Publications

Wondering how to cite openCARP?

Publication about openCARP

Plank, G., Loewe A., Neic. A et al. (2021). The openCARP simulation environment for cardiac electrophysiology. Computer Methods and Programs in Biomedicine 2021;208:106223. doi:10.1016/j.cmpb.2021.106223

The list below is autogenerated from the works citing this work. Not all of them necessarily used openCARP directly.

Publications using openCARP

1. Doste, R., Camps, J., Wang, Z. J., Berg, L. A., Holmes, M., Smith, H., Beetz, M., Li, L., Banerjee, A., Grau, V., and Rodriguez, B., An automated computational pipeline for generating large-scale cohorts of patient-specific ventricular models in electromechanical in silico trials. Computer Methods and Programs in Biomedicine 2026;279:109290. doi:10.1016/j.cmpb.2026.109290.
2. Ohnemus, S., Dasí, A., Greiner, J., Wülfers, E. M., Tillert, L., Vierock, J., Quinn, T. A., Kohl, P., Boyle, P. M., Timmermann, V., and Schneider-Warme, F., Channelrhodopsin ion selectivity determines mechanisms and efficacy of optogenetic defibrillation in human atria and ventricles. 2026; doi:10.64898/2026.05.11.724228.
3. Berg, L. A., Oliveira, R. S., Camps, J., Lima, L. M. R. de, Oliveira Campos, J. de, Wang, Z. J., Doste, R., Bueno-Orovio, A., Santos, R. W. dos, and Rodriguez, B., Toward cardiac electrophysiology digital twins with an efficient open source scalable solver on GPU clusters. Scientific Reports 2026;16: doi:10.1038/s41598-025-33709-w.
4. Brown, A. L., Liu, J., Ennis, D. B., and Marsden, A. L., Cardiac mechanics modeling: Recent developments and current challenges. Journal of Elasticity 2026;158: doi:10.1007/s10659-026-10204-5.
5. De Silva, K., Massé, S., Abderrahman, Y., Subha, T., Highsmith, D., Ebrahimi, B., Sheppard, D., Botzer, L., Asta, J., Lai, P. F. H., Denham, N., and Nanthakumar, K., A multidimensional mapping array for assessment of myocardial activation using the electrotomographic mapping concept. Heart Rhythm 2026; doi:10.1016/j.hrthm.2026.03.1902.
6. Denham, N., Massé, S., Abderrahman, Y., Asta, J., Lai, P., Rodriguez, H., Tsoref, L., Nemaire, M., Anderson, R., Vigmond, E., and Nanthakumar, K., Principal component–referenced multipolar mapping to localize an arrhythmic source from various depths of the myocardium. JACC: Clinical Electrophysiology 2026;12:785–798. doi:10.1016/j.jacep.2025.10.016.
7. Gsell, M. A. F., Klöckl, B. A., Neic, A., Mautner, B., Augustin, C. M., Zappon, E., and Plank, G., PyMeshTool — a framework for building efficient automated image-based cardiac anatomical twinning workflows in python. Computer Methods and Programs in Biomedicine 2026;285:109476. doi:10.1016/j.cmpb.2026.109476.
8. Mamajiwala, M., Corrado, C., Lanyon, C. W., Niederer, S. A., Wilkinson, R. D., and Clayton, R. H., Rapid calibration of atrial electrophysiology models using gaussian process emulators in the ensemble kalman filter. Scientific Reports 2026;16: doi:10.1038/s41598-026-39948-9.
9. Martínez Díaz, P., Hosseini, H., Sobota, V., Martínez Antón, C., López Barrera, C., Van Den Abeele, R., Vandersickel, N., Roney, C., Pambrun, T., Hocini, M., Bayer, J., and Vigmond, E. J., Role of interatrial connection ablation in reentry dynamics: An in silico evaluation. Europace 2026; doi:10.1093/europace/euag145.
10. Obada, G., Ogbomo-Harmitt, S., Deprez, M., and Aslanidi, O., “Machine learning highlights left atrial fibrotic heterogeneity as a key predictor of atrial fibrillation inducibility,” 2026 in Statistical Atlases and Computational Models of the Heart. Regular and CMRxRecon Challenge Papers (Springer Nature Switzerland), 249–260. doi:10.1007/978-3-032-17734-6_24.
11. Orós-Rodrigo, S., Fu, J., Greiner, J., Madl, J., Linder, M., Zgierski-Johnston, C., Loewe, A., Kohl, P., and Rog-Zielinska, E., Cardiomyocyte mechanical activity counteracts intraluminal calcium depletion in the transverse-axial tubular system during fast electrical stimulation. 2026; doi:10.64898/2026.01.09.698373.
12. Ramírez, E., Alós, R., Ruipérez-Campillo, S., Cervigón, R., Castells, F., and Millet, J., EGM analyzer: A modular software platform for visualization and analysis of cardiac electrograms. SoftwareX 2026;35:102798. doi:10.1016/j.softx.2026.102798.
