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For: Niederer SA, Aboelkassem Y, Cantwell CD, Corrado C, Coveney S, Cherry EM, Delhaas T, Fenton FH, Panfilov AV, Pathmanathan P, Plank G, Riabiz M, Roney CH, Dos Santos RW, Wang L. Creation and application of virtual patient cohorts of heart models. Philos Trans A Math Phys Eng Sci 2020;378:20190558. [PMID: 32448064 DOI: 10.1098/rsta.2019.0558] [Cited by in Crossref: 24] [Cited by in F6Publishing: 28] [Article Influence: 12.0] [Reference Citation Analysis]
Number Citing Articles
1 Lei CL, Clerx M, Gavaghan DJ, Mirams GR. Model-driven optimal experimental design for calibrating cardiac electrophysiology models.. [DOI: 10.1101/2022.11.01.514669] [Reference Citation Analysis]
2 Ryu D, Baek S, Kim J. Region-dependent mechanical characterization of porcine thoracic aorta with a one-to-many correspondence method to create virtual datasets using uniaxial tensile tests. Front Bioeng Biotechnol 2022;10:937326. [DOI: 10.3389/fbioe.2022.937326] [Reference Citation Analysis]
3 Gillette K, Gsell MAF, Strocchi M, Grandits T, Neic A, Manninger M, Scherr D, Roney CH, Prassl AJ, Augustin CM, Vigmond EJ, Plank G. A personalized real-time virtual model of whole heart electrophysiology. Front Physiol 2022;13:907190. [DOI: 10.3389/fphys.2022.907190] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
4 Rappel W. The physics of heart rhythm disorders. Physics Reports 2022;978:1-45. [DOI: 10.1016/j.physrep.2022.06.003] [Reference Citation Analysis]
5 de Lepper AGW, Buck CMA, van 't Veer M, Huberts W, van de Vosse FN, Dekker LRC. From evidence-based medicine to digital twin technology for predicting ventricular tachycardia in ischaemic cardiomyopathy. J R Soc Interface 2022;19:20220317. [PMID: 36128708 DOI: 10.1098/rsif.2022.0317] [Reference Citation Analysis]
6 Coorey G, Figtree GA, Fletcher DF, Snelson VJ, Vernon ST, Winlaw D, Grieve SM, McEwan A, Yang JYH, Qian P, O'Brien K, Orchard J, Kim J, Patel S, Redfern J. The health digital twin to tackle cardiovascular disease-a review of an emerging interdisciplinary field. NPJ Digit Med 2022;5:126. [PMID: 36028526 DOI: 10.1038/s41746-022-00640-7] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
7 Beetz M, Banerjee A, Grau V. Multi-Domain Variational Autoencoders for Combined Modeling of MRI-Based Biventricular Anatomy and ECG-Based Cardiac Electrophysiology. Front Physiol 2022;13:886723. [PMID: 35755443 DOI: 10.3389/fphys.2022.886723] [Reference Citation Analysis]
8 Zieliński K, Gólczewski T, Kozarski M, Darowski M. Virtual and Artificial Cardiorespiratory Patients in Medicine and Biomedical Engineering. Membranes 2022;12:548. [DOI: 10.3390/membranes12060548] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
9 Gaio ED, Rocha BM, dos Santos RW. Modeling Contrast Perfusion and Adsorption Phenomena in the Human Left Ventricle. Computational Science – ICCS 2022 2022. [DOI: 10.1007/978-3-031-08754-7_52] [Reference Citation Analysis]
10 Jiang X, Li Z, Missel R, Zaman MS, Zenger B, Good WW, Macleod RS, Sapp JL, Wang L. Few-Shot Generation of Personalized Neural Surrogates for Cardiac Simulation via Bayesian Meta-learning. Lecture Notes in Computer Science 2022. [DOI: 10.1007/978-3-031-16452-1_5] [Reference Citation Analysis]
11 Mastrostefano E, Stolfi P, Castiglione F. A data-driven model for the generation of Virtual Cohorts. 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 2021. [DOI: 10.1109/bibm52615.2021.9669283] [Reference Citation Analysis]
12 Augustin CM, Gsell MAF, Karabelas E, Willemen E, Prinzen FW, Lumens J, Vigmond EJ, Plank G. A computationally efficient physiologically comprehensive 3D-0D closed-loop model of the heart and circulation. Comput Methods Appl Mech Eng 2021;386:114092. [PMID: 34630765 DOI: 10.1016/j.cma.2021.114092] [Cited by in Crossref: 9] [Cited by in F6Publishing: 7] [Article Influence: 9.0] [Reference Citation Analysis]
13 Rodero C, Strocchi M, Lee AWC, Rinaldi CA, Vigmond EJ, Plank G, Lamata P, Niederer SA. Impact of anatomical reverse remodelling in the design of optimal quadripolar pacing leads: A computational study. Comput Biol Med 2021;140:105073. [PMID: 34852973 DOI: 10.1016/j.compbiomed.2021.105073] [Reference Citation Analysis]
14 Zaman MS, Dhamala J, Bajracharya P, Sapp JL, Horácek BM, Wu KC, Trayanova NA, Wang L. Fast Posterior Estimation of Cardiac Electrophysiological Model Parameters via Bayesian Active Learning. Front Physiol 2021;12:740306. [PMID: 34759835 DOI: 10.3389/fphys.2021.740306] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
15 Fresca S, Manzoni A, Dedè L, Quarteroni A. POD-Enhanced Deep Learning-Based Reduced Order Models for the Real-Time Simulation of Cardiac Electrophysiology in the Left Atrium. Front Physiol 2021;12:679076. [PMID: 34630131 DOI: 10.3389/fphys.2021.679076] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
16 Romero P, Lozano M, Martínez-Gil F, Serra D, Sebastián R, Lamata P, García-Fernández I. Clinically-Driven Virtual Patient Cohorts Generation: An Application to Aorta. Front Physiol 2021;12:713118. [PMID: 34539438 DOI: 10.3389/fphys.2021.713118] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
17 Dössel O, Luongo G, Nagel C, Loewe A. Computer Modeling of the Heart for ECG Interpretation—A Review. Hearts 2021;2:350-68. [DOI: 10.3390/hearts2030028] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 5.0] [Reference Citation Analysis]
18 Muniz-Terrera G, Mendelevitch O, Barnes R, Lesh MD. Virtual Cohorts and Synthetic Data in Dementia: An Illustration of Their Potential to Advance Research. Front Artif Intell 2021;4:613956. [PMID: 34079930 DOI: 10.3389/frai.2021.613956] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
19 Pagani S, Manzoni A. Enabling forward uncertainty quantification and sensitivity analysis in cardiac electrophysiology by reduced order modeling and machine learning. Int J Numer Method Biomed Eng 2021;37:e3450. [PMID: 33599106 DOI: 10.1002/cnm.3450] [Cited by in Crossref: 7] [Cited by in F6Publishing: 8] [Article Influence: 7.0] [Reference Citation Analysis]
20 Voigt I, Inojosa H, Dillenseger A, Haase R, Akgün K, Ziemssen T. Digital Twins for Multiple Sclerosis. Front Immunol 2021;12:669811. [PMID: 34012452 DOI: 10.3389/fimmu.2021.669811] [Cited by in Crossref: 30] [Cited by in F6Publishing: 32] [Article Influence: 30.0] [Reference Citation Analysis]
21 Rodero C, Strocchi M, Marciniak M, Longobardi S, Whitaker J, O'Neill MD, Gillette K, Augustin C, Plank G, Vigmond EJ, Lamata P, Niederer SA. Linking statistical shape models and simulated function in the healthy adult human heart. PLoS Comput Biol 2021;17:e1008851. [PMID: 33857152 DOI: 10.1371/journal.pcbi.1008851] [Cited by in Crossref: 19] [Cited by in F6Publishing: 22] [Article Influence: 19.0] [Reference Citation Analysis]
22 Plank G, Loewe A, Neic A, Augustin C, Huang Y, Gsell MAF, Karabelas E, Nothstein M, Prassl AJ, Sánchez J, Seemann G, Vigmond EJ. The openCARP Simulation Environment for Cardiac Electrophysiology.. [DOI: 10.1101/2021.03.01.433036] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
23 Wang H, Ma H, Sové RJ, Emens LA, Popel AS. Quantitative systems pharmacology model predictions for efficacy of atezolizumab and nab-paclitaxel in triple-negative breast cancer. J Immunother Cancer 2021;9:e002100. [PMID: 33579739 DOI: 10.1136/jitc-2020-002100] [Cited by in Crossref: 11] [Cited by in F6Publishing: 11] [Article Influence: 11.0] [Reference Citation Analysis]
24 Fedele M, Quarteroni A. Polygonal surface processing and mesh generation tools for the numerical simulation of the cardiac function. Int J Numer Method Biomed Eng 2021;37:e3435. [PMID: 33415829 DOI: 10.1002/cnm.3435] [Cited by in Crossref: 11] [Cited by in F6Publishing: 14] [Article Influence: 11.0] [Reference Citation Analysis]
25 Ramírez WA, Gizzi A, Sack KL, Filippi S, Guccione JM, Hurtado DE. On the Role of Ionic Modeling on the Signature of Cardiac Arrhythmias for Healthy and Diseased Hearts. Mathematics 2020;8:2242. [DOI: 10.3390/math8122242] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 3.0] [Reference Citation Analysis]
26 Fresca S, Manzoni A, Dedè L, Quarteroni A. Deep learning-based reduced order models in cardiac electrophysiology. PLoS One 2020;15:e0239416. [PMID: 33002014 DOI: 10.1371/journal.pone.0239416] [Cited by in Crossref: 22] [Cited by in F6Publishing: 23] [Article Influence: 11.0] [Reference Citation Analysis]
27 Mirams GR, Niederer SA, Clayton RH. The fickle heart: uncertainty quantification in cardiac and cardiovascular modelling and simulation. Philos Trans A Math Phys Eng Sci 2020;378:20200119. [PMID: 32448073 DOI: 10.1098/rsta.2020.0119] [Cited by in Crossref: 8] [Cited by in F6Publishing: 9] [Article Influence: 4.0] [Reference Citation Analysis]