1
|
Huang L, Ruan S, Xing Y, Feng M. A review of uncertainty quantification in medical image analysis: Probabilistic and non-probabilistic methods. Med Image Anal 2024; 97:103223. [PMID: 38861770 DOI: 10.1016/j.media.2024.103223] [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: 10/09/2023] [Revised: 03/16/2024] [Accepted: 05/27/2024] [Indexed: 06/13/2024]
Abstract
The comprehensive integration of machine learning healthcare models within clinical practice remains suboptimal, notwithstanding the proliferation of high-performing solutions reported in the literature. A predominant factor hindering widespread adoption pertains to an insufficiency of evidence affirming the reliability of the aforementioned models. Recently, uncertainty quantification methods have been proposed as a potential solution to quantify the reliability of machine learning models and thus increase the interpretability and acceptability of the results. In this review, we offer a comprehensive overview of the prevailing methods proposed to quantify the uncertainty inherent in machine learning models developed for various medical image tasks. Contrary to earlier reviews that exclusively focused on probabilistic methods, this review also explores non-probabilistic approaches, thereby furnishing a more holistic survey of research pertaining to uncertainty quantification for machine learning models. Analysis of medical images with the summary and discussion on medical applications and the corresponding uncertainty evaluation protocols are presented, which focus on the specific challenges of uncertainty in medical image analysis. We also highlight some potential future research work at the end. Generally, this review aims to allow researchers from both clinical and technical backgrounds to gain a quick and yet in-depth understanding of the research in uncertainty quantification for medical image analysis machine learning models.
Collapse
Affiliation(s)
- Ling Huang
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore
| | - Su Ruan
- Quantif, LITIS, University of Rouen Normandy, France.
| | - Yucheng Xing
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore
| | - Mengling Feng
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore; Institute of Data Science, National University of Singapore, Singapore
| |
Collapse
|
2
|
Jaffery OA, Melki L, Slabaugh G, Good WW, Roney CH. A Review of Personalised Cardiac Computational Modelling Using Electroanatomical Mapping Data. Arrhythm Electrophysiol Rev 2024; 13:e08. [PMID: 38807744 PMCID: PMC11131150 DOI: 10.15420/aer.2023.25] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Accepted: 12/27/2023] [Indexed: 05/30/2024] Open
Abstract
Computational models of cardiac electrophysiology have gradually matured during the past few decades and are now being personalised to provide patient-specific therapy guidance for improving suboptimal treatment outcomes. The predictive features of these personalised electrophysiology models hold the promise of providing optimal treatment planning, which is currently limited in the clinic owing to reliance on a population-based or average patient approach. The generation of a personalised electrophysiology model entails a sequence of steps for which a range of activation mapping, calibration methods and therapy simulation pipelines have been suggested. However, the optimal methods that can potentially constitute a clinically relevant in silico treatment are still being investigated and face limitations, such as uncertainty of electroanatomical data recordings, generation and calibration of models within clinical timelines and requirements to validate or benchmark the recovered tissue parameters. This paper is aimed at reporting techniques on the personalisation of cardiac computational models, with a focus on calibrating cardiac tissue conductivity based on electroanatomical mapping data.
Collapse
Affiliation(s)
- Ovais A Jaffery
- School of Engineering and Materials Science, Queen Mary University of London London, UK
| | - Lea Melki
- R&D Algorithms, Acutus Medical Carlsbad, CA, US
| | - Gregory Slabaugh
- Digital Environment Research Institute, Queen Mary University of London London, UK
| | | | - Caroline H Roney
- School of Engineering and Materials Science, Queen Mary University of London London, UK
| |
Collapse
|
3
|
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.
Collapse
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
| |
Collapse
|
4
|
Camps J, Lawson B, Drovandi C, Minchole A, Wang ZJ, Grau V, Burrage K, Rodriguez B. Inference of ventricular activation properties from non-invasive electrocardiography. Med Image Anal 2021; 73:102143. [PMID: 34271532 PMCID: PMC8505755 DOI: 10.1016/j.media.2021.102143] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Revised: 06/17/2021] [Accepted: 06/18/2021] [Indexed: 12/13/2022]
Abstract
The realisation of precision cardiology requires novel techniques for the non-invasive characterisation of individual patients’ cardiac function to inform therapeutic and diagnostic decision-making. Both electrocardiography and imaging are used for the clinical diagnosis of cardiac disease. The integration of multi-modal datasets through advanced computational methods could enable the development of the cardiac ‘digital twin’, a comprehensive virtual tool that mechanistically reveals a patient's heart condition from clinical data and simulates treatment outcomes. The adoption of cardiac digital twins requires the non-invasive efficient personalisation of the electrophysiological properties in cardiac models. This study develops new computational techniques to estimate key ventricular activation properties for individual subjects by exploiting the synergy between non-invasive electrocardiography, cardiac magnetic resonance (CMR) imaging and modelling and simulation. More precisely, we present an efficient sequential Monte Carlo approximate Bayesian computation-based inference method, integrated with Eikonal simulations and torso-biventricular models constructed based on clinical CMR imaging. The method also includes a novel strategy to treat combined continuous (conduction speeds) and discrete (earliest activation sites) parameter spaces and an efficient dynamic time warping-based ECG comparison algorithm. We demonstrate results from our inference method on a cohort of twenty virtual subjects with cardiac ventricular myocardial-mass volumes ranging from 74 cm3 to 171 cm3 and considering low versus high resolution for the endocardial discretisation (which determines possible locations of the earliest activation sites). Results show that our method can successfully infer the ventricular activation properties in sinus rhythm from non-invasive epicardial activation time maps and ECG recordings, achieving higher accuracy for the endocardial speed and sheet (transmural) speed than for the fibre or sheet-normal directed speeds. Estimation of the ventricular speeds and earliest activation sites from ECG and CMR. Evaluation with twenty virtual subjects shows the effect of anatomical variability. Bayesian-inspired simultaneous estimation of continuous and discrete parameters. Efficient dynamic time warping-based comparison of electrocardiograms (ECG). Changing fibre and sheet-normal speed does not affect healthy activation sequence.
Collapse
Affiliation(s)
- Julia Camps
- Department of Computer Science, University of Oxford, Oxford, United Kingdom.
| | - Brodie Lawson
- Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS), Queensland University of Technology (QUT), Brisbane, Australia; QUT Centre for Data Science (CDS), Queensland University of Technology, Brisbane, Australia
| | - Christopher Drovandi
- Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS), Queensland University of Technology (QUT), Brisbane, Australia; QUT Centre for Data Science (CDS), Queensland University of Technology, Brisbane, Australia
| | - Ana Minchole
- Department of Computer Science, University of Oxford, Oxford, United Kingdom
| | - Zhinuo Jenny Wang
- Department of Computer Science, University of Oxford, Oxford, United Kingdom
| | - Vicente Grau
- Institute of Biomedical Engineering (IBME), University of Oxford, Oxford, United Kingdom
| | - Kevin Burrage
- Department of Computer Science, University of Oxford, Oxford, United Kingdom; Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS), Queensland University of Technology (QUT), Brisbane, Australia
| | - Blanca Rodriguez
- Department of Computer Science, University of Oxford, Oxford, United Kingdom
| |
Collapse
|
5
|
Abstract
The treatment of individual patients in cardiology practice increasingly relies on advanced imaging, genetic screening and devices. As the amount of imaging and other diagnostic data increases, paralleled by the greater capacity to personalize treatment, the difficulty of using the full array of measurements of a patient to determine an optimal treatment seems also to be paradoxically increasing. Computational models are progressively addressing this issue by providing a common framework for integrating multiple data sets from individual patients. These models, which are based on physiology and physics rather than on population statistics, enable computational simulations to reveal diagnostic information that would have otherwise remained concealed and to predict treatment outcomes for individual patients. The inherent need for patient-specific models in cardiology is clear and is driving the rapid development of tools and techniques for creating personalized methods to guide pharmaceutical therapy, deployment of devices and surgical interventions.
Collapse
|
6
|
Shalaby N, Zemzemi N, Elkhodary K. Simulating the effect of sodium channel blockage on cardiac electromechanics. Proc Inst Mech Eng H 2019; 234:16-27. [PMID: 31625448 DOI: 10.1177/0954411919882514] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
There is growing interest to better understand drug-induced cardiovascular complications and to predict undesirable side effects at as early a stage in the drug development process as possible. The purpose of this paper is to investigate computationally the influence of sodium ion channel blockage on cardiac electromechanics. To do so, we implement a myofiber orientation dependent passive stress model (Holzapfel-Ogden) in the multiphysics solver Chaste to simulate an imaged physiological model of the human ventricles. A dosage of a sodium channel blocker was then applied and its inhibitory effects on the electrical propagation across ventricles were modeled. We employ the Kerckhoffs active stress model to generate electrically excited contractile behavior of myofibers. Our predictions indicate that a delay in the electrical activation of ventricular tissue caused by the sodium channel blockage translates to a delay in the mechanical biomarkers that were investigated. Moreover, sodium channel blockage was found to increase left ventricular twist. A multiphysics computational framework from the cell level to the organ level was thus used to predict the effect of sodium channel blocking drugs on cardiac electromechanics.
Collapse
Affiliation(s)
- Noha Shalaby
- Mechanical Engineering Department, The American University in Cairo, New Cairo, Egypt
| | - Nejib Zemzemi
- INRIA Bordeaux Sud-Ouest, Carmen Group, Talence, France.,IHU-LIRYC, Pessac, France
| | - Khalil Elkhodary
- Mechanical Engineering Department, The American University in Cairo, New Cairo, Egypt
| |
Collapse
|
7
|
Mincholé A, Zacur E, Ariga R, Grau V, Rodriguez B. MRI-Based Computational Torso/Biventricular Multiscale Models to Investigate the Impact of Anatomical Variability on the ECG QRS Complex. Front Physiol 2019; 10:1103. [PMID: 31507458 PMCID: PMC6718559 DOI: 10.3389/fphys.2019.01103] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2019] [Accepted: 08/08/2019] [Indexed: 01/07/2023] Open
Abstract
AIMS Patient-to-patient anatomical differences are an important source of variability in the electrocardiogram, and they may compromise the identification of pathological electrophysiological abnormalities. This study aims at quantifying the contribution of variability in ventricular and torso anatomies to differences in QRS complexes of the 12-lead ECG using computer simulations. METHODS A computational pipeline is presented that enables computer simulations using human torso/biventricular anatomically based electrophysiological models from clinically standard magnetic resonance imaging (MRI). The ventricular model includes membrane kinetics represented by the biophysically detailed O'Hara Rudy model modified for tissue heterogeneity and includes fiber orientation based on the Streeter rule. A population of 265 torso/biventricular models was generated by combining ventricular and torso anatomies obtained from clinically standard MRIs, augmented with a statistical shape model of the body. 12-lead ECGs were simulated on the 265 human torso/biventricular electrophysiology models, and QRS morphology, duration and amplitude were quantified in each ECG lead for each of the human torso-biventricular models. RESULTS QRS morphologies in limb leads are mainly determined by ventricular anatomy, while in the precordial leads, and especially V1 to V4, they are determined by heart position within the torso. Differences in ventricular orientation within the torso can explain morphological variability from monophasic to biphasic QRS complexes. QRS duration is mainly influenced by myocardial volume, while it is hardly affected by the torso anatomy or position. An average increase of 0.12 ± 0.05 ms in QRS duration is obtained for each cm3 of myocardial volume across all the leads while it hardly changed due to changes in torso volume. CONCLUSION Computer simulations using populations of human torso/biventricular models based on clinical MRI enable quantification of anatomical causes of variability in the QRS complex of the 12-lead ECG. The human models presented also pave the way toward their use as testbeds in silico clinical trials.
