PMID: 34171774 DOI: 10.1016/j.cmpb.2021.106223] [Citation(s) in RCA: 61] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Accepted: 05/28/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVE Cardiac electrophysiology is a medical specialty with a long and rich tradition of computational modeling. Nevertheless, no community standard for cardiac electrophysiology simulation software has evolved yet. Here, we present the openCARP simulation environment as one solution that could foster the needs of large parts of this community. METHODS AND RESULTS openCARP and the Python-based carputils framework allow developing and sharing simulation pipelines which automate in silico experiments including all modeling and simulation steps to increase reproducibility and productivity. The continuously expanding openCARP user community is supported by tailored infrastructure. Documentation and training material facilitate access to this complementary research tool for new users. After a brief historic review, this paper summarizes requirements for a high-usability electrophysiology simulator and describes how openCARP fulfills them. We introduce the openCARP modeling workflow in a multi-scale example of atrial fibrillation simulations on single cell, tissue, organ and body level and finally outline future development potential. CONCLUSION As an open simulator, openCARP can advance the computational cardiac electrophysiology field by making state-of-the-art simulations accessible. In combination with the carputils framework, it offers a tailored software solution for the scientific community and contributes towards increasing use, transparency, standardization and reproducibility of in silico experiments.
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Affiliation(s)
- Gernot Plank
- Gottfried Schatz Research Center, Division of Biophysics, Medical University of Graz, Graz, Austria.
| | - Axel Loewe
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | | | - Christoph Augustin
- Gottfried Schatz Research Center, Division of Biophysics, Medical University of Graz, Graz, Austria
| | - Yung-Lin Huang
- Institute for Experimental Cardiovascular Medicine, University Heart Center Freiburg. Bad Krozingen, Medical Center - University of Freiburg, Freiburg, Germany; Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Matthias A F Gsell
- Gottfried Schatz Research Center, Division of Biophysics, Medical University of Graz, Graz, Austria
| | - Elias Karabelas
- Institute of Mathematics and Scientific Computing, University of Graz, Graz, Austria
| | - Mark Nothstein
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | - Anton J Prassl
- Gottfried Schatz Research Center, Division of Biophysics, Medical University of Graz, Graz, Austria
| | - Jorge Sánchez
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | - Gunnar Seemann
- Institute for Experimental Cardiovascular Medicine, University Heart Center Freiburg. Bad Krozingen, Medical Center - University of Freiburg, Freiburg, Germany; Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Edward J Vigmond
- IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, F-33600 Pessac-Bordeaux, France; Université Bordeaux, IMB, UMR 5251, F-33400 Talence, France
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Romero P, Lozano M, Martínez-Gil F, Serra D, Sebastián R, Lamata P, García-Fernández I. Clinically-Driven Virtual Patient Cohorts Generation: An Application to Aorta. Front Physiol 2021; 12:713118. [PMID: 34539438 PMCID: PMC8440937 DOI: 10.3389/fphys.2021.713118] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Accepted: 08/03/2021] [Indexed: 12/20/2022] Open
Abstract
The combination of machine learning methods together with computational modeling and simulation of the cardiovascular system brings the possibility of obtaining very valuable information about new therapies or clinical devices through in-silico experiments. However, the application of machine learning methods demands access to large cohorts of patients. As an alternative to medical data acquisition and processing, which often requires some degree of manual intervention, the generation of virtual cohorts made of synthetic patients can be automated. However, the generation of a synthetic sample can still be computationally demanding to guarantee that it is clinically meaningful and that it reflects enough inter-patient variability. This paper addresses the problem of generating virtual patient cohorts of thoracic aorta geometries that can be used for in-silico trials. In particular, we focus on the problem of generating a cohort of patients that meet a particular clinical criterion, regardless the access to a reference sample of that phenotype. We formalize the problem of clinically-driven sampling and assess several sampling strategies with two goals, sampling efficiency, i.e., that the generated individuals actually belong to the target population, and that the statistical properties of the cohort can be controlled. Our results show that generative adversarial networks can produce reliable, clinically-driven cohorts of thoracic aortas with good efficiency. Moreover, non-linear predictors can serve as an efficient alternative to the sometimes expensive evaluation of anatomical or functional parameters of the organ of interest.
