1. Banduc, T., Azzolin, L., Manninger, M., Scherr, D., Plank, G., Pezzuto, S., and Sahli Costabal, F.,
Simulation-free prediction of atrial fibrillation inducibility with the fibrotic kernel signature. Medical Image Analysis 2025;99:103375. doi:
10.1016/j.media.2024.103375.
2. Biasi, N., Seghetti, P., Parollo, M., Zucchelli, G., and Tognetti, A.,
A matlab toolbox for cardiac electrophysiology simulations on patient-specific geometries. Computers in Biology and Medicine 2025;185:109529. doi:
10.1016/j.compbiomed.2024.109529.
3. Jones, C. E., and Oomen, P. J. A.,
Synergistic biophysics and machine learning modeling to rapidly predict cardiac growth probability. Computers in Biology and Medicine 2025;184:109323. doi:
10.1016/j.compbiomed.2024.109323.
4. Alba, V., Aumage, O., Barthou, D., Colin, R., Counilh, M.-C., Genaud, S., Guermouche, A., Loechner, V., and Thangamani, A.,
Performance portability of generated cardiac simulation kernels through automatic dimensioning and load balancing on heterogeneous nodes. 2024 in
2024 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) (IEEE), 1006–1015. doi:
10.1109/ipdpsw63119.2024.00171.
5. Dake, P. G., Mukherjee, J., Sahu, K. C., and Pandit, A. B.,
Computational fluid dynamics in cardiovascular engineering: A comprehensive review. Transactions of the Indian National Academy of Engineering 2024;9:335–362. doi:
10.1007/s41403-024-00478-3.
6. Kabus, D., Cloet, M., Zemlin, C., Bernus, O., and Dierckx, H.,
The ithildin library for efficient numerical solution of anisotropic reaction-diffusion problems in excitable media. 2024; doi:
10.1101/2024.05.01.592026.
7. Martinez Diaz, P., Sanchez, J., Fitzen, N. A., Ravens, U., Doessel, O., and Loewe, A.,
The right atrium affects in silico arrhythmia vulnerability in both atria. Europace 2024;26: doi:
10.1093/europace/euae102.586.
8. Abrasheva, V. O., Kovalenko, S. G., Slotvitsky, M., Romanova, S. А., Aitova, A. A., Frolova, S., Tsvelaya, V., and Syunyaev, R. A.,
Human sodium current voltage‐dependence at physiological temperature measured by coupling a patch‐clamp experiment to a mathematical model. The Journal of Physiology 2024;602:633–661. doi:
10.1113/jp285162.
9. Arnold, R., Prassl, A. J., Neic, A., Thaler, F., Augustin, C. M., Gsell, M. A. F., Gillette, K., Manninger, M., Scherr, D., and Plank, G.,
pyCEPS: A cross-platform electroanatomic mapping data to computational model conversion platform for the calibration of digital twin models of cardiac electrophysiology. Computer Methods and Programs in Biomedicine 2024;254:108299. doi:
10.1016/j.cmpb.2024.108299.
10. Barnafi, N. A., Huynh, N. M. M., Pavarino, L. F., and Scacchi, S.,
Robust parallel nonlinear solvers for implicit time discretizations of the bidomain equations with staggered ionic models. Computers & Mathematics with Applications 2024;167:134–149. doi:
10.1016/j.camwa.2024.04.014.
11. Berndt, A., Lee, J., Nguyen, A., Jin, Z., Moghadasi, A., Gibbs, C., Wait, S., Evitts, K., Asencio, A., Bremner, S., Zuniga, S., Chavan, V., Williams, A., Smith, A., et al.,
Far-red and sensitive sensor for monitoring real time H2O2 dynamics with subcellular resolution and in multi-parametric imaging applications. 2024; doi:
10.21203/rs.3.rs-3974015/v1.
12. Bertrand, A., Yamamoto, C., Monopoli, G., Schrotter, T., Myklebust, L., Uv, J. J., Arevalo, H. J., and Maleckar, M. M.,
“Augmentation of cardiac ischemic geometry for improving machine learning performance in arrhythmic risk stratification,” 2024 in
Computational Physiology (Springer Nature Switzerland), 39–53. doi:
10.1007/978-3-031-53145-3_3.
13. Brown, A. L., Sexton, Z. A., Hu, Z., Yang, W., and Marsden, A. L.,
“Computational approaches for mechanobiology in cardiovascular development and diseases,” 2024 in
Heart Development and Disease (Elsevier), 19–50. doi:
10.1016/bs.ctdb.2024.01.006.
14. Cluitmans, M. J. M., Plank, G., and Heijman, J.,
Digital twins for cardiac electrophysiology: State of the art and future challenges. Herzschrittmachertherapie + Elektrophysiologie 2024;35:118–123. doi:
10.1007/s00399-024-01014-0.
15. Dharmaprani, D., Tiver, K., Salari Shahrbabaki, S., Jenkins, E. V., Chapman, D., Strong, C., Quah, J. X., Tonchev, I., O’Loughlin, L., Mitchell, L., Tung, M., Ahmad, W., Stoyanov, N., Aguilar, M., et al.,
Observable atrial and ventricular fibrillation episode durations are conformant with a power law based on system size and spatial synchronization. Circulation: Arrhythmia and Electrophysiology 2024;17: doi:
10.1161/circep.123.012684.
16. Fehrentz, T., Amin, E., Görldt, N., Strasdeit, T., Moussavi‐Torshizi, S., Leippe, P., Trauner, D., Meyer, C., Frey, N., Sasse, P., and Klöcker, N.,
Optical control of cardiac electrophysiology by the photochromic ligand azobupivacaine 2. British Journal of Pharmacology 2024; doi:
10.1111/bph.17394.
17. Finsberg, H., and Hake, J.,
Gotranx: General ODE translator. Journal of Open Source Software 2024;9:7063. doi:
10.21105/joss.07063.
18. Göbel, F., Cojean, T., and Anzt, H.,
“BDDC preconditioning on GPUs for cardiac simulations,” 2024 in
Euro-Par 2023: Parallel Processing Workshops (Springer Nature Switzerland), 265–268. doi:
10.1007/978-3-031-48803-0_30.
19. Gonzalo, A., Augustin, C. M., Bifulco, S. F., Telle, Å., Chahine, Y., Kassar, A., Guerrero‐Hurtado, M., Durán, E., Martínez‐Legazpi, P., Flores, O., Bermejo, J., Plank, G., Akoum, N., Boyle, P. M., et al.,
Multiphysics simulations reveal haemodynamic impacts of patient‐derived fibrosis‐related changes in left atrial tissue mechanics. The Journal of Physiology 2024; doi:
10.1113/jp287011.