Collapse
Affiliation(s)
- Ana Mincholé
- Department of Computer Science, University of Oxford, Oxford, United Kingdom
| | - Ernesto Zacur
- Institute of Biomedical Engineering (IBME), University of Oxford, Oxford, United Kingdom
| | - Rina Ariga
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Vicente Grau
- Institute of Biomedical Engineering (IBME), University of Oxford, Oxford, United Kingdom
| | - Blanca Rodriguez
- Department of Computer Science, University of Oxford, Oxford, United Kingdom
| |
Collapse
|
8
|
Kirn B, Walmsley J, Lumens J. Uniqueness of local myocardial strain patterns with respect to activation time and contractility of the failing heart: a computational study. Biomed Eng Online 2018; 17:182. [PMID: 30518387 PMCID: PMC6280493 DOI: 10.1186/s12938-018-0614-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2018] [Accepted: 11/27/2018] [Indexed: 01/26/2023] Open
Abstract
Background Myocardial deformation measured by strain is used to detect electro-mechanical abnormalities in cardiac tissue. Estimation of myocardial properties from regional strain patterns when multiple pathologies are present is therefore a promising application of computer modelling. However, if different tissue properties lead to indistinguishable strain patterns (‘degeneracy’), the applicability of any such method will be limited. We investigated whether estimation of local activation time (AT) and contractility from myocardial strain patterns is theoretically possible. Methods For four different global cardiac pathologies local myocardial strain patterns for 1025 combinations of AT and contractility were simulated with a computational model (CircAdapt). For each strain pattern, a cohort of similar patterns was found within estimated measurement error using the sum of least-squared differences. Cohort members came from (1) the same pathology only, and (2) all four pathologies. Uncertainty was calculated as accuracy and precision of cohort members in parameter space. Connectedness within the cohorts was also studied. Results We found that cohorts drawn from one pathology had parameters with adjacent values although their distribution was neither constant nor symmetrical. In comparison cohorts drawn from four pathologies had disconnected components with drastically different parameter values and accuracy and precision values up to three times higher. Conclusions Global pathology must be known when extracting AT and contractility from strain patterns, otherwise degeneracy occurs causing unacceptable uncertainty in derived parameters. Electronic supplementary material The online version of this article (10.1186/s12938-018-0614-1) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- Borut Kirn
- Department of Physiology, Medical Faculty, University of Ljubljana, Zaloska 4, 1000, Ljubljana, Slovenia.
| | - John Walmsley
- Department of Biomedical Engineering, CARIM School for Cardiovascular Diseases, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Joost Lumens
- Department of Biomedical Engineering, CARIM School for Cardiovascular Diseases, Maastricht University Medical Center, Maastricht, The Netherlands
| |
Collapse
|
9
|
Lawson BA, Burrage K, Burrage P, Drovandi CC, Bueno-Orovio A. Slow Recovery of Excitability Increases Ventricular Fibrillation Risk as Identified by Emulation. Front Physiol 2018; 9:1114. [PMID: 30210355 PMCID: PMC6121112 DOI: 10.3389/fphys.2018.01114] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2018] [Accepted: 07/25/2018] [Indexed: 12/28/2022] Open
Abstract
Purpose: Rotor stability and meandering are key mechanisms determining and sustaining cardiac fibrillation, with important implications for anti-arrhythmic drug development. However, little is yet known on how rotor dynamics are modulated by variability in cellular electrophysiology, particularly on kinetic properties of ion channel recovery. Methods: We propose a novel emulation approach, based on Gaussian process regression augmented with machine learning, for data enrichment, automatic detection, classification, and analysis of re-entrant biomarkers in cardiac tissue. More than 5,000 monodomain simulations of long-lasting arrhythmic episodes with Fenton-Karma ionic dynamics, further enriched by emulation to 80 million electrophysiological scenarios, were conducted to investigate the role of variability in ion channel densities and kinetics in modulating rotor-driven arrhythmic behavior. Results: Our methods predicted the class of excitation behavior with classification accuracy up to 96%, and emulation effectively predicted frequency, stability, and spatial biomarkers of functional re-entry. We demonstrate that the excitation wavelength interpretation of re-entrant behavior hides critical information about rotor persistence and devolution into fibrillation. In particular, whereas action potential duration directly modulates rotor frequency and meandering, critical windows of excitability are identified as the main determinants of breakup. Further novel electrophysiological insights of particular relevance for ventricular arrhythmias arise from our multivariate analysis, including the role of incomplete activation of slow inward currents in mediating tissue rate-dependence and dispersion of repolarization, and the emergence of slow recovery of excitability as a significant promoter of this mechanism of dispersion and increased arrhythmic risk. Conclusions: Our results mechanistically explain pro-arrhythmic effects of class Ic anti-arrhythmics in the ventricles despite their established role in the pharmacological management of atrial fibrillation. This is mediated by their slow recovery of excitability mode of action, promoting incomplete activation of slow inward currents and therefore increased dispersion of repolarization, given the larger influence of these currents in modulating the action potential in the ventricles compared to the atria. These results exemplify the potential of emulation techniques in elucidating novel mechanisms of arrhythmia and further application to cardiac electrophysiology.
Collapse
Affiliation(s)
- Brodie A Lawson
- ARC Centre of Excellence for Mathematical and Statistical Frontiers, School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD, Australia
| | - Kevin Burrage
- ARC Centre of Excellence for Mathematical and Statistical Frontiers, School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD, Australia.,Department of Computer Science, University of Oxford, Oxford, United Kingdom
| | - Pamela Burrage
- ARC Centre of Excellence for Mathematical and Statistical Frontiers, School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD, Australia
| | - Christopher C Drovandi
- ARC Centre of Excellence for Mathematical and Statistical Frontiers, School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD, Australia
| | | |
Collapse
|
10
|
Lyon A, Mincholé A, Martínez JP, Laguna P, Rodriguez B. Computational techniques for ECG analysis and interpretation in light of their contribution to medical advances. J R Soc Interface 2018; 15:20170821. [PMID: 29321268 PMCID: PMC5805987 DOI: 10.1098/rsif.2017.0821] [Citation(s) in RCA: 74] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2017] [Accepted: 12/08/2017] [Indexed: 01/09/2023] Open
Abstract
Widely developed for clinical screening, electrocardiogram (ECG) recordings capture the cardiac electrical activity from the body surface. ECG analysis can therefore be a crucial first step to help diagnose, understand and predict cardiovascular disorders responsible for 30% of deaths worldwide. Computational techniques, and more specifically machine learning techniques and computational modelling are powerful tools for classification, clustering and simulation, and they have recently been applied to address the analysis of medical data, especially ECG data. This review describes the computational methods in use for ECG analysis, with a focus on machine learning and 3D computer simulations, as well as their accuracy, clinical implications and contributions to medical advances. The first section focuses on heartbeat classification and the techniques developed to extract and classify abnormal from regular beats. The second section focuses on patient diagnosis from whole recordings, applied to different diseases. The third section presents real-time diagnosis and applications to wearable devices. The fourth section highlights the recent field of personalized ECG computer simulations and their interpretation. Finally, the discussion section outlines the challenges of ECG analysis and provides a critical assessment of the methods presented. The computational methods reported in this review are a strong asset for medical discoveries and their translation to the clinical world may lead to promising advances.