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Affiliation(s)
- Pau Romero
- Computational Multiscale Simulation Lab, Department of Computer Science, Universitat de Valencia, Valencia, Spain
| | - Miguel Lozano
- Computational Multiscale Simulation Lab, Department of Computer Science, Universitat de Valencia, Valencia, Spain
| | - Francisco Martínez-Gil
- Computational Multiscale Simulation Lab, Department of Computer Science, Universitat de Valencia, Valencia, Spain
| | - Dolors Serra
- Computational Multiscale Simulation Lab, Department of Computer Science, Universitat de Valencia, Valencia, Spain
| | - Rafael Sebastián
- Computational Multiscale Simulation Lab, Department of Computer Science, Universitat de Valencia, Valencia, Spain
| | - Pablo Lamata
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, Kings College London, London, United Kingdom
| | - Ignacio García-Fernández
- Computational Multiscale Simulation Lab, Department of Computer Science, Universitat de Valencia, Valencia, Spain
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Digital Twins: From Personalised Medicine to Precision Public Health. J Pers Med 2021; 11:jpm11080745. [PMID: 34442389 PMCID: PMC8401029 DOI: 10.3390/jpm11080745] [Citation(s) in RCA: 77] [Impact Index Per Article: 25.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 07/26/2021] [Accepted: 07/28/2021] [Indexed: 01/19/2023] Open
Abstract
A digital twin is a virtual model of a physical entity, with dynamic, bi-directional links between the physical entity and its corresponding twin in the digital domain. Digital twins are increasingly used today in different industry sectors. Applied to medicine and public health, digital twin technology can drive a much-needed radical transformation of traditional electronic health/medical records (focusing on individuals) and their aggregates (covering populations) to make them ready for a new era of precision (and accuracy) medicine and public health. Digital twins enable learning and discovering new knowledge, new hypothesis generation and testing, and in silico experiments and comparisons. They are poised to play a key role in formulating highly personalised treatments and interventions in the future. This paper provides an overview of the technology's history and main concepts. A number of application examples of digital twins for personalised medicine, public health, and smart healthy cities are presented, followed by a brief discussion of the key technical and other challenges involved in such applications, including ethical issues that arise when digital twins are applied to model humans.
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Abstract
Computer modeling of the electrophysiology of the heart has undergone significant progress. A healthy heart can be modeled starting from the ion channels via the spread of a depolarization wave on a realistic geometry of the human heart up to the potentials on the body surface and the ECG. Research is advancing regarding modeling diseases of the heart. This article reviews progress in calculating and analyzing the corresponding electrocardiogram (ECG) from simulated depolarization and repolarization waves. First, we describe modeling of the P-wave, the QRS complex and the T-wave of a healthy heart. Then, both the modeling and the corresponding ECGs of several important diseases and arrhythmias are delineated: ischemia and infarction, ectopic beats and extrasystoles, ventricular tachycardia, bundle branch blocks, atrial tachycardia, flutter and fibrillation, genetic diseases and channelopathies, imbalance of electrolytes and drug-induced changes. Finally, we outline the potential impact of computer modeling on ECG interpretation. Computer modeling can contribute to a better comprehension of the relation between features in the ECG and the underlying cardiac condition and disease. It can pave the way for a quantitative analysis of the ECG and can support the cardiologist in identifying events or non-invasively localizing diseased areas. Finally, it can deliver very large databases of reliably labeled ECGs as training data for machine learning.
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48
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Monaci S, Gillette K, Puyol-Antón E, Rajani R, Plank G, King A, Bishop M. Automated Localization of Focal Ventricular Tachycardia From Simulated Implanted Device Electrograms: A Combined Physics-AI Approach. Front Physiol 2021; 12:682446. [PMID: 34276403 PMCID: PMC8281305 DOI: 10.3389/fphys.2021.682446] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Accepted: 05/31/2021] [Indexed: 11/13/2022] Open
Abstract
Background: Focal ventricular tachycardia (VT) is a life-threating arrhythmia, responsible for high morbidity rates and sudden cardiac death (SCD). Radiofrequency ablation is the only curative therapy against incessant VT; however, its success is dependent on accurate localization of its source, which is highly invasive and time-consuming. Objective: The goal of our study is, as a proof of concept, to demonstrate the possibility of utilizing electrogram (EGM) recordings from cardiac implantable electronic devices (CIEDs). To achieve this, we utilize fast and accurate whole torso electrophysiological (EP) simulations in conjunction with convolutional neural networks (CNNs) to automate the localization of focal VTs using simulated EGMs. Materials and Methods: A highly detailed 3D torso model was used to simulate ∼4000 focal VTs, evenly distributed across the left ventricle (LV), utilizing a rapid reaction-eikonal environment. Solutions were subsequently combined with lead field computations on the torso to derive accurate electrocardiograms (ECGs) and EGM traces, which were used as inputs to CNNs to localize focal sources. We compared the localization performance of a previously developed CNN architecture (Cartesian probability-based) with our novel CNN algorithm utilizing universal ventricular coordinates (UVCs). Results: Implanted device EGMs successfully localized VT sources with localization error (8.74 mm) comparable to ECG-based localization (6.69 mm). Our novel UVC CNN architecture outperformed the existing Cartesian probability-based algorithm (errors = 4.06 mm and 8.07 mm for ECGs and EGMs, respectively). Overall, localization was relatively insensitive to noise and changes in body compositions; however, displacements in ECG electrodes and CIED leads caused performance to decrease (errors 16-25 mm). Conclusion: EGM recordings from implanted devices may be used to successfully, and robustly, localize focal VT sources, and aid ablation planning.