20. Gsell, M. A. F., Neic, A., Bishop, M. J., Gillette, K., Prassl, A. J., Augustin, C. M., Vigmond, E. J., and Plank, G.,
ForCEPSS—a framework for cardiac electrophysiology simulations standardization. Computer Methods and Programs in Biomedicine 2024;251:108189. doi:
10.1016/j.cmpb.2024.108189.
21. Hirschvogel, M.,
Ambit – a FEniCS-based cardiovascular multi-physics solver. Journal of Open Source Software 2024;9:5744. doi:
10.21105/joss.05744.
22. Huynh, N. M. M.,
Convergence analysis for virtual element discretizations of the cardiac bidomain model. Journal of Scientific Computing 2024;98: doi:
10.1007/s10915-023-02435-8.
23. Joshua Frederic, S., Fatemeh, C., Tomas, S., Mark, P., Martin, W., and Axel, L.,
Electrograms in a cardiac cell-by-cell model. 2024; doi:
10.47952/gro-publ-194.
24. Langen, J. S., Boyle, P. M., Malan, D., and Sasse, P.,
Optogenetic quantification of source sink relationship in intact hearts to explain cardiac arrhythmia initiation and protection. 2024; doi:
10.1101/2024.08.14.604123.
25. Linder, M., Stary, T., Bitay, G., Nagy, N., and Loewe, A.,
Sympathetic stimulation can compensate for hypocalcaemia-induced bradycardia in human and rabbit sinoatrial node cells. 2024; doi:
10.1101/2024.08.30.610432.
26. Lootens, S., Janssens, I., Van Den Abeele, R., Wülfers, E. M., Bezerra, A. S., Verstraeten, B., Hendrickx, S., Okenov, A., Nezlobinsky, T., Panfilov, A. V., and Vandersickel, N.,
Directed graph mapping exceeds phase mapping for the detection of simulated 2D meandering rotors in fibrotic tissue with added noise. Computers in Biology and Medicine 2024;171:108138. doi:
10.1016/j.compbiomed.2024.108138.
27. Magtibay, K., Massé, S., Nanthakumar, K., and Umapathy, K.,
Effects of spatially dense adrenergic stimulation to rotor behaviour in simulated atrial sheets. Computers in Biology and Medicine 2024;182:109195. doi:
10.1016/j.compbiomed.2024.109195.
28. Maier, B., Göddeke, D., Huber, F., Klotz, T., Röhrle, O., and Schulte, M.,
OpenDiHu: An efficient and scalable framework for biophysical simulations of the neuromuscular system. Journal of Computational Science 2024;79:102291. doi:
10.1016/j.jocs.2024.102291.
29. Marins Ramalho de Lima, L., Rocha Ribeiro, R., Arantes Berg, L., Martins Rocha, B., Sachetto Oliveira, R., Weber dos Santos, R., and Oliveira Campos, J. de,
“MonoWeb: Cardiac electrophysiology web simulator,” 2024 in
Computational Science – ICCS 2024 (Springer Nature Switzerland), 147–154. doi:
10.1007/978-3-031-63772-8_14.
30. Martínez Díaz, P., Dasí, A., Goetz, C., Unger, L. A., Haas, A., Luik, A., Rodríguez, B., Dössel, O., and Loewe, A.,
Impact of effective refractory period personalization on arrhythmia vulnerability in patient-specific atrial computer models. Europace 2024;26: doi:
10.1093/europace/euae215.
31. Meisenzahl, C., Gillette, K., Prassl, A. J., Plank, G., Sapp, J. L., and Wang, L.,
BOATMAP: Bayesian optimization active targeting for monomorphic arrhythmia pace-mapping. Computers in Biology and Medicine 2024;182:109201. doi:
10.1016/j.compbiomed.2024.109201.
32. Myklebust, L., Maleckar, M. M., and Arevalo, H.,
Fibrosis modeling choice affects morphology of ventricular arrhythmia in non-ischemic cardiomyopathy. Frontiers in Physiology 2024a;15: doi:
10.3389/fphys.2024.1370795.
33. Myklebust, L., Monopoli, G., Balaban, G., Aabel, E. W., Ribe, M., Castrini, A. I., Hasselberg, N. E., Bugge, C., Five, C., Haugaa, K., Maleckar, M. M., and Arevalo, H.,
Stretch of the papillary insertion triggers reentrant arrhythmia: An in silico patient study. Frontiers in Physiology 2024b;15: doi:
10.3389/fphys.2024.1447938.
34. O’Hara, R. P., Lacy, A., Prakosa, A., Kholmovski, E. G., Maurizi, N., Pruvot, E. J., Teres, C., Antiochos, P., Masi, A., Schwitter, J., and Trayanova, N. A.,
Cardiac MRI oversampling in heart digital twins improves preprocedure ventricular tachycardia identification in postinfarction patients. JACC: Clinical Electrophysiology 2024;10:2035–2048. doi:
10.1016/j.jacep.2024.04.032.
35. Ogbomo-Harmitt, S., Obada, G., Vandersickel, N., King, A. P., and Aslanidi, O.,
“Effects of fibrotic border zone on drivers for atrial fibrillation: An in-silico mechanistic investigation,” 2024 in
Statistical Atlases and Computational Models of the Heart. Regular and CMRxRecon Challenge Papers (Springer Nature Switzerland), 174–185. doi:
10.1007/978-3-031-52448-6_17.
36. Owusu-Mensah, A., Treat, J., Bernardi, J., Pfeiffer, R., Goodrow, R., Tsevi, B., Lam, V., Audette, M., Cordeiro, J. M., and Deo, M.,
Identification and characterization of two novel KCNH2 mutations contributing to long QT syndrome. PLOS ONE 2024;19:e0287206. doi:
10.1371/journal.pone.0287206.
37. Potyagaylo, D., Dam, P. M. van, Kuniewicz, M., Dolega-Dolegowski, D., Pregowska, A., Atkinson, A., Dobrzynski, H., and Proniewska, K.,
Interactive teaching of medical 3D cardiac anatomy: Atrial anatomy enhanced by ECG and 3D visualization. Frontiers in Medicine 2024;11: doi:
10.3389/fmed.2024.1422017.
38. Ruan, C., Zhou, J., Zhang, Z., Li, T., Chen, L., Li, Z., and Chen, Y.,
Numerical simulation progress of whole-heart modeling: A review. Physics of Fluids 2024;36: doi:
10.1063/5.0238853.
39. Safitra, M. F., Lubis, M., Kusumasari, T. F., and Putri, D. P.,
Advancements in artificial intelligence and data science: Models, applications, and challenges. Procedia Computer Science 2024;234:381–388. doi:
10.1016/j.procs.2024.03.018.
40. Sakata, K., Bradley, R. P., Prakosa, A., Yamamoto, C. A. P., Ali, S. Y., Loeffler, S., Tice, B. M., Boyle, P. M., Kholmovski, E. G., Yadav, R., Sinha, S. K., Marine, J. E., Calkins, H., Spragg, D. D., et al.,
Assessing the arrhythmogenic propensity of fibrotic substrate using digital twins to inform a mechanisms-based atrial fibrillation ablation strategy. Nature Cardiovascular Research 2024a;3:857–868. doi:
10.1038/s44161-024-00489-x.
41. Sakata, K., Bradley, R. P., Prakosa, A., Yamamoto, C. A. P., Yusuf Ali, S., Loeffler, S., Kholmovski, E. G., Kumar Sinha, S., Marine, J. E., Calkins, H., Spragg, D. D., and Trayanova, N. A.,
Optimizing the distribution of ablation lesions to prevent postablation atrial tachycardia. JACC: Clinical Electrophysiology 2024b;10:2347–2358. doi:
10.1016/j.jacep.2024.07.002.
42. Trayanova, N. A., Lyon, A., Shade, J., and Heijman, J.,
Computational modeling of cardiac electrophysiology and arrhythmogenesis: Toward clinical translation. Physiological Reviews 2024;104:1265–1333. doi:
10.1152/physrev.00017.2023.
43. Yang, Y.,
eLife assessment: Neural network emulation of the human ventricular cardiomyocyte action potential for more efficient computations in pharmacological studies. 2024; doi:
10.7554/elife.91911.3.sa0.
44. Yin, M., Charon, N., Brody, R., Lu, L., Trayanova, N., and Maggioni, M.,
A scalable framework for learning the geometry-dependent solution operators of partial differential equations. Nature Computational Science 2024; doi:
10.1038/s43588-024-00732-2.
45. Gillette, K., Gsell, M. A. F., Strocchi, M., Grandits, T., Neic, A., Manninger, M., Scherr, D., Roney, C. H., Prassl, A. J., Augustin, C. M., Vigmond, E. J., and Plank, G.,
A personalized real-time virtual model of whole heart electrophysiology. Europace 2023a;25: doi:
10.1093/europace/euad122.541.
46. Pikunov, A. V., Syunyaev, R. A., Ali, R., Prakosa, A., Boyle, P. M., Steckmeister, V., Kutschka, I., Rytkin, E., Voigt, N., Trayanova, N., and Efimov, I. R.,
The role of structuralvscellular remodeling in arrhythmogenesis: Personalized computer models of atrial fibrillation. 2023; doi:
10.1101/2023.05.13.540632.
47. Nagel, C., Espinosa, C. B., Gillette, K., Gsell, M. A. F., Sanchez, J., Plank, G., Dossel, O., and Loewe, A.,
Comparison of propagation models and forward calculation methods on cellular, tissue and organ scale atrial electrophysiology. IEEE Trans. Biomed. Eng. Transactions on Biomedical Engineering 2023;70:511–522. doi:
10.1109/tbme.2022.3196144.
48. Africa, P. C., Piersanti, R., Fedele, M., Dede’, L., and Quarteroni, A.,
Lifex-fiber: An open tool for myofibers generation in cardiac computational models. BMC Bioinformatics 2023a;24: doi:
10.1186/s12859-023-05260-w.
49. Africa, P. C., Piersanti, R., Regazzoni, F., Bucelli, M., Salvador, M., Fedele, M., Pagani, S., Dede’, L., and Quarteroni, A.,
Lifex-ep: A robust and efficient software for cardiac electrophysiology simulations. BMC Bioinformatics 2023b;24: doi:
10.1186/s12859-023-05513-8.
50. Ahmed Jaffery, O., Vidal Horrach, C., J. Lagalante, D., Thomas, G., Slabaugh, G., Melki, L., W. Good, W., and H. Roney, C.,
Subject-specific ablation of pathologic conduction patterns beyond the pulmonary veins: A personalised modelling approach. 2023 in
2023 Computing in Cardiology Conference (CinC) CinC2023. (Computing in Cardiology). doi:
10.22489/cinc.2023.400.
51. Antunes, M. E. T., Campos, F. O., Sandoval, I., Siles, J. G., Uzelac, I., and Salinet, J.,
“Atrial fibrillation mechanisms: A contribution from computational modelling,” 2023 in
IX Latin American Congress on Biomedical Engineering and XXVIII Brazilian Congress on Biomedical Engineering (Springer Nature Switzerland), 139–146. doi:
10.1007/978-3-031-49401-7_14.
52. Azzolin, L., Eichenlaub, M., Nagel, C., Nairn, D., Sánchez, J., Unger, L., Arentz, T., Westermann, D., Dössel, O., Jadidi, A., and Loewe, A.,
AugmentA: Patient-specific augmented atrial model generation tool. Computerized Medical Imaging and Graphics 2023;108:102265. doi:
10.1016/j.compmedimag.2023.102265.
53. Bergquist, J., Lange, M., Zenger, B., Orkild, B., Paccione, E., Kwan, E., Hunt, B., Dong, J., MacLeod, R., Narayan, A., and Ranjan, R.,
Uncertainty quantification of the effect of variable conductivity in ventricular fibrotic regions on ventricular tachycardia. 2023 in
2023 Computing in Cardiology Conference (CinC) CinC2023. (Computing in Cardiology). doi:
10.22489/cinc.2023.141.
54. Biasi, N., Seghetti, P., Tognetti, A., and Vigmond, E.,
Incremental pacing induces sustained reentry in a computational model of brugada syndrome. 2023 in
2023 Computing in Cardiology Conference (CinC) CinC2023. (Computing in Cardiology). doi:
10.22489/cinc.2023.192.
55. Bifulco, S. F., and Boyle, P. M.,
“Computational modeling and simulation of the fibrotic human atria,” 2023 in
Familial Cardiomyopathies (Springer US), 105–115. doi:
10.1007/978-1-0716-3527-8_6.
56. Bifulco, S. F., Macheret, F., Scott, G. D., Akoum, N., and Boyle, P. M.,
Explainable machine learning to predict anchored reentry substrate created by persistent atrial fibrillation ablation in computational models. Journal of the American Heart Association 2023;12: doi:
10.1161/jaha.123.030500.
57. Camargo, M. L. A. de, Bassani, J. W. M., Bassani, R. A., and Silva, R. R. da,
“ForceLAB simulator update: A study on the positive cooperativity of cross-bridge formation in ventricular myocyte,” 2023 in
IX Latin American Congress on Biomedical Engineering and XXVIII Brazilian Congress on Biomedical Engineering (Springer Nature Switzerland), 129–138. doi:
10.1007/978-3-031-49401-7_13.
58. Cao, B., Zhang, N., Fu, Z., Dong, R., Chen, T., Zhang, W., Tong, L., Wang, Z., Ma, M., Song, Z., Pan, F., Bai, J., Wu, Y., Deng, D., et al.,
Studying the influence of finite element mesh size on the accuracy of ventricular tachycardia simulation. Reviews in Cardiovascular Medicine 2023;24:351. doi:
10.31083/j.rcm2412351.
59. Correas, M., Guillem, M. S., and Sánchez, J.,
“Automated generation of purkinje networks in the human heart considering the anatomical variability,” 2023 in
Functional Imaging and Modeling of the Heart (Springer Nature Switzerland), 127–136. doi:
10.1007/978-3-031-35302-4_13.
60. Finsberg, H. N. T., Herck, I. G. M. van, Daversin-Catty, C., Arevalo, H., and Wall, S.,
Simcardems: A FEniCS-based cardiac electro-mechanics solver. Journal of Open Source Software 2023;8:4753. doi:
10.21105/joss.04753.
61. Fitzen, N. A., Martínez Díaz, P., Dössel, O., and Loewe, A.,
Impact of the right atrium on arrhythmia vulnerability. Current Directions in Biomedical Engineering 2023;9:142–145. doi:
10.1515/cdbme-2023-1036.
62. Ghebryal, J., Rodero, C., Barrows, R. K., Strocchi, M., Roney, C. H., Augustin, C. M., Plank, G., and Niederer, S. A.,
“Effect of varying pericardial boundary conditions on whole heart function: A computational study,” 2023 in
Functional Imaging and Modeling of the Heart (Springer Nature Switzerland), 545–554. doi:
10.1007/978-3-031-35302-4_56.
63. Gibbs, C. E., Marchianó, S., Zhang, K., Yang, X., Murry, C. E., and Boyle, P. M.,
Graft–host coupling changes can lead to engraftment arrhythmia: A computational study. The Journal of Physiology 2023;601:2733–2749. doi:
10.1113/jp284244.
64. Gillette, K., Gsell, M. A. F., Nagel, C., Bender, J., Winkler, B., Williams, S. E., Bär, M., Schäffter, T., Dössel, O., Plank, G., and Loewe, A.,
MedalCare-XL: 16,900 healthy and pathological synthetic 12 lead ECGs from electrophysiological simulations. Scientific Data 2023b;10: doi:
10.1038/s41597-023-02416-4.
65. Leenknegt, L., Panfilov, A. V., and Dierckx, H.,
Impact of electrode orientation, myocardial wall thickness, and myofiber direction on intracardiac electrograms: Numerical modeling and analytical solutions. Frontiers in Physiology 2023;14: doi:
10.3389/fphys.2023.1213218.
66. Lei, C. L., Clerx, M., Gavaghan, D. J., and Mirams, G. R.,
Model-driven optimal experimental design for calibrating cardiac electrophysiology models. Computer Methods and Programs in Biomedicine 2023;240:107690. doi:
10.1016/j.cmpb.2023.107690.
67. Lindner, L. P., Gerach, T., Jahnke, T., Loewe, A., Weiss, D., and Wieners, C.,
Efficient time splitting schemes for the monodomain equation in cardiac electrophysiology. International Journal for Numerical Methods in Biomedical Engineering 2023;39: doi:
10.1002/cnm.3666.
68. Lyu, Y., Bennamoun, M., Sharif, N., Lip, G. Y. H., and Dwivedi, G.,
Artificial intelligence in the image-guided care of atrial fibrillation. Life 2023;13:1870. doi:
10.3390/life13091870.
69. Macarulla-Rodriguez, M., Sánchez, J., and Salud Guillem Sánchez, M. de la,
Non-invasive estimation of atrial fibrosis location and density. 2023 in
2023 Computing in Cardiology Conference (CinC) CinC2023. (Computing in Cardiology). doi:
10.22489/cinc.2023.107.
70. Magtibay, K., Massé, S., Nanthakumar, K., and Umapathy, K.,
Pro-arrhythmic role of adrenergic spatial densities in the human atria: An in-silico study. PLOS ONE 2023;18:e0290676. doi:
10.1371/journal.pone.0290676.
71. Martinez Anton, C., Sánchez, J., Caslli, N., Heinemann, L., Anna Unger, L., Loewe, A., and Doessel, O.,
In silico computation of electrograms and local electrical impedance to assess non-transmural fibrosis. 2023 in
2023 Computing in Cardiology Conference (CinC) CinC2023. (Computing in Cardiology). doi:
10.22489/cinc.2023.283.
72. Moinuddin, A., Ali, S. Y., Goel, A., Sethi, Y., Patel, N., Kaka, N., Satapathy, P., Sah, R., Barboza, J. J., and Suhail, M. K.,
The age of computational cardiology and future of long-term ablation target prediction for ventricular tachycardia. Frontiers in Cardiovascular Medicine 2023;10: doi:
10.3389/fcvm.2023.1233991.
73. Moussavi‐Torshizi, S. E., Amin, E., and Klöcker, N.,
Sex‐specific repolarization heterogeneity in mouse left ventricle: Optical mapping combined with mathematical modeling predict the contribution of specific ionic currents. Physiological Reports 2023;11: doi:
10.14814/phy2.15670.
74. Nonlinear analysis and machine learning in cardiology, Frontiers Media SA 2023 doi:
10.3389/978-2-8325-2293-6.
75. Ochs, A. R., and Boyle, P. M.,
Optogenetic modulation of arrhythmia triggers: Proof-of-concept from computational modeling. Cellular and Molecular Bioengineering 2023;16:243–259. doi:
10.1007/s12195-023-00781-z.
76. Orkild, B., Bergquist, J., Paccione, E., Lange, M., Kwan, E., Hunt, B., MacLeod, R., Narayan, A., and Ranjan, R.,
A grid search of fibrosis thresholds for uncertainty quantification in atrial flutter simulations. 2023 in
2023 Computing in Cardiology Conference (CinC) CinC2023. (Computing in Cardiology). doi:
10.22489/cinc.2023.348.
77. Paccione, E., Lange, M., Orkild, B., Bergquist, J., Kwan, E., Hunt, B., Dosdall, D., MacLeod, R., and Ranjan, R.,
Effects of biventricular pacing locations on anti-tachycardia pacing success in a patient-specific model. 2023 in
2023 Computing in Cardiology Conference (CinC) CinC2023. (Computing in Cardiology). doi:
10.22489/cinc.2023.369.
78. Paterson, D. J.,
eLife assessment: Predicting ventricular tachycardia circuits in patients with arrhythmogenic right ventricular cardiomyopathy using genotype-specific heart digital twins. 2023; doi:
10.7554/elife.88865.2.sa3.
79. Petras, A., Gsell, M. A. F., Augustin, C. M., Rodriguez-Padilla, J., Jung, A., Strocchi, M., Prinzen, F. W., Niederer, S. A., Plank, G., and Vigmond, E. J.,
Mechanoelectric effects in healthy cardiac function and under left bundle branch block pathology. Computers in Biology and Medicine 2023;156:106696. doi:
10.1016/j.compbiomed.2023.106696.