13. Sakata, K., Prakosa, A., Yamamoto, C. A. P., Ali, S. Y., Mohsen, Y., Loeffler, S., Kholmovski, E. G., Marine, J. E., Calkins, H., Spragg, D. D., and Trayanova, N. A., Relationships between three-dimensional fibrosis distribution, atrial adiposity, and voltage abnormalities associated with persistent atrial fibrillation. Europace 2026a;28: doi:10.1093/europace/euag060.
14. Sakata, K., Yamamoto, C. A. P., Ali, S. Y., Loeffler, S., Prakosa, A., Tice, B. M., Kholmovski, E. G., Marine, J. E., Calkins, H., Spragg, D. D., and Trayanova, N. A., Assessment of persistent atrial fibrillation arrhythmogenesis in the right atrium using digital twins. Heart Rhythm 2026b;23:e123–e132. doi:10.1016/j.hrthm.2025.10.052.
15. Seghetti, P., Gsell, M. A. F., Prassl, A. J., Bishop, M., and Plank, G., AutoVARP – a framework for automated reproducible inducibility testing in computational models of cardiac electrophysiology. Computer Methods and Programs in Biomedicine 2026;285:109466. doi:10.1016/j.cmpb.2026.109466.
16. Walton, R. D., Renard, E., Haïssaguerre, M., Benoist, D., Chaigne, S., Charron, S., Cheniti, G., Constantin, M., Denis, A., Douard, M., Dubes, V., Duchateau, J., Guicheney, P., Guillot, B., et al., Endocardial ito-slow overexpression and fibrotic remodeling underlying pause-dependent early repolarization in humans. JACC: Clinical Electrophysiology 2026;12:1211–1228. doi:10.1016/j.jacep.2026.01.022.
17. Xin, L., Haiying, L., Yanhong, C., Shiqi, Y., Linsheng, H., and Jian, W., Triggers and maintenance of idiopathic atrial fibrillation: A multiscale computational simulation study. Computer Methods and Programs in Biomedicine 2026;274:109173. doi:10.1016/j.cmpb.2025.109173.
18. Zappon, E., Azzolin, L., Gsell, M. A. F., Thaler, F., Prassl, A. J., Arnold, R., Gillette, K., Kariman, M., Manninger, M., Scherr, D., Neic, A., Urschler, M., Augustin, C. M., Vigmond, E. J., et al., An efficient end-to-end computational framework for the generation of ECG calibrated volumetric models of human atrial electrophysiology. Medical Image Analysis 2026;107:103822. doi:10.1016/j.media.2025.103822.
19. Zhang, K., Magtibay, K., Trayanova, N., and Vigmond, E., A model of β‐adrenergic stimulation in human ventricular cells for tissue‐scale simulations of sympathetically modulated tachycardias. The Journal of Physiology 2026;604:1897–1914. doi:10.1113/jp289340.
20. Zhou, B., Balmus, M., Corrado, C., Cicci, L., Qian, S., and Niederer, S. A., TorchCor: High-performance cardiac electrophysiology simulations with the finite element method on GPUs. SoftwareX 2026;33:102521. doi:10.1016/j.softx.2026.102521.
21. Zolotarev, A. M., Ip, S. H. L., Vydyula, K. S., Dhillon, B. K., Martín, C. H., Misghina, S. B., and Roney, C. H., “openCARP-PINNs: Towards faster prediction of cardiac signals propagation,” 2026 in Statistical Atlases and Computational Models of the Heart. Regular and CMRxRecon Challenge Papers (Springer Nature Switzerland), 214–223. doi:10.1007/978-3-032-17734-6_21.
22. Sakata, K., Yamamoto, C. A. P., Prakosa, A., Tice, B. M., Ali, S. Y., Loeffler, S., Kholmovski, E. G., Sinha, S. K., Marine, J. E., Calkins, H., Spragg, D. D., and Trayanova, N. A., Digital twins enable stratification of persistent atrial fibrillation patients for ablation diminishing unnecessary heart damage. npj Digital Medicine 2025;8: doi:10.1038/s41746-025-01625-y.
23. Sobota, V., Stoks, J., Patel, K. H. K., Shetty, R., Ni, H., Grandi, E., Ng, F. S., Volders, P. G. A., Cluitmans, M. J. M., and Bayer, J. D., The apicobasal dispersion of ventricular repolarization in humans is associated with age and affects arrhythmia vulnerability. The Journal of Physiology 2025; doi:10.1113/jp288356.
24. Van Den Abeele, R., Hendrickx, S., Carlier, N., Wülfers, E. M., Santos Bezerra, A., Verstraeten, B., Lootens, S., Desplenter, K., Okenov, A., Nezlobinsky, T., Haas, A., Luik, A., Knecht, S., Duytschaever, M., et al., DGM-TOP: Automatic identification of the critical boundaries in atrial tachycardia. Frontiers in Physiology 2025a;16: doi:10.3389/fphys.2025.1563807.
25. Wei, S., Guenter, V., and Spiