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Wang T, Karel J, Invers-Rubio E, Hernández-Romero I, Peeters R, Bonizzi P, Guillem MS. Standardized 2D atrial mapping and its clinical applications. Comput Biol Med 2024; 168:107755. [PMID: 38039895 DOI: 10.1016/j.compbiomed.2023.107755] [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: 06/08/2023] [Revised: 10/10/2023] [Accepted: 11/20/2023] [Indexed: 12/03/2023]
Abstract
The visualization and comparison of electrophysiological information in the atrium among different patients could be facilitated by a standardized 2D atrial mapping. However, due to the complexity of the atrial anatomy, unfolding the 3D geometry into a 2D atrial mapping is challenging. In this study, we aim to develop a standardized approach to achieve a 2D atrial mapping that connects the left and right atria, while maintaining fixed positions and sizes of atrial segments across individuals. Atrial segmentation is a prerequisite for the process. Segmentation includes 19 different segments with 12 segments from the left atrium, 5 segments from the right atrium, and two segments for the atrial septum. To ensure consistent and physiologically meaningful segment connections, an automated procedure is applied to open up the atrial surfaces and project the 3D information into 2D. The corresponding 2D atrial mapping can then be utilized to visualize different electrophysiological information of a patient, such as activation time patterns or phase maps. This can in turn provide useful information for guiding catheter ablation. The proposed standardized 2D maps can also be used to compare more easily structural information like fibrosis distribution with rotor presence and location. We show several examples of visualization of different electrophysiological properties for both healthy subjects and patients affected by atrial fibrillation. These examples show that the proposed maps provide an easy way to visualize and interpret intra-subject information and perform inter-subject comparison, which may provide a reference framework for the analysis of the atrial fibrillation substrate before treatment, and during a catheter ablation procedure.
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Affiliation(s)
- Tiantian Wang
- Department of Advanced Computing Sciences, Maastricht University, The Netherlands
| | - Joël Karel
- Department of Advanced Computing Sciences, Maastricht University, The Netherlands.
| | - Eric Invers-Rubio
- Arrhythmia Unit, Hospital Clínic de Barcelona Cardiovascular Institute (ICCV), Universitat de Barcelona, Barcelona, Catalonia, Spain
| | | | - Ralf Peeters
- Department of Advanced Computing Sciences, Maastricht University, The Netherlands
| | - Pietro Bonizzi
- Department of Advanced Computing Sciences, Maastricht University, The Netherlands
| | - Maria S Guillem
- ITACA Institute, Universitat Politècnica de València, Valencia, Spain
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2
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Papathanasiou KA, Vrachatis DA, Kazantzis D, Kossyvakis C, Giotaki SG, Deftereos G, Raisakis K, Kaoukis A, Avramides D, Lambadiari V, Siasos G, Deftereos S. Left atrial appendage morphofunctional indices could be predictive of arrhythmia recurrence post-atrial fibrillation ablation: a meta-analysis. Egypt Heart J 2023; 75:29. [PMID: 37079174 PMCID: PMC10119349 DOI: 10.1186/s43044-023-00356-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 04/14/2023] [Indexed: 04/21/2023] Open
Abstract
BACKGROUND Left atrium changes are implicated in atrial fibrillation (AF) substrate and are predictive of AF outcomes. Left atrial appendage (LAA) is an integral component of left atrial structure and could be affected by atrial cardiomyopathy. We aimed to elucidate the association between LAA indices and late arrhythmia recurrence after atrial fibrillation catheter ablation (AFCA). METHODS The MEDLINE database, ClinicalTrials.gov, medRxiv and Cochrane Library were searched for studies evaluating LAA and late arrhythmia recurrence in patients undergoing AFCA. Data were pooled by meta-analysis using a random-effects model. The primary endpoint was pre-ablation difference in LAA anatomic or functional indices. RESULTS A total of 34 studies were found eligible and five LAA indices were analyzed. LAA ejection fraction and LAA emptying velocity were significantly lower in patients with AF recurrence post-ablation [SMD = - 0.66; 95% CI (- 1.01, - 0.32) and SMD = - 0.56; 95% CI (- 0.73, - 0.40) respectively] as compared to arrhythmia free controls. LAA volume and LAA orifice area were significantly higher in patients with AF recurrence post-ablation (SMD = 0.51; 95% CI 0.35-0.67, and SMD = 0.35; 95% CI 0.20-0.49, respectively) as compared to arrhythmia free controls. LAA morphology was not predictive of AF recurrence post-ablation (chicken wing morphology; OR 1.27; 95% CI 0.79-2.02). Moderate statistical heterogeneity and small case-control studies are the main limitations of our meta-analysis. CONCLUSIONS Our findings suggest that LAA ejection fraction, LAA emptying velocity, LAA orifice area and LAA volume differ between patients suffering from arrhythmia recurrence post-ablation and arrhythmia free counterparts, while LAA morphology is not predictive of AF recurrence.