80. Pigozzo, R. B., Santos, R. W. dos, and Rocha, B. M.,
“Sensitivity analysis of a cardiac electrophysiology model for the occurrence of electrical alternans,” 2023 in
Computational Science and Its Applications – ICCSA 2023 Workshops (Springer Nature Switzerland), 44–58. doi:
10.1007/978-3-031-37105-9_4.
81. Qian, S., Ugurlu, D., Fairweather, E., Strocchi, M., Toso, L. D., Deng, Y., Plank, G., Vigmond, E., Razavi, R., Young, A., Lamata, P., Bishop, M., and Niederer, S.,
Developing cardiac digital twins at scale: Insights from personalised myocardial conduction velocity. 2023; doi:
10.1101/2023.12.05.23299435.
82. Ramlugun, G. S., Kulkarni, K., Pallares-Lupon, N., Boukens, B. J., Efimov, I. R., Vigmond, E. J., Bernus, O., and Walton, R. D.,
A comprehensive framework for evaluation of high pacing frequency and arrhythmic optical mapping signals. Frontiers in Physiology 2023;14: doi:
10.3389/fphys.2023.734356.
83. Reimer, J., Domínguez-Rivera, S. A., Sundnes, J., and Spiteri, R. J.,
Physiological accuracy in simulating refractory cardiac tissue: The volume-averaged bidomain model vs. The cell-based EMI model. 2023; doi:
10.1101/2023.04.10.536323.
84. Ricci, E., Bartolucci, C., and Severi, S.,
The virtual sinoatrial node: What did computational models tell us about cardiac pacemaking? Progress in Biophysics and Molecular Biology 2023;177:55–79. doi:
10.1016/j.pbiomolbio.2022.10.008.
85. Rinné, S., Oertli, A., Nagel, C., Tomsits, P., Jenewein, T., Kääb, S., Kauferstein, S., Loewe, A., Beckmann, B. M., and Decher, N.,
Functional characterization of a spectrum of novel romano-ward syndrome KCNQ1 variants. International Journal of Molecular Sciences 2023;24:1350. doi:
10.3390/ijms24021350.
86. Romitti, G. S., Liberos, A., Romero, P., Serra, D., García, I., Lozano, M., Sebastian, R., and Rodrigo, M.,
“Cellular automata for fast simulations of arrhythmogenic atrial substrate,” 2023 in
Functional Imaging and Modeling of the Heart (Springer Nature Switzerland), 107–116. doi:
10.1007/978-3-031-35302-4_11.
87. Roney, C. H., Solis Lemus, J. A., Lopez Barrera, C., Zolotarev, A., Ulgen, O., Kerfoot, E., Bevis, L., Misghina, S., Vidal Horrach, C., Jaffery, O. A., Ehnesh, M., Rodero, C., Dharmaprani, D., Ríos-Muñoz, G. R., et al.,
Constructing bilayer and volumetric atrial models at scale. Interface Focus 2023;13: doi:
10.1098/rsfs.2023.0038.
88. Ryzhii, M., and Ryzhii, E.,
A compact multi-functional model of the rabbit atrioventricular node with dual pathways. Frontiers in Physiology 2023;14: doi:
10.3389/fphys.2023.1126648.
89. Schwarz, E. L., Pegolotti, L., Pfaller, M. R., and Marsden, A. L.,
Beyond CFD: Emerging methodologies for predictive simulation in cardiovascular health and disease. Biophysics Reviews 2023;4: doi:
10.1063/5.0109400.
90. Solis-Lemus, J. A., Baptiste, T., Barrows, R., Sillett, C., Gharaviri, A., Raffaele, G., Razeghi, O., Strocchi, M., Sim, I., Kotadia, I., Bodagh, N., O’Hare, D., O’Neill, M., Williams, S. E., et al.,
Model reproducibility study on left atrial fibres. 2023; doi:
10.48550/ARXIV.2301.06998.
91. Solís-Lemus, J. A., Baptiste, T., Barrows, R., Sillett, C., Gharaviri, A., Raffaele, G., Razeghi, O., Strocchi, M., Sim, I., Kotadia, I., Bodagh, N., O’Hare, D., O’Neill, M., Williams, S. E., et al.,
Evaluation of an open-source pipeline to create patient-specific left atrial models: A reproducibility study. Computers in Biology and Medicine 2023;162:107009. doi:
10.1016/j.compbiomed.2023.107009.
92. Steyer, J., Chegini, F., Potse, M., Loewe, A., and Weiser, M.,
Continuity of mircoscopic cardiac conduction in a computational cell-by-cell model. 2023a in
2023 Computing in Cardiology Conference (CinC) CinC2023. (Computing in Cardiology). doi:
10.22489/cinc.2023.385.
93. Steyer, J., Diaz, L. P. M., Unger, L. A., and Loewe, A.,
“Simulated excitation patterns in the atria and their corresponding electrograms,” 2023b in
Functional Imaging and Modeling of the Heart (Springer Nature Switzerland), 204–212. doi:
10.1007/978-3-031-35302-4_21.
94. Sun, Y., Lu, S., Zhao, C., Feng, Y., Chen, W., Xia, L., and Deng, D.,
A fully automated two-stage segmentation approach for late gadolinium-enhanced cardiac magnetic resonance images in personalized cardiac modeling. 2023 in
2023 Computing in Cardiology Conference (CinC) CinC2023. (Computing in Cardiology). doi:
10.22489/cinc.2023.277.
95. Sung, E., Kyranakis, S., Daimee, U. A., Engels, M., Prakosa, A., Zhou, S., Nazarian, S., Zimmerman, S. L., Chrispin, J., and Trayanova, N. A.,
Evaluation of a deep learning‐enabled automated computational heart modelling workflow for personalized assessment of ventricular arrhythmias. The Journal of Physiology 2023;602:4625–4644. doi:
10.1113/jp284125.
96. Thangamani, A., Jost, T. T., Loechner, V., Genaud, S., and Bramas, B.,
Lifting code generation of cardiac physiology simulation to novel compiler technology. 2023 in
Proceedings of the 21st ACM/IEEE International Symposium on Code Generation and Optimization CGO ’23. (ACM), 68–80. doi:
10.1145/3579990.3580008.
97. Trevisan Jost, T., Thangamani, A., Colin, R., Loechner, V., Genaud, S., and Bramas, B.,
“GPU code generation of cardiac electrophysiology simulation with MLIR,” 2023 in
Euro-Par 2023: Parallel Processing (Springer Nature Switzerland), 549–563. doi:
10.1007/978-3-031-39698-4_37.
98. Vidal Horrach, C., Ahmed Jaffery, O., Jay Hunter, R., Honarbakhsh, S., and H. Roney, C.,
Patient-specific atrial fibrillation simulation prediction depend on rhythm used for calibration. 2023 in
2023 Computing in Cardiology Conference (CinC) CinC2023. (Computing in Cardiology). doi:
10.22489/cinc.2023.386.