Collapse
Affiliation(s)
- Aurore Lyon
- Department of Computer Science, British Heart Foundation, Oxford, UK
| | - Ana Mincholé
- Department of Computer Science, British Heart Foundation, Oxford, UK
| | - Juan Pablo Martínez
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) Group, University of Zaragoza, CIBER-BBN, Zaragoza, Spain
| | - Pablo Laguna
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) Group, University of Zaragoza, CIBER-BBN, Zaragoza, Spain
| | - Blanca Rodriguez
- Department of Computer Science, British Heart Foundation, Oxford, UK
| |
Collapse
|
11
|
Wallman M, Sandberg F. Characterisation of human AV-nodal properties using a network model. Med Biol Eng Comput 2017; 56:247-259. [DOI: 10.1007/s11517-017-1684-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2017] [Accepted: 07/03/2017] [Indexed: 02/05/2023]
|
12
|
Feng Y, Guo Z, Dong Z, Zhou XY, Kwok KW, Ernst S, Lee SL. An efficient cardiac mapping strategy for radiofrequency catheter ablation with active learning. Int J Comput Assist Radiol Surg 2017; 12:1199-1207. [PMID: 28477277 PMCID: PMC5509834 DOI: 10.1007/s11548-017-1587-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2017] [Accepted: 02/28/2017] [Indexed: 11/28/2022]
Abstract
OBJECTIVE A major challenge in radiofrequency catheter ablation procedures is the voltage and activation mapping of the endocardium, given a limited mapping time. By learning from expert interventional electrophysiologists (operators), while also making use of an active-learning framework, guidance on performing cardiac voltage mapping can be provided to novice operators or even directly to catheter robots. METHODS A learning from demonstration (LfD) framework, based upon previous cardiac mapping procedures performed by an expert operator, in conjunction with Gaussian process (GP) model-based active learning, was developed to efficiently perform voltage mapping over right ventricles (RV). The GP model was used to output the next best mapping point, while getting updated towards the underlying voltage data pattern as more mapping points are taken. A regularized particle filter was used to keep track of the kernel hyperparameter used by GP. The travel cost of the catheter tip was incorporated to produce time-efficient mapping sequences. RESULTS The proposed strategy was validated on a simulated 2D grid mapping task, with leave-one-out experiments on 25 retrospective datasets, in an RV phantom using the Stereotaxis Niobe® remote magnetic navigation system, and on a tele-operated catheter robot. In comparison with an existing geometry-based method, regression error was reduced and was minimized at a faster rate over retrospective procedure data. CONCLUSION A new method of catheter mapping guidance has been proposed based on LfD and active learning. The proposed method provides real-time guidance for the procedure, as well as a live evaluation of mapping sufficiency.
Collapse
Affiliation(s)
- Yingjing Feng
- Hamlyn Centre and Department of Computing, Imperial College London, London, UK
| | - Ziyan Guo
- Department of Mechanical Engineering, The University of Hong Kong, Hong Kong, China
| | - Ziyang Dong
- Department of Mechanical Engineering, The University of Hong Kong, Hong Kong, China
| | - Xiao-Yun Zhou
- Hamlyn Centre and Department of Computing, Imperial College London, London, UK
| | - Ka-Wai Kwok
- Department of Mechanical Engineering, The University of Hong Kong, Hong Kong, China
| | | | - Su-Lin Lee
- Hamlyn Centre and Department of Computing, Imperial College London, London, UK.
| |
Collapse
|
13
|
Giffard-Roisin S, Jackson T, Fovargue L, Lee J, Delingette H, Razavi R, Ayache N, Sermesant M. Noninvasive Personalization of a Cardiac Electrophysiology Model From Body Surface Potential Mapping. IEEE Trans Biomed Eng 2016; 64:2206-2218. [PMID: 28113292 DOI: 10.1109/tbme.2016.2629849] [Citation(s) in RCA: 44] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
GOAL We use noninvasive data (body surface potential mapping, BSPM) to personalize the main parameters of a cardiac electrophysiological (EP) model for predicting the response to different pacing conditions. METHODS First, an efficient forward model is proposed, coupling the Mitchell-Schaeffer transmembrane potential model with a current dipole formulation. Then, we estimate the main parameters of the cardiac model: activation onset location and tissue conductivity. A large patient-specific database of simulated BSPM is generated, from which specific features are extracted to train a machine learning algorithm. The activation onset location is computed from a Kernel Ridge Regression and a second regression calibrates the global ventricular conductivity. RESULTS The evaluation of the results is done both on a benchmark dataset of a patient with premature ventricular contraction (PVC) and on five nonischaemic implanted cardiac resynchonization therapy (CRT) patients with a total of 21 different pacing conditions. Good personalization results were found in terms of the activation onset location for the PVC (mean distance error, MDE = 20.3 mm), for the pacing sites (MDE = 21.7 mm) and for the CRT patients (MDE = 24.6 mm). We tested the predictive power of the personalized model for biventricular pacing and showed that we could predict the new electrical activity patterns with a good accuracy in terms of BSPM signals. CONCLUSION We have personalized the cardiac EP model and predicted new patient-specific pacing conditions. SIGNIFICANCE This is an encouraging first step towards a noninvasive preoperative prediction of the response to different pacing conditions to assist clinicians for CRT patient selection and therapy planning.
Collapse
|
14
|
Gemmell P, Burrage K, Rodríguez B, Quinn TA. Rabbit-specific computational modelling of ventricular cell electrophysiology: Using populations of models to explore variability in the response to ischemia. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2016; 121:169-84. [PMID: 27320382 PMCID: PMC5405055 DOI: 10.1016/j.pbiomolbio.2016.06.003] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/21/2016] [Accepted: 06/13/2016] [Indexed: 11/04/2022]
Abstract
Computational modelling, combined with experimental investigations, is a powerful method for investigating complex cardiac electrophysiological behaviour. The use of rabbit-specific models, due to the similarities of cardiac electrophysiology in this species with human, is especially prevalent. In this paper, we first briefly review rabbit-specific computational modelling of ventricular cell electrophysiology, multi-cellular simulations including cellular heterogeneity, and acute ischemia. This mini-review is followed by an original computational investigation of variability in the electrophysiological response of two experimentally-calibrated populations of rabbit-specific ventricular myocyte action potential models to acute ischemia. We performed a systematic exploration of the response of the model populations to varying degrees of ischemia and individual ischemic parameters, to investigate their individual and combined effects on action potential duration and refractoriness. This revealed complex interactions between model population variability and ischemic factors, which combined to enhance variability during ischemia. This represents an important step towards an improved understanding of the role that physiological variability may play in electrophysiological alterations during acute ischemia.
Collapse
Affiliation(s)
- Philip Gemmell
- Department of Computer Science, University of Oxford, Oxford, UK
| | - Kevin Burrage
- Department of Computer Science, University of Oxford, Oxford, UK; School of Mathematical Sciences and ARC Centre of Excellence, ACEMS, Queensland University of Technology, Brisbane, Australia
| | - Blanca Rodríguez
- Department of Computer Science, University of Oxford, Oxford, UK
| | - T Alexander Quinn
- Department of Physiology and Biophysics, Dalhousie University, 5850 College St, Lab 3F, Halifax, NS B3H 4R2, Canada; School of Biomedical Engineering, Dalhousie University, 5850 College St, Lab 3F, Halifax, NS B3H 4R2, Canada.
| |
Collapse
|
15
|
Hill AP, Perry MD, Abi-Gerges N, Couderc JP, Fermini B, Hancox JC, Knollmann BC, Mirams GR, Skinner J, Zareba W, Vandenberg JI. Computational cardiology and risk stratification for sudden cardiac death: one of the grand challenges for cardiology in the 21st century. J Physiol 2016; 594:6893-6908. [PMID: 27060987 PMCID: PMC5134408 DOI: 10.1113/jp272015] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2015] [Accepted: 03/16/2016] [Indexed: 12/25/2022] Open
Abstract
Risk stratification in the context of sudden cardiac death has been acknowledged as one of the major challenges facing cardiology for the past four decades. In recent years, the advent of high performance computing has facilitated organ-level simulation of the heart, meaning we can now examine the causes, mechanisms and impact of cardiac dysfunction in silico. As a result, computational cardiology, largely driven by the Physiome project, now stands at the threshold of clinical utility in regards to risk stratification and treatment of patients at risk of sudden cardiac death. In this white paper, we outline a roadmap of what needs to be done to make this translational step, using the relatively well-developed case of acquired or drug-induced long QT syndrome as an exemplar case.
Collapse
Affiliation(s)
- Adam P Hill
- Victor Chang Cardiac Research Institute, 405 Liverpool Street, Darlinghurst, NSW, 2010, Australia.,St. Vincent's Clinical School, University of New South Wales, Sydney, NSW, 2052, Australia
| | - Matthew D Perry
- Victor Chang Cardiac Research Institute, 405 Liverpool Street, Darlinghurst, NSW, 2010, Australia.,St. Vincent's Clinical School, University of New South Wales, Sydney, NSW, 2052, Australia
| | - Najah Abi-Gerges
- AnaBios Corporation, 3030 Bunker Hill St., San Diego, CA, 92109, USA
| | | | - Bernard Fermini
- Global Safety Pharmacology, Pfizer Inc, MS8274-1347 Eastern Point Road, Groton, CT, 06340, USA
| | - Jules C Hancox
- School of Physiology, Pharmacology and Neuroscience, University of Bristol, Bristol, UK
| | - Bjorn C Knollmann
- Vanderbilt University School of Medicine, 1285 Medical Research Building IV, Nashville, Tennessee, 37232, USA
| | - Gary R Mirams
- Computational Biology, Department of Computer Science, University of Oxford, Oxford, United Kingdom
| | - Jon Skinner
- Cardiac Inherited Disease Group, Starship Hospital, Auckland, New Zealand
| | - Wojciech Zareba
- University of Rochester Medical Center, Rochester, NY, 14642, USA
| | - Jamie I Vandenberg
- Victor Chang Cardiac Research Institute, 405 Liverpool Street, Darlinghurst, NSW, 2010, Australia.,St. Vincent's Clinical School, University of New South Wales, Sydney, NSW, 2052, Australia
| |
Collapse
|
16
|
Mirams GR, Pathmanathan P, Gray RA, Challenor P, Clayton RH. Uncertainty and variability in computational and mathematical models of cardiac physiology. J Physiol 2016; 594:6833-6847. [PMID: 26990229 PMCID: PMC5134370 DOI: 10.1113/jp271671] [Citation(s) in RCA: 90] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2015] [Accepted: 02/28/2016] [Indexed: 12/22/2022] Open
Abstract
KEY POINTS Mathematical and computational models of cardiac physiology have been an integral component of cardiac electrophysiology since its inception, and are collectively known as the Cardiac Physiome. We identify and classify the numerous sources of variability and uncertainty in model formulation, parameters and other inputs that arise from both natural variation in experimental data and lack of knowledge. The impact of uncertainty on the outputs of Cardiac Physiome models is not well understood, and this limits their utility as clinical tools. We argue that incorporating variability and uncertainty should be a high priority for the future of the Cardiac Physiome. We suggest investigating the adoption of approaches developed in other areas of science and engineering while recognising unique challenges for the Cardiac Physiome; it is likely that novel methods will be necessary that require engagement with the mathematics and statistics community. ABSTRACT The Cardiac Physiome effort is one of the most mature and successful applications of mathematical and computational modelling for describing and advancing the understanding of physiology. After five decades of development, physiological cardiac models are poised to realise the promise of translational research via clinical applications such as drug development and patient-specific approaches as well as ablation, cardiac resynchronisation and contractility modulation therapies. For models to be included as a vital component of the decision process in safety-critical applications, rigorous assessment of model credibility will be required. This White Paper describes one aspect of this process by identifying and classifying sources of variability and uncertainty in models as well as their implications for the application and development of cardiac models. We stress the need to understand and quantify the sources of variability and uncertainty in model inputs, and the impact of model structure and complexity and their consequences for predictive model outputs. We propose that the future of the Cardiac Physiome should include a probabilistic approach to quantify the relationship of variability and uncertainty of model inputs and outputs.
Collapse
Affiliation(s)
- Gary R Mirams
- Computational Biology, Department of Computer Science, University of Oxford, Oxford, OX1 3QD, UK
| | - Pras Pathmanathan
- US Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, MD, 20993, USA
| | - Richard A Gray
- US Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, MD, 20993, USA
| | - Peter Challenor
- College of Engineering, Mathematics and Physical Science, University of Exeter, Exeter, EX4 4QF, UK
| | - Richard H Clayton
- Insigneo institute for in-silico medicine and Department of Computer Science, University of Sheffield, Regent Court, Sheffield, S1 4DP, UK
| |
Collapse
|
17
|
Neumann D, Mansi T, Itu L, Georgescu B, Kayvanpour E, Sedaghat-Hamedani F, Amr A, Haas J, Katus H, Meder B, Steidl S, Hornegger J, Comaniciu D. A self-taught artificial agent for multi-physics computational model personalization. Med Image Anal 2016; 34:52-64. [PMID: 27133269 DOI: 10.1016/j.media.2016.04.003] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2016] [Revised: 04/08/2016] [Accepted: 04/19/2016] [Indexed: 02/05/2023]
Abstract
Personalization is the process of fitting a model to patient data, a critical step towards application of multi-physics computational models in clinical practice. Designing robust personalization algorithms is often a tedious, time-consuming, model- and data-specific process. We propose to use artificial intelligence concepts to learn this task, inspired by how human experts manually perform it. The problem is reformulated in terms of reinforcement learning. In an off-line phase, Vito, our self-taught artificial agent, learns a representative decision process model through exploration of the computational model: it learns how the model behaves under change of parameters. The agent then automatically learns an optimal strategy for on-line personalization. The algorithm is model-independent; applying it to a new model requires only adjusting few hyper-parameters of the agent and defining the observations to match. The full knowledge of the model itself is not required. Vito was tested in a synthetic scenario, showing that it could learn how to optimize cost functions generically. Then Vito was applied to the inverse problem of cardiac electrophysiology and the personalization of a whole-body circulation model. The obtained results suggested that Vito could achieve equivalent, if not better goodness of fit than standard methods, while being more robust (up to 11% higher success rates) and with faster (up to seven times) convergence rate. Our artificial intelligence approach could thus make personalization algorithms generalizable and self-adaptable to any patient and any model.
Collapse
Affiliation(s)
- Dominik Neumann
- Medical Imaging Technologies, Siemens Healthcare GmbH, Erlangen, Germany; Pattern Recognition Lab, FAU Erlangen-Nürnberg, Erlangen, Germany.
| | - Tommaso Mansi
- Medical Imaging Technologies, Siemens Healthcare, Princeton, USA
| | - Lucian Itu
- Siemens Corporate Technology, Siemens SRL, Brasov, Romania; Transilvania University of Brasov, Brasov, Romania
| | - Bogdan Georgescu
- Medical Imaging Technologies, Siemens Healthcare, Princeton, USA
| | - Elham Kayvanpour
- Department of Internal Medicine III, University Hospital Heidelberg, Germany
| | | | - Ali Amr
- Department of Internal Medicine III, University Hospital Heidelberg, Germany
| | - Jan Haas
- Department of Internal Medicine III, University Hospital Heidelberg, Germany
| | - Hugo Katus
- Department of Internal Medicine III, University Hospital Heidelberg, Germany
| | - Benjamin Meder
- Department of Internal Medicine III, University Hospital Heidelberg, Germany
| | - Stefan Steidl
- Pattern Recognition Lab, FAU Erlangen-Nürnberg, Erlangen, Germany
| | | | - Dorin Comaniciu
- Medical Imaging Technologies, Siemens Healthcare, Princeton, USA
| |
Collapse
|
18
|
Cantwell CD, Roney CH, Ng FS, Siggers JH, Sherwin SJ, Peters NS. Techniques for automated local activation time annotation and conduction velocity estimation in cardiac mapping. Comput Biol Med 2015; 65:229-42. [PMID: 25978869 PMCID: PMC4593301 DOI: 10.1016/j.compbiomed.2015.04.027] [Citation(s) in RCA: 114] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2015] [Revised: 04/13/2015] [Accepted: 04/16/2015] [Indexed: 11/24/2022]
Abstract
Measurements of cardiac conduction velocity provide valuable functional and structural insight into the initiation and perpetuation of cardiac arrhythmias, in both a clinical and laboratory context. The interpretation of activation wavefronts and their propagation can identify mechanistic properties of a broad range of electrophysiological pathologies. However, the sparsity, distribution and uncertainty of recorded data make accurate conduction velocity calculation difficult. A wide range of mathematical approaches have been proposed for addressing this challenge, often targeted towards specific data modalities, species or recording environments. Many of these algorithms require identification of activation times from electrogram recordings which themselves may have complex morphology or low signal-to-noise ratio. This paper surveys algorithms designed for identifying local activation times and computing conduction direction and speed. Their suitability for use in different recording contexts and applications is assessed.
Collapse
Affiliation(s)
- C D Cantwell
- Department of Aeronautics, Imperial College London, South Kensington Campus, London, UK; National Heart and Lung Institute, Imperial College London, South Kensington Campus, London, UK.
| | - C H Roney
- Department of Bioengineering, Imperial College London, South Kensington Campus, London, UK; National Heart and Lung Institute, Imperial College London, South Kensington Campus, London, UK
| | - F S Ng
- National Heart and Lung Institute, Imperial College London, South Kensington Campus, London, UK
| | - J H Siggers
- Department of Bioengineering, Imperial College London, South Kensington Campus, London, UK
| | - S J Sherwin
- Department of Aeronautics, Imperial College London, South Kensington Campus, London, UK
| | - N S Peters
- National Heart and Lung Institute, Imperial College London, South Kensington Campus, London, UK
| |
Collapse
|
19
|
Kayvanpour E, Mansi T, Sedaghat-Hamedani F, Amr A, Neumann D, Georgescu B, Seegerer P, Kamen A, Haas J, Frese KS, Irawati M, Wirsz E, King V, Buss S, Mereles D, Zitron E, Keller A, Katus HA, Comaniciu D, Meder B. Towards Personalized Cardiology: Multi-Scale Modeling of the Failing Heart. PLoS One 2015; 10:e0134869. [PMID: 26230546 PMCID: PMC4521877 DOI: 10.1371/journal.pone.0134869] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2015] [Accepted: 07/14/2015] [Indexed: 01/14/2023] Open
Abstract
BACKGROUND Despite modern pharmacotherapy and advanced implantable cardiac devices, overall prognosis and quality of life of HF patients remain poor. This is in part due to insufficient patient stratification and lack of individualized therapy planning, resulting in less effective treatments and a significant number of non-responders. METHODS AND RESULTS State-of-the-art clinical phenotyping was acquired, including magnetic resonance imaging (MRI) and biomarker assessment. An individualized, multi-scale model of heart function covering cardiac anatomy, electrophysiology, biomechanics and hemodynamics was estimated using a robust framework. The model was computed on n=46 HF patients, showing for the first time that advanced multi-scale models can be fitted consistently on large cohorts. Novel multi-scale parameters derived from the model of all cases were analyzed and compared against clinical parameters, cardiac imaging, lab tests and survival scores to evaluate the explicative power of the model and its potential for better patient stratification. Model validation was pursued by comparing clinical parameters that were not used in the fitting process against model parameters. CONCLUSION This paper illustrates how advanced multi-scale models can complement cardiovascular imaging and how they could be applied in patient care. Based on obtained results, it becomes conceivable that, after thorough validation, such heart failure models could be applied for patient management and therapy planning in the future, as we illustrate in one patient of our cohort who received CRT-D implantation.
Collapse
Affiliation(s)
- Elham Kayvanpour
- Department of Medicine III, University of Heidelberg, Heidelberg, Germany
- DZHK (German Centre for Cardiovascular Research), Heidelberg, Germany
| | - Tommaso Mansi
- Siemens Corporation, Corporate Technology, Imaging and Computer Vision, Princeton, New Jersey, United States of America
| | - Farbod Sedaghat-Hamedani
- Department of Medicine III, University of Heidelberg, Heidelberg, Germany
- DZHK (German Centre for Cardiovascular Research), Heidelberg, Germany
| | - Ali Amr
- Department of Medicine III, University of Heidelberg, Heidelberg, Germany
- DZHK (German Centre for Cardiovascular Research), Heidelberg, Germany
| | - Dominik Neumann
- Siemens Corporation, Corporate Technology, Imaging and Computer Vision, Princeton, New Jersey, United States of America
| | - Bogdan Georgescu
- Siemens Corporation, Corporate Technology, Imaging and Computer Vision, Princeton, New Jersey, United States of America
| | - Philipp Seegerer
- Siemens Corporation, Corporate Technology, Imaging and Computer Vision, Princeton, New Jersey, United States of America
| | - Ali Kamen
- Siemens Corporation, Corporate Technology, Imaging and Computer Vision, Princeton, New Jersey, United States of America
| | - Jan Haas
- Department of Medicine III, University of Heidelberg, Heidelberg, Germany
- DZHK (German Centre for Cardiovascular Research), Heidelberg, Germany
| | - Karen S. Frese
- Department of Medicine III, University of Heidelberg, Heidelberg, Germany
- DZHK (German Centre for Cardiovascular Research), Heidelberg, Germany
| | - Maria Irawati
- Department of Medicine III, University of Heidelberg, Heidelberg, Germany
| | - Emil Wirsz
- Siemens AG, Corporate Technology, Erlangen, Germany
| | - Vanessa King
- Siemens Corporation, Corporate Technology, Sensor Technologies, Princeton, New Jersey, United States of America
| | - Sebastian Buss
- Department of Medicine III, University of Heidelberg, Heidelberg, Germany
| | - Derliz Mereles
- Department of Medicine III, University of Heidelberg, Heidelberg, Germany
| | - Edgar Zitron
- Department of Medicine III, University of Heidelberg, Heidelberg, Germany
| | - Andreas Keller
- Biomarker Discovery Center Heidelberg, Heidelberg, Germany
- Department of Human Genetics, Saarland University, Homburg, Germany
| | - Hugo A. Katus
- Department of Medicine III, University of Heidelberg, Heidelberg, Germany
- DZHK (German Centre for Cardiovascular Research), Heidelberg, Germany
- Klaus Tschira Institute for Computational Cardiology, Heidelberg, Germany
| | - Dorin Comaniciu
- Siemens Corporation, Corporate Technology, Imaging and Computer Vision, Princeton, New Jersey, United States of America
| | - Benjamin Meder
- Department of Medicine III, University of Heidelberg, Heidelberg, Germany
- DZHK (German Centre for Cardiovascular Research), Heidelberg, Germany
- Klaus Tschira Institute for Computational Cardiology, Heidelberg, Germany
| |
Collapse
|
20
|
Robust image-based estimation of cardiac tissue parameters and their uncertainty from noisy data. ACTA ACUST UNITED AC 2015; 17:9-16. [PMID: 25485357 DOI: 10.1007/978-3-319-10470-6_2] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/22/2023]
Abstract
Clinical applications of computational cardiac models require precise personalization, i.e. fitting model parameters to capture patient's physiology. However, due to parameter non-identifiability, limited data, uncertainty in the clinical measurements, and modeling assumptions, various combinations of parameter values may exist that yield the same quality of fit. Hence, there is a need for quantifying the uncertainty in estimated parameters and to ascertain the uniqueness of the found solution. This paper presents a stochastic method to estimate the parameters of an image-based electromechanical model of the heart and their uncertainty due to noise in measurements. First, Bayesian inference is applied to fully estimate the posterior probability density function (PDF) of the model. To that end, Markov Chain Monte Carlo sampling is used, which is made computationally tractable by employing a fast surrogate model based on Polynomial Chaos Expansion, instead of the true forward model. Then, we use the mean-shift algorithm to automatically find the modes of the PDF and select the most likely one while being robust to noise. The approach is used to estimate global active stress and passive stiffness from invasive pressure and image-based volume quantification. Experiments on eight patients showed that not only our approach yielded goodness of fits equivalent to a well-established deterministic method, but we could also demonstrate the non-uniqueness of the problem and report uncertainty estimates, crucial information for subsequent clinical assessments of the personalized models.
Collapse
|
21
|
Sadrieh A, Domanski L, Pitt-Francis J, Mann SA, Hodkinson EC, Ng CA, Perry MD, Taylor JA, Gavaghan D, Subbiah RN, Vandenberg JI, Hill AP. Multiscale cardiac modelling reveals the origins of notched T waves in long QT syndrome type 2. Nat Commun 2014; 5:5069. [PMID: 25254353 DOI: 10.1038/ncomms6069] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2014] [Accepted: 08/26/2014] [Indexed: 01/08/2023] Open
Abstract
The heart rhythm disorder long QT syndrome (LQTS) can result in sudden death in the young or remain asymptomatic into adulthood. The features of the surface electrocardiogram (ECG), a measure of the electrical activity of the heart, can be equally variable in LQTS patients, posing well-described diagnostic dilemmas. Here we report a correlation between QT interval prolongation and T-wave notching in LQTS2 patients and use a novel computational framework to investigate how individual ionic currents, as well as cellular and tissue level factors, contribute to notched T waves. Furthermore, we show that variable expressivity of ECG features observed in LQTS2 patients can be explained by as little as 20% variation in the levels of ionic conductances that contribute to repolarization reserve. This has significant implications for interpretation of whole-genome sequencing data and underlies the importance of interpreting the entire molecular signature of disease in any given individual.
Collapse
Affiliation(s)
- Arash Sadrieh
- 1] Division of Molecular Cardiology and Biophysics, Victor Chang Cardiac Research Institute, 405 Liverpool Street, Darlinghurst, New South Wales 2010, Australia [2] St Vincent's Clinical School, University of New South Wales, Sydney, New South Wales 2052, Australia
| | - Luke Domanski
- CSIRO eResearch and Computational and Simulation Sciences, Canberra, Australian Capital Territory 2601, Australia
| | - Joe Pitt-Francis
- Department of Computer Science, University of Oxford, Oxford OX1 3QD, UK
| | - Stefan A Mann
- 1] Division of Molecular Cardiology and Biophysics, Victor Chang Cardiac Research Institute, 405 Liverpool Street, Darlinghurst, New South Wales 2010, Australia [2] St Vincent's Clinical School, University of New South Wales, Sydney, New South Wales 2052, Australia
| | - Emily C Hodkinson
- 1] Division of Molecular Cardiology and Biophysics, Victor Chang Cardiac Research Institute, 405 Liverpool Street, Darlinghurst, New South Wales 2010, Australia [2] St Vincent's Clinical School, University of New South Wales, Sydney, New South Wales 2052, Australia
| | - Chai-Ann Ng
- 1] Division of Molecular Cardiology and Biophysics, Victor Chang Cardiac Research Institute, 405 Liverpool Street, Darlinghurst, New South Wales 2010, Australia [2] St Vincent's Clinical School, University of New South Wales, Sydney, New South Wales 2052, Australia
| | - Matthew D Perry
- 1] Division of Molecular Cardiology and Biophysics, Victor Chang Cardiac Research Institute, 405 Liverpool Street, Darlinghurst, New South Wales 2010, Australia [2] St Vincent's Clinical School, University of New South Wales, Sydney, New South Wales 2052, Australia
| | - John A Taylor
- CSIRO eResearch and Computational and Simulation Sciences, Canberra, Australian Capital Territory 2601, Australia
| | - David Gavaghan
- Department of Computer Science, University of Oxford, Oxford OX1 3QD, UK
| | - Rajesh N Subbiah
- 1] Division of Molecular Cardiology and Biophysics, Victor Chang Cardiac Research Institute, 405 Liverpool Street, Darlinghurst, New South Wales 2010, Australia [2] St Vincent's Clinical School, University of New South Wales, Sydney, New South Wales 2052, Australia
| | - Jamie I Vandenberg
- 1] Division of Molecular Cardiology and Biophysics, Victor Chang Cardiac Research Institute, 405 Liverpool Street, Darlinghurst, New South Wales 2010, Australia [2] St Vincent's Clinical School, University of New South Wales, Sydney, New South Wales 2052, Australia
| | - Adam P Hill
- 1] Division of Molecular Cardiology and Biophysics, Victor Chang Cardiac Research Institute, 405 Liverpool Street, Darlinghurst, New South Wales 2010, Australia [2] St Vincent's Clinical School, University of New South Wales, Sydney, New South Wales 2052, Australia
| |
Collapse
|
22
|
Zettinig O, Mansi T, Neumann D, Georgescu B, Rapaka S, Seegerer P, Kayvanpour E, Sedaghat-Hamedani F, Amr A, Haas J, Steen H, Katus H, Meder B, Navab N, Kamen A, Comaniciu D. Data-driven estimation of cardiac electrical diffusivity from 12-lead ECG signals. Med Image Anal 2014; 18:1361-76. [PMID: 24857832 DOI: 10.1016/j.media.2014.04.011] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2014] [Revised: 03/17/2014] [Accepted: 04/10/2014] [Indexed: 11/25/2022]
Abstract
Diagnosis and treatment of dilated cardiomyopathy (DCM) is challenging due to a large variety of causes and disease stages. Computational models of cardiac electrophysiology (EP) can be used to improve the assessment and prognosis of DCM, plan therapies and predict their outcome, but require personalization. In this work, we present a data-driven approach to estimate the electrical diffusivity parameter of an EP model from standard 12-lead electrocardiograms (ECG). An efficient forward model based on a mono-domain, phenomenological Lattice-Boltzmann model of cardiac EP, and a boundary element-based mapping of potentials to the body surface is employed. The electrical diffusivity of myocardium, left ventricle and right ventricle endocardium is then estimated using polynomial regression which takes as input the QRS duration and electrical axis. After validating the forward model, we computed 9500 EP simulations on 19 different DCM patients in just under three seconds each to learn the regression model. Using this database, we quantify the intrinsic uncertainty of electrical diffusion for given ECG features and show in a leave-one-patient-out cross-validation that the regression method is able to predict myocardium diffusion within the uncertainty range. Finally, our approach is tested on the 19 cases using their clinical ECG. 84% of them could be personalized using our method, yielding mean prediction errors of 18.7ms for the QRS duration and 6.5° for the electrical axis, both values being within clinical acceptability. By providing an estimate of diffusion parameters from readily available clinical data, our data-driven approach could therefore constitute a first calibration step toward a more complete personalization of cardiac EP.
Collapse
Affiliation(s)
- Oliver Zettinig
- Siemens Corporate Technology, Imaging and Computer Vision, Princeton, NJ, USA; Computer Aided Medical Procedures, Technische Universität München, Germany
| | - Tommaso Mansi
- Siemens Corporate Technology, Imaging and Computer Vision, Princeton, NJ, USA.
| | - Dominik Neumann
- Siemens Corporate Technology, Imaging and Computer Vision, Princeton, NJ, USA; Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany
| | - Bogdan Georgescu
- Siemens Corporate Technology, Imaging and Computer Vision, Princeton, NJ, USA
| | - Saikiran Rapaka
- Siemens Corporate Technology, Imaging and Computer Vision, Princeton, NJ, USA
| | - Philipp Seegerer
- Siemens Corporate Technology, Imaging and Computer Vision, Princeton, NJ, USA; Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany
| | | | | | - Ali Amr
- Heidelberg University Hospital, Heidelberg, Germany
| | - Jan Haas
- Heidelberg University Hospital, Heidelberg, Germany
| | | | - Hugo Katus
- Heidelberg University Hospital, Heidelberg, Germany
| | | | - Nassir Navab
- Computer Aided Medical Procedures, Technische Universität München, Germany
| | - Ali Kamen
- Siemens Corporate Technology, Imaging and Computer Vision, Princeton, NJ, USA
| | - Dorin Comaniciu
- Siemens Corporate Technology, Imaging and Computer Vision, Princeton, NJ, USA
| |
Collapse
|