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Affiliation(s)
- Konstantinos A Papathanasiou
- Second Department of Cardiology, National and Kapodistrian University of Athens, Medical School, Attikon University Hospital, 1 Rimini Str., Chaidari, Attiki, 12462, Athens, Greece.
| | - Dimitrios A Vrachatis
- Second Department of Cardiology, National and Kapodistrian University of Athens, Medical School, Attikon University Hospital, 1 Rimini Str., Chaidari, Attiki, 12462, Athens, Greece
| | - Dimitrios Kazantzis
- Bristol Eye Hospital, University Hospitals Bristol and Weston NHS Foundation Trust, Bristol, UK
| | | | - Sotiria G Giotaki
- Second Department of Cardiology, National and Kapodistrian University of Athens, Medical School, Attikon University Hospital, 1 Rimini Str., Chaidari, Attiki, 12462, Athens, Greece
| | - Gerasimos Deftereos
- Department of Cardiology, "G. Gennimatas" General Hospital of Athens, Athens, Greece
| | - Konstantinos Raisakis
- Department of Cardiology, "G. Gennimatas" General Hospital of Athens, Athens, Greece
| | - Andreas Kaoukis
- Department of Cardiology, "G. Gennimatas" General Hospital of Athens, Athens, Greece
| | - Dimitrios Avramides
- Department of Cardiology, "G. Gennimatas" General Hospital of Athens, Athens, Greece
| | - Vaia Lambadiari
- Second Department of Internal Medicine, National and Kapodistrian University of Athens, Medical School, Attikon University Hospital, 12462, Athens, Greece
| | - Gerasimos Siasos
- 3rd Department of Cardiology, National and Kapodistrian University of Athens, Medical School, Sotiria Chest Disease Hospital, Athens, Greece
| | - Spyridon Deftereos
- Second Department of Cardiology, National and Kapodistrian University of Athens, Medical School, Attikon University Hospital, 1 Rimini Str., Chaidari, Attiki, 12462, Athens, Greece
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Papathanasiou KA, Vrachatis DA, Deftereos S. A call for shorter blanking period, time to get off the ground. Europace 2023; 25:1195. [PMID: 36691738 PMCID: PMC10062320 DOI: 10.1093/europace/euac286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023] Open
Affiliation(s)
- Konstantinos A Papathanasiou
- Second Department of Cardiology, National and Kapodistrian University of Athens, Medical School, Attikon University Hospital, 1 Rimini Str., 12462 Athens, Greece
| | - Dimitrios A Vrachatis
- Second Department of Cardiology, National and Kapodistrian University of Athens, Medical School, Attikon University Hospital, 1 Rimini Str., 12462 Athens, Greece
| | - Spyridon Deftereos
- Second Department of Cardiology, National and Kapodistrian University of Athens, Medical School, Attikon University Hospital, 1 Rimini Str., 12462 Athens, Greece
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Ogbomo-Harmitt S, Muffoletto M, Zeidan A, Qureshi A, King AP, Aslanidi O. Exploring interpretability in deep learning prediction of successful ablation therapy for atrial fibrillation. Front Physiol 2023; 14:1054401. [PMID: 36998987 PMCID: PMC10043207 DOI: 10.3389/fphys.2023.1054401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 02/28/2023] [Indexed: 03/16/2023] Open
Abstract
Background: Radiofrequency catheter ablation (RFCA) therapy is the first-line treatment for atrial fibrillation (AF), the most common type of cardiac arrhythmia globally. However, the procedure currently has low success rates in dealing with persistent AF, with a reoccurrence rate of ∼50% post-ablation. Therefore, deep learning (DL) has increasingly been applied to improve RFCA treatment for AF. However, for a clinician to trust the prediction of a DL model, its decision process needs to be interpretable and have biomedical relevance.Aim: This study explores interpretability in DL prediction of successful RFCA therapy for AF and evaluates if pro-arrhythmogenic regions in the left atrium (LA) were used in its decision process.Methods: AF and its termination by RFCA have been simulated in MRI-derived 2D LA tissue models with segmented fibrotic regions (n = 187). Three ablation strategies were applied for each LA model: pulmonary vein isolation (PVI), fibrosis-based ablation (FIBRO) and a rotor-based ablation (ROTOR). The DL model was trained to predict the success of each RFCA strategy for each LA model. Three feature attribution (FA) map methods were then used to investigate interpretability of the DL model: GradCAM, Occlusions and LIME.Results: The developed DL model had an AUC (area under the receiver operating characteristic curve) of 0.78 ± 0.04 for predicting the success of the PVI strategy, 0.92 ± 0.02 for FIBRO and 0.77 ± 0.02 for ROTOR. GradCAM had the highest percentage of informative regions in the FA maps (62% for FIBRO and 71% for ROTOR) that coincided with the successful RFCA lesions known from the 2D LA simulations, but unseen by the DL model. Moreover, GradCAM had the smallest coincidence of informative regions of the FA maps with non-arrhythmogenic regions (25% for FIBRO and 27% for ROTOR).Conclusion: The most informative regions of the FA maps coincided with pro-arrhythmogenic regions, suggesting that the DL model leveraged structural features of MRI images to identify such regions and make its prediction. In the future, this technique could provide a clinician with a trustworthy decision support tool.
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Tan W, Wang K, Yang X, Wang K, Wang N, Jiang TB. LncRNA HOTAIR promotes myocardial fibrosis in atrial fibrillation through binding with PTBP1 to increase the stability of Wnt5a. Int J Cardiol 2022; 369:21-28. [PMID: 35787431 DOI: 10.1016/j.ijcard.2022.06.073] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 06/02/2022] [Accepted: 06/29/2022] [Indexed: 11/25/2022]
Abstract
BACKGROUND Atrial fibrillation (AF) is one of the most common arrhythmia in clinical practice, and atrial fibrosis is the important mediator in AF. LncRNA HOTAIR was reported to be up-regulated in AF, while the underlying mechanism of HOTAIR in AF remains unclear. METHODS In vitro and in vivo AF model was established. qRT-PCR and Western blotting were used to assess the mRNA expression (HOTAIR, Wnt5a and PTBP1) and protein levels (Wnt5a, collagen I/III, α-SMA, CTGF, p-ERK, ERK, p-JNK, and JNK), respectively. MTT, CCK8, transwell assay was used to test cell viability, proliferation and migration, respectively. RIP assay assessed the correlation among HOTAIR, PTBP1 and Wnt5a. The level of α-SMA was detected by immunofluorescence. HE and Masson staining detected the histological changes and fibrosis in mouse heart tissues. RESULTS Ang II significantly increased the viability of atrial fibroblasts. The levels of HOTAIR and Wnt5a in fibroblasts were up-regulated by Ang II. HOTAIR silencing or Wnt5a significantly inhibited Ang II-induced proliferation, migration and fibrosis in fibroblasts. HOTAIR silencing repressed Wnt5a-mediated ERK and JNK signaling pathway, and Wnt5a partially abolished the effect of HOTAIR silencing on cell proliferation, migration and fibrosis. Meanwhile, HOTAIR could increase the mRNA stability of Wnt5a via recruiting PTBP1. Furthermore, HOTAIR knockdown notably inhibited the fibrosis in heart tissues of AF mice via regulation of Wnt signaling. CONCLUSION HOTAIR could promote atrial fibrosis in AF through binding with PTBP1 to increase Wnt5a stability. Our study might shed new insights on exploring new strategies against AF.
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Affiliation(s)
- Wei Tan
- Department of Cardiovascular, The First Affiliated Hospital of Soochow University, Suzhou 215000, Jiangsu Province, PR China; Department of Cardiovascular, Suqian First Hospital, Suqian 223800, Jiangsu Province, PR China
| | - Kun Wang
- Department of Thoracic and Cardiac Surgery, Suqian First Hospital, Suqian 223800, Jiangsu Province, PR China
| | - Xue Yang
- Department of Cardiovascular, Suqian First Hospital, Suqian 223800, Jiangsu Province, PR China
| | - Kun Wang
- Department of Cardiovascular, Suqian First Hospital, Suqian 223800, Jiangsu Province, PR China
| | - Ning Wang
- Department of Cardiovascular, Suqian First Hospital, Suqian 223800, Jiangsu Province, PR China
| | - Ting-Bo Jiang
- Department of Cardiovascular, The First Affiliated Hospital of Soochow University, Suzhou 215000, Jiangsu Province, PR China.
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Interpretable machine learning of action potential duration restitution kinetics in single-cell models of atrial cardiomyocytes. J Electrocardiol 2022; 74:137-145. [PMID: 36223672 DOI: 10.1016/j.jelectrocard.2022.09.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 08/28/2022] [Accepted: 09/19/2022] [Indexed: 12/13/2022]
Abstract
Action potential duration (APD) restitution curve and its maximal slope (Smax) reflect single cell-level dynamic instability for inducing chaotic heart rhythms. However, conventional parameter sensitivity analysis often fails to describe nonlinear relationships between ion channel parameters and electrophysiological phenotypes, such as Smax. We explored the parameter-phenotype mapping in a population of 5000 single-cell atrial cell models through interpretable machine learning (ML) approaches. Parameter sensitivity analyses could explain the linear relationships between parameters and electrophysiological phenotypes, including APD90, resting membrane potential, Vmax, refractory period, and APD/calcium alternans threshold, but not for Smax. However, neural network models had better prediction performance for Smax. To interpret the ML model, we evaluated the parameter importance at the global and local levels by computing the permutation feature importance and the local interpretable model-agnostic explanations (LIME) values, respectively. Increases in ICaL, INCX, and IKr, and decreases in IK1, Ib,Cl, IKur, ISERCA, and Ito are correlated with higher Smax values. The LIME algorithm determined that INaK plays a significant role in determining Smax as well as Ito and IKur. The atrial cardiomyocyte population was hierarchically clustered into three distinct groups based on the LIME values and the single-cell simulation confirmed that perturbations in INaK resulted in different behaviors of APD restitution curves in three clusters. Our combined top-down interpretable ML and bottom-up mechanistic simulation approaches uncovered the role of INaK in heterogeneous behaviors of Smax in the atrial cardiomyocyte population.
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Sanchez de la Nava AM, Arenal Á, Fernández-Avilés F, Atienza F. Artificial Intelligence-Driven Algorithm for Drug Effect Prediction on Atrial Fibrillation: An in silico Population of Models Approach. Front Physiol 2021; 12:768468. [PMID: 34938202 PMCID: PMC8685526 DOI: 10.3389/fphys.2021.768468] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 10/27/2021] [Indexed: 12/12/2022] Open
Abstract
Background: Antiarrhythmic drugs are the first-line treatment for atrial fibrillation (AF), but their effect is highly dependent on the characteristics of the patient. Moreover, anatomical variability, and specifically atrial size, have also a strong influence on AF recurrence. Objective: We performed a proof-of-concept study using artificial intelligence (AI) that enabled us to identify proarrhythmic profiles based on pattern identification from in silico simulations. Methods: A population of models consisting of 127 electrophysiological profiles with a variation of nine electrophysiological variables (G Na , I NaK , G K1, G CaL , G Kur , I KCa , [Na] ext , and [K] ext and diffusion) was simulated using the Koivumaki atrial model on square planes corresponding to a normal (16 cm2) and dilated (22.5 cm2) atrium. The simple pore channel equation was used for drug implementation including three drugs (isoproterenol, flecainide, and verapamil). We analyzed the effect of every ionic channel combination to evaluate arrhythmia induction. A Random Forest algorithm was trained using the population of models and AF inducibility as input and output, respectively. The algorithm was trained with 80% of the data (N = 832) and 20% of the data was used for testing with a k-fold cross-validation (k = 5). Results: We found two electrophysiological patterns derived from the AI algorithm that was associated with proarrhythmic behavior in most of the profiles, where G K1 was identified as the most important current for classifying the proarrhythmicity of a given profile. Additionally, we found different effects of the drugs depending on the electrophysiological profile and a higher tendency of the dilated tissue to fibrillate (Small tissue: 80 profiles vs Dilated tissue: 87 profiles). Conclusion: Artificial intelligence algorithms appear as a novel tool for electrophysiological pattern identification and analysis of the effect of antiarrhythmic drugs on a heterogeneous population of patients with AF.
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Affiliation(s)
- Ana Maria Sanchez de la Nava
- Department of Cardiology, Hospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón (IISGM), Madrid, Spain.,Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares (CIBERCV), Madrid, Spain.,ITACA Institute, Universitat Politécnica de València, València, Spain
| | - Ángel Arenal
- Department of Cardiology, Hospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón (IISGM), Madrid, Spain.,Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares (CIBERCV), Madrid, Spain.,Facultad de Medicina, Universidad Complutense de Madrid, Madrid, Spain
| | - Francisco Fernández-Avilés
- Department of Cardiology, Hospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón (IISGM), Madrid, Spain.,Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares (CIBERCV), Madrid, Spain.,Facultad de Medicina, Universidad Complutense de Madrid, Madrid, Spain
| | - Felipe Atienza
- Department of Cardiology, Hospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón (IISGM), Madrid, Spain.,Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares (CIBERCV), Madrid, Spain.,Facultad de Medicina, Universidad Complutense de Madrid, Madrid, Spain
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Muizniece L, Bertagnoli A, Qureshi A, Zeidan A, Roy A, Muffoletto M, Aslanidi O. Reinforcement Learning to Improve Image-Guidance of Ablation Therapy for Atrial Fibrillation. Front Physiol 2021; 12:733139. [PMID: 34512401 PMCID: PMC8424004 DOI: 10.3389/fphys.2021.733139] [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: 06/29/2021] [Accepted: 08/03/2021] [Indexed: 11/29/2022] Open
Abstract
Atrial fibrillation (AF) is the most common cardiac arrhythmia and currently affects more than 650,000 people in the United Kingdom alone. Catheter ablation (CA) is the only AF treatment with a long-term curative effect as it involves destroying arrhythmogenic tissue in the atria. However, its success rate is suboptimal, approximately 50% after a 2-year follow-up, and this high AF recurrence rate warrants significant improvements. Image-guidance of CA procedures have shown clinical promise, enabling the identification of key patient anatomical and pathological (such as fibrosis) features of atrial tissue, which require ablation. However, the latter approach still suffers from a lack of functional information and the need to interpret structures in the images by a clinician. Deep learning plays an increasingly important role in biomedicine, facilitating efficient diagnosis and treatment of clinical problems. This study applies deep reinforcement learning in combination with patient imaging (to provide structural information of the atria) and image-based modelling (to provide functional information) to design patient-specific CA strategies to guide clinicians and improve treatment success rates. To achieve this, patient-specific 2D left atrial (LA) models were derived from late-gadolinium enhancement (LGE) MRI scans of AF patients and were used to simulate patient-specific AF scenarios. Then a reinforcement Q-learning algorithm was created, where an ablating agent moved around the 2D LA, applying CA lesions to terminate AF and learning through feedback imposed by a reward policy. The agent achieved 84% success rate in terminating AF during training and 72% success rate in testing. Finally, AF recurrence rate was measured by attempting to re-initiate AF in the 2D atrial models after CA with 11% recurrence showing a great improvement on the existing therapies. Thus, reinforcement Q-learning algorithms can predict successful CA strategies from patient MRI data and help to improve the patient-specific guidance of CA therapy.
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Affiliation(s)
- Laila Muizniece
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Adrian Bertagnoli
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom.,Department of Biomedical Engineering, ETH Zürich, Zürich, Switzerland
| | - Ahmed Qureshi
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Aya Zeidan
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Aditi Roy
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom.,Department of Computer Science, University of Oxford, Oxford, United Kingdom
| | - Marica Muffoletto
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Oleg Aslanidi
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
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Halfar R, Lawson BAJ, Dos Santos RW, Burrage K. Machine Learning Identification of Pro-arrhythmic Structures in Cardiac Fibrosis. Front Physiol 2021; 12:709485. [PMID: 34483962 PMCID: PMC8415115 DOI: 10.3389/fphys.2021.709485] [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: 05/14/2021] [Accepted: 06/30/2021] [Indexed: 12/17/2022] Open
Abstract
Cardiac fibrosis and other scarring of the heart, arising from conditions ranging from myocardial infarction to ageing, promotes dangerous arrhythmias by blocking the healthy propagation of cardiac excitation. Owing to the complexity of the dynamics of electrical signalling in the heart, however, the connection between different arrangements of blockage and various arrhythmic consequences remains poorly understood. Where a mechanism defies traditional understanding, machine learning can be invaluable for enabling accurate prediction of quantities of interest (measures of arrhythmic risk) in terms of predictor variables (such as the arrangement or pattern of obstructive scarring). In this study, we simulate the propagation of the action potential (AP) in tissue affected by fibrotic changes and hence detect sites that initiate re-entrant activation patterns. By separately considering multiple different stimulus regimes, we directly observe and quantify the sensitivity of re-entry formation to activation sequence in the fibrotic region. Then, by extracting the fibrotic structures around locations that both do and do not initiate re-entries, we use neural networks to determine to what extent re-entry initiation is predictable, and over what spatial scale conduction heterogeneities appear to act to produce this effect. We find that structural information within about 0.5 mm of a given point is sufficient to predict structures that initiate re-entry with more than 90% accuracy.
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Affiliation(s)
- Radek Halfar
- IT4Innovations, VSB-Technical University of Ostrava, Ostrava, Czechia
| | - Brodie A J Lawson
- Centre for Data Science, School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD, Australia.,ARC Centre of Excellence for Mathematical and Statistical Frontiers, School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD, Australia
| | - Rodrigo Weber Dos Santos
- Graduate Program in Computational Modeling, Universidade Federal de Juiz de Fora, Juiz de Fora, Brazil
| | - 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
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