99. Wickramaratne, S. D., and Parekh, A.,
SleepSIM: Conditional GAN-based non-REM sleep EEG signal generator. 2023 in
2023 45th Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC) (IEEE), 1–4. doi:
10.1109/embc40787.2023.10341043.
100. Willems, R., Kruithof, E., Janssens, K. L. P. M., Cluitmans, M. J. M., Sluis, O. van der, Bovendeerd, P. H. M., and Verhoosel, C. V.,
“Isogeometric-mechanics-driven electrophysiology simulations of ventricular tachycardia,” 2023 in
Functional Imaging and Modeling of the Heart (Springer Nature Switzerland), 97–106. doi:
10.1007/978-3-031-35302-4_10.
101. Coveney, S., Cantwell, C., and Roney, C.,
Atrial conduction velocity mapping: Clinical tools, algorithms and approaches for understanding the arrhythmogenic substrate. Med Biol Eng Comput, Biological Engineering, Computing 2022a;60:2463–2478. doi:
10.1007/s11517-022-02621-0.
102. Karabelas, E., Gsell, M. A. F., Haase, G., Plank, G., and Augustin, C. M.,
An accurate, robust, and efficient finite element framework with applications to anisotropic, nearly and fully incompressible elasticity. Computer Methods in Applied Mechanics and Engineering 2022;394:114887. doi:
10.1016/j.cma.2022.114887.
103. Sánchez, J., and Loewe, A.,
A review of healthy and fibrotic myocardium microstructure modeling and corresponding intracardiac electrograms. Frontiers in Physiology 2022a;13: doi:
10.3389/fphys.2022.908069.
104. Alba, V.,
Load balancing and precision analysis for cardiac simulation M2 Internship report. 2022; Available at:
https://hal.inria.fr/hal-03784546.
105. Alberto Barrios Espinosa, "Cristian., Sánchez, J., Doessel, O., and Loewe", A.,
Diffusion reaction eikonal alternant model: Towards fast simulations of complex cardiac arrhythmias. 2022 in
2022 Computing in Cardiology Conference (CinC) CinC2022. (Computing in Cardiology). doi:
10.22489/cinc.2022.054.
106. Amsaleg, A., Sánchez, J., Mikut, R., and Loewe, A.,
Characterization of the pace-and-drive capacity of the human sinoatrial node: A 3D in silico study. 2022; doi:
10.1101/2022.06.03.494644.
107. Azzolin, L., Eichenlaub, M., Nagel, C., Nairn, D., Sanchez, J., Unger, L., Dössel, O., Jadidi, A., and Loewe, A.,
Personalized ablation vs. Conventional ablation strategies to terminate atrial fibrillation and prevent recurrence. EP Europace 2022;25:211–222. doi:
10.1093/europace/euac116.
108. Bach, F., Klar, J., Loewe, A., Sánchez, J., Seemann, G., Huang, Y.-L., and Ulrich, R.,
The openCARP CDE: Concept for and implementation of a sustainable collaborativedevelopment environment for research software. Bausteine Forschungsdatenmanagement 2022;2022:64–84. doi:
10.17192/bfdm.2022.1.8368.
109. Blackwell, D. J., Faggioni, M., Wleklinski, M. J., Gomez-Hurtado, N., Venkataraman, R., Gibbs, C. E., Baudenbacher, F. J., Gong, S., Fishman, G. I., Boyle, P. M., Pfeifer, K., and Knollmann, B. C.,
The purkinje–myocardial junction is the anatomic origin of ventricular arrhythmia in CPVT. JCI Insight 2022;7: doi:
10.1172/jci.insight.151893.
110. Campos, F. O., Neic, A., Mendonca Costa, C., Whitaker, J., O’Neill, M., Razavi, R., Rinaldi, C. A., DanielScherr, Niederer, S. A., Plank, G., and Bishop, M. J.,
An automated near-real time computational method for induction and treatment of scar-related ventricular tachycardias. Medical Image Analysis 2022;80:102483. doi:
10.1016/j.media.2022.102483.
111. Coveney, S., Roney, C. H., Corrado, C., Wilkinson, R. D., Oakley, J. E., Niederer, S. A., and Clayton, R. H.,
Calibrating cardiac electrophysiology models using latent gaussian processes on atrial manifolds. Scientific Reports 2022b;12: doi:
10.1038/s41598-022-20745-z.
112. Esmailie, F., Razavi, A., Yeats, B., Sivakumar, S. K., Chen, H., Samaee, M., Shah, I. A., Veneziani, A., Yadav, P., Thourani, V. H., and Dasi, L. P.,
Biomechanics of transcatheter aortic valve replacement complications and computational predictive modeling. Structural Heart 2022;6:100032. doi:
10.1016/j.shj.2022.100032.
113. Franco Ocaña, P., Electrophysiological model of the left ventricle: Prediction of reentry circuits with fast simulations based on cellular automata applying clinical stimulation protocols. 2022;
114. Fuchsberger, J., Aigner, P., Niederer, S., Plank, G., Schima, H., Haase, G., and Karabelas, E.,
On the incorporation of obstacles in a fluid flow problem using a navier–stokes–brinkman penalization approach. Journal of Computational Science 2022;57:101506. doi:
10.1016/j.jocs.2021.101506.
115. Geng, Z., Jin, L., Huang, Y., and Wu, X.,
Rate dependence of early afterdepolarizations in the his-purkinje system: A simulation study. Computer Methods and Programs in Biomedicine 2022;217:106665. doi:
10.1016/j.cmpb.2022.106665.
116. Gillette, "Karli., Gsell, M., Kurath-Koller, S., Manninger, M., J. Prassl, A., Scherr, D., and Plank", G.,
Exploring role of accessory pathway location in wolff-parkinson-white syndrome in a model of whole heart electrophysiology. 2022 in
2022 Computing in Cardiology Conference (CinC) CinC2022. (Computing in Cardiology). doi:
10.22489/cinc.2022.057.
117. Goette, A., Rickert, V., and Brandner, S.,
Simulatoren und simulatortraining in der interventionellen elektrophysiologie. Herzschrittmachertherapie + Elektrophysiologie 2022;33:351–354. doi:
10.1007/s00399-022-00882-8.
118. Hustad, K. G., and Cai, X.,
Resource-efficient use of modern processor architectures for numerically solving cardiac ionic cell models. Frontiers in Physiology 2022;13: doi:
10.3389/fphys.2022.904648.
119. Johanne Uv, J., and Arevalo, H.,
Electrophysiological simulation of maternal-fetal ECG on a 3D maternal torso model. 2022 in
2022 Computing in Cardiology Conference (CinC) CinC2022. (Computing in Cardiology). doi:
10.22489/cinc.2022.136.
120. Jung, A., Gsell, M. A. F., Augustin, C. M., and Plank, G.,
An integrated workflow for building digital twins of cardiac electromechanics—a multi-fidelity approach for personalising active mechanics. Mathematics 2022;10:823. doi:
10.3390/math10050823.
121. Klein, V. S., Modeling and measuring cardiac magnetostimulation. 2022;
122. Lange, M., Kwan, E., Dosdall, D. J., MacLeod, R. S., Bunch, T. J., and Ranjan, R.,
Case report: Personalized computational model guided ablation for left atrial flutter. Frontiers in Cardiovascular Medicine 2022;9: doi:
10.3389/fcvm.2022.893752.
123. Lee, C. H., Application of neural networks to predict patient-specific cellular parameters in computational cardiac models. 2022;
124. Loewe, A., Martínez Díaz, P., Nagel, C., and Sánchez, J.,
“Cardiac digital twin modeling,” 2022 in
Innovative Treatment Strategies for Clinical Electrophysiology (Springer Nature Singapore), 111–134. doi:
10.1007/978-981-19-6649-1_7.
125. Martinez Diaz, "Patricia., Sánchez, J., Nagel, C., Martinez Perez, M., Hernández-Romero, I., Salud Guillem Sánchez, M. de la, Doessel, O., and Loewe", A.,
Personalized modeling of atrial activation and p-waves: A comparison between invasive and non-invasive cardiac mapping. 2022 in
2022 Computing in Cardiology Conference (CinC) CinC2022. (Computing in Cardiology). doi:
10.22489/cinc.2022.334.
126. Rappel, W.-J.,
The physics of heart rhythm disorders. 2022; doi:
10.52843/cassyni.598vpx.
127. Reimer, J. A.,
A comparison of the bidomain and EMI models in refractory cardiac tissue. 2022; Available at:
https://hdl.handle.net/10388/14430.
128. Rodero, C., Longobardi, S., Augustin, C., Strocchi, M., Plank, G., Lamata, P., and Niederer, S. A.,
Calibration of cohorts of virtual patient heart models using bayesian history matching. Annals of Biomedical Engineering 2022;51:241–252. doi:
10.1007/s10439-022-03095-9.
129. Rodríguez-Padilla, J., Petras, A., Magat, J., Bayer, J., Bihan-Poudec, Y., El Hamrani, D., Ramlugun, G., Neic, A., Augustin, C. M., Vaillant, F., Constantin, M., Benoist, D., Pourtau, L., Dubes, V., et al.,
Impact of intraventricular septal fiber orientation on cardiac electromechanical function. American Journal of Physiology-Heart and Circulatory Physiology 2022;322:H936–H952. doi:
10.1152/ajpheart.00050.2022.
130. Ryzhii, M., and Ryzhii, E.,
Pacemaking function of two simplified cell models. PLOS ONE 2022;17:e0257935. doi:
10.1371/journal.pone.0257935.
131. Sánchez, J., and Loewe, A., Mechanical consequences of electrical remodeling due to persistent atrial fibrillation: A cellular level sensitivity analysis. 2022b in 49th Computing in Cardiology Conference (CinC).
132. Serra, D., Romero, P., Garcia-Fernandez, I., Lozano, M., Liberos, A., Rodrigo, M., Bueno-Orovio, A., Berruezo, A., and Sebastian, R.,
An automata-based cardiac electrophysiology simulator to assess arrhythmia inducibility. Mathematics 2022;10:1293. doi:
10.3390/math10081293.
133. Sira Romitti, "Giada., Romero de Antonio, P., Liberos Mascarell, A., Serra Almor, D., García Fernandez, I., Lozano Ibañez, M., Sebastian Aguilar, R., and Rodrigo Bort", M.,
Effects of long- and short-term memory on action potential duration for atrial cellular automata. 2022 in
2022 Computing in Cardiology Conference (CinC) CinC2022. (Computing in Cardiology). doi:
10.22489/cinc.2022.258.
134. Skupien, N., Barrios Espinosa, C., Dössel, O., and Loewe, A.,
Refining the eikonal model to reproduce the influence of atrial tissue geometry on conduction velocity. Current Directions in Biomedical Engineering 2022;8:133–136. doi:
10.1515/cdbme-2022-1035.
135. Sutanto, H., and Heijman, J.,
Integrative computational modeling of cardiomyocyte calcium handling and cardiac arrhythmias: Current status and future challenges. Cells 2022;11:1090. doi:
10.3390/cells11071090.
136. Zhu, C., Vedula, V., Parker, D., Wilson, N., Shadden, S., and Marsden, A.,
svFSI: A multiphysics package for integrated cardiac modeling. Journal of Open Source Software 2022;7:4118. doi:
10.21105/joss.04118.
137. Bifulco, S. F., Scott, G. D., Sarairah, S., Birjandian, Z., Roney, C. H., Niederer, S. A., Mahnkopf, C., Kuhnlein, P., Mitlacher, M., Tirschwell, D., Longstreth, W., Akoum, N., and Boyle, P. M.,
Computational modeling identifies embolic stroke of undetermined source patients with potential arrhythmic substrate. eLife 2021;10: doi:
10.7554/elife.64213.
138. Azzolin, L., Nagel, C., Nairn, D., Sanchez, J., Zheng, T., Eichenlaub, M., Jadidi, A., Dossel, O., and Loewe, A.,
Automated framework for the augmentation of missing anatomical structures and generation of personalized atrial models from clinical data. 2021a in
2021 Computing in Cardiology (CinC) (IEEE), 1–4. doi:
10.23919/cinc53138.2021.9662846.
139. Azzolin, L., Schuler, S., Dössel, O., and Loewe, A.,
A reproducible protocol to assess arrhythmia vulnerability in silico: Pacing at the end of the effective refractory period. Frontiers in Physiology 2021b;12: doi:
10.3389/fphys.2021.656411.
140. Beach, M., Sim, I., Mehta, A., Kotadia, I., O’Hare, D., Whitaker, J., Solis-Lemus, J. A., Razeghi, O., Chiribiri, A., O’Neill, M., Williams, S., Niederer, S. A., and Roney, C. H., Using the universal atrial coordinate system for MRI and electroanatomic data registration in patient-specific left atrial model construction and simulation. 2021 in Functional Imaging and Modeling of the Heart, eds. D. B. Ennis, L. E. Perotti, and V. Y. Wang (Cham: Springer International Publishing), 629–638.
141. Coveney, S., Corrado, C., Oakley, J. E., Wilkinson, R. D., Niederer, S. A., and Clayton, R. H.,
Bayesian calibration of electrophysiology models using restitution curve emulators. Frontiers in Physiology 2021;12: doi:
10.3389/fphys.2021.693015.
142. Dıaz, P. M., Azzolin, L., Arciniégas, J. P. S., Nagel, C., Dössel, O., and Loewe, A.,
Influence of the right atrium for arrhythmia vulnerability: Geometry inference using a statistical shape model. 2021; doi:
10.5445/IR/1000138456.
143. Dössel, O., Luongo, G., Nagel, C., and Loewe, A.,
Computer modeling of the heart for ECG interpretation—a review. Hearts 2021;2:350–368. doi:
10.3390/hearts2030028.
144. Espinosa, C. B., Skupien, N., Kachel, G., Dössel, O., and Loewe, A.,
Influence of wave-front and atrial tissue properties on eikonal model simulations. 2021; doi:
10.5445/IR/1000138447.
145. Luongo, G., Azzolin, L., Schuler, S., Rivolta, M. W., Almeida, T. P., Martínez, J. P., Soriano, D. C., Luik, A., Müller-Edenborn, B., Jadidi, A., Dössel, O., Sassi, R., Laguna, P., and Loewe, A.,
Machine learning enables noninvasive prediction of atrial fibrillation driver location and acute pulmonary vein ablation success using the 12-lead ECG. Cardiovascular Digital Health Journal 2021;2:126–136. doi:
10.1016/j.cvdhj.2021.03.002.
146. Maleckar, M. M., Myklebust, L., Uv, J., Florvaag, P. M., Strøm, V., Glinge, C., Jabbari, R., Vejlstrup, N., Engstrøm, T., Ahtarovski, K., Jespersen, T., Tfelt-Hansen, J., Naumova, V., and Arevalo, H.,
Combined in-silico and machine learning approaches toward predicting arrhythmic risk in post-infarction patients. Frontiers in Physiology 2021;12: doi:
10.3389/fphys.2021.745349.
147. Monbaliu, R., A meandering spiral due to early afterdepolarizations as possible mechanism of torsade de pointes. 2021;
148. Moreno, A., Walton, R. D., Bernus, O., Vigmond, E. J., and Bayer, J. D.,
Low-energy, single-pulse surface stimulation defibrillates large mammalian ventricles. Heart Rhythm 2021; doi:
10.1016/j.hrthm.2021.10.006.
149. Nagel, C., Schuler, S., Dössel, O., and Loewe, A.,
A bi-atrial statistical shape model for large-scale in silico studies of human atria: Model development and application to ECG simulations. Medical Image Analysis 2021;74:102210. doi:
10.1016/j.media.2021.102210.
150. Ochs, A. R., Karathanos, T. V., Trayanova, N. A., and Boyle, P. M.,
Optogenetic stimulation using anion channelrhodopsin (GtACR1) facilitates termination of reentrant arrhythmias with low light energy requirements: A computational study. Frontiers in Physiology 2021;12: doi:
10.3389/fphys.2021.718622.
151. Sánchez, J., Luongo, G., Nothstein, M., Unger, L. A., Saiz, J., Trenor, B., Luik, A., Dössel, O., and Loewe, A.,
Using machine learning to characterize atrial fibrotic substrate from intracardiac signals with a hybrid in silico and in vivo dataset. Frontiers in Physiology 2021a;12: doi:
10.3389/fphys.2021.699291.
152. Sánchez, J., Trenor, B., Saiz, J., Dössel, O., and Loewe, A.,
Fibrotic remodeling during persistent atrial fibrillation: In silico investigation of the role of calcium for human atrial myofibroblast electrophysiology. Cells 2021b;10:2852. doi:
10.3390/cells10112852.
153. Schicketanz, L., Unger, L. A., Sánchez, J., Dössel, O., and Loewe, A.,
Separating atrial near fields and atrial far fields in simulated intra-atrial electrograms. Current Directions in Biomedical Engineering 2021;7:175–178. doi:
10.1515/cdbme-2021-2045.
154. Tong, L., Zhao, C., Fu, Z., Dong, R., Wu, Z., Wang, Z., Zhang, N., Wang, X., Cao, B., Sun, Y., Zheng, D., Xia, L., and Deng, D.,
Preliminary study: Learning the impact of simulation time on reentry location and morphology induced by personalized cardiac modeling. Frontiers in Physiology 2021;12: doi:
10.3389/fphys.2021.733500.
155. Azzolin, L., Luongo, G., Rocher, S., Saiz, J., Doessel, O., and Loewe, A.,
Influence of gradient and smoothness of atrial wall thickness on initiation and maintenance of atrial fibrillation. 2020 in
47th Computing in Cardiology Conference (CinC) (Computing in Cardiology). doi:
10.22489/cinc.2020.261.
156. Luongo, G., Azzolin, L., Rivolta, M. W., Almeida, T. P. de, Martı́nez, J. P., Soriano, D. C., Doessel, O., Sassi, R., Laguna, P., and Loewe, A.,
Machine learning to find areas of rotors sustaining atrial fibrillation from the ECG. 2020a in
47th Computing in Cardiology Conference (CinC) (Computing in Cardiology). doi:
10.22489/cinc.2020.181.
157. Luongo, G., Azzolin, L., Rivolta, M. W., Sassi, R., Martinez, J. P., Laguna, P., Dossel, O., and Loewe, A.,
Non-invasive identification of atrial fibrillation driver location using the 12-lead ECG: Pulmonary vein rotors vs. Other locations. 2020b in
42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (
IEEE). doi:
10.1109/embc44109.2020.9176135.
158. Sánchez, J., Nothstein, M., Neic, A., Huang, Y.-L., Prassl, A. J., Klar, J., Ulrich, R., Bach, F., Zschumme, P., Selzer, M., Plank, G., Vigmond, E., Seemann, G., and Loewe, A.,
openCARP: An open sustainable framework for in-silico cardiac electrophysiology research. 2020 in
47th Computing in Cardiology Conference (CinC) (Computing in Cardiology). doi:
10.22489/cinc.2020.111.
159. Wleklinski, M. J., Kannankeril, P. J., and Knollmann, B. C.,
Molecular and tissue mechanisms of catecholaminergic polymorphic ventricular tachycardia. The Journal of Physiology 2020;598:2817–2834. doi:
10.1113/jp276757.
160. Ogbomo-Harmitt, S., Qureshi, A., King, A., and Aslanidi, O.,
Impact of fibrosis border zone characterisation on fibrosis-substrate isolation ablation outcome for atrial fibrillation. NA; Available at:
https://www.researchgate.net/publication/362182383_Impact_of_Fibrosis_Border_Zone_Characterisation_on_Fibrosis-Substrate_Isolation_Ablation_Outcome_for_Atrial_Fibrillation.
161. Cloet, M., Volkaerts, M., Samaey, G., Claus, P., and Dierckx, H.,
Modeling cardiac tissue at multiple scales for bayesian inversion. Available at:
https://kulak.kuleuven.be/posterwall/posters/2023/is_ai/u0121614/u0121614_2023_03_02_053509.pdf.
162. Sánchez Arciniegas, J. P.,
A multiscale in silico study to characterize the atrial electrical activity of patients with atrial fibrillation. A translational study to guide ablation therapy. doi:
10.4995/thesis/10251/171456.