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Affiliation(s)
| | - Karli Gillette
- Division of Biophysics, Medical University of Graz, Graz, Austria
| | | | | | - Gernot Plank
- Division of Biophysics, Medical University of Graz, Graz, Austria
| | - Andrew King
- King’s College London, London, United Kingdom
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Good WW, Zenger B, Bergquist JA, Rupp LC, Gillette K, Angel N, Chou D, Plank G, MacLeod RS. Combining endocardial mapping and electrocardiographic imaging (ECGI) for improving PVC localization: A feasibility study. J Electrocardiol 2021; 69S:51-54. [PMID: 34649726 PMCID: PMC9014370 DOI: 10.1016/j.jelectrocard.2021.08.013] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 08/10/2021] [Accepted: 08/13/2021] [Indexed: 12/29/2022]
Abstract
INTRODUCTION Accurate reconstruction of cardiac activation wavefronts is crucial for clinical diagnosis, management, and treatment of cardiac arrhythmias. Furthermore, reconstruction of activation profiles within the intramural myocardium has long been impossible because electrical mapping was only performed on the endocardial surface. Recent advancements in electrocardiographic imaging (ECGI) have made endocardial and epicardial activation mapping possible. We propose a novel approach to use both endocardial and epicardial mapping in a combined approach to reconstruct intramural activation times. OBJECTIVE To implement and validate a combined epicardial/endocardial intramural activation time reconstruction technique. METHODS We used 11 simulations of ventricular activation paced from sites throughout myocardial wall and extracted endocardial and epicardial activation maps at approximate clinical resolution. From these maps, we interpolated the activation times through the myocardium using thin-plate-spline radial basis functions. We evaluated activation time reconstruction accuracy using root-mean-squared error (RMSE) of activation times and the percent of nodes within 1 ms of the ground truth. RESULTS Reconstructed intramural activation times showed an RMSE and percentage of nodes within 1 ms of the ground truth simulations of 3 ms and 70%, respectively. In the worst case, the RMSE and percentage of nodes were 4 ms and 60%, respectively. CONCLUSION We showed that a simple, yet effective combination of clinical endocardial and epicardial activation maps can accurately reconstruct intramural wavefronts. Furthermore, we showed that this approach provided robust reconstructions across multiple intramural stimulation sites.
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Affiliation(s)
- Wilson W. Good
- Scientific Computing and Imaging Institute, University of Utah, SLC, UT, USA,Department of Biomedical Engineering, University of Utah, SLC, UT, USA,Nora Eccles Cardiovascular Research and Training Institute, University of Utah, SLC, UT, USA,Acutus Medical, Carlsbad, CA, USA
| | - Brian Zenger
- Scientific Computing and Imaging Institute, University of Utah, SLC, UT, USA,Department of Biomedical Engineering, University of Utah, SLC, UT, USA,Nora Eccles Cardiovascular Research and Training Institute, University of Utah, SLC, UT, USA
| | - Jake A. Bergquist
- Scientific Computing and Imaging Institute, University of Utah, SLC, UT, USA,Department of Biomedical Engineering, University of Utah, SLC, UT, USA,Nora Eccles Cardiovascular Research and Training Institute, University of Utah, SLC, UT, USA
| | - Lindsay C. Rupp
- Scientific Computing and Imaging Institute, University of Utah, SLC, UT, USA,Department of Biomedical Engineering, University of Utah, SLC, UT, USA,Nora Eccles Cardiovascular Research and Training Institute, University of Utah, SLC, UT, USA
| | - Karli Gillette
- Institute of Biophysics, Medical University of Graz, Graz, Austria
| | | | | | - Gernot Plank
- Institute of Biophysics, Medical University of Graz, Graz, Austria
| | - Rob S. MacLeod
- Scientific Computing and Imaging Institute, University of Utah, SLC, UT, USA,Department of Biomedical Engineering, University of Utah, SLC, UT, USA,Nora Eccles Cardiovascular Research and Training Institute, University of Utah, SLC, UT, USA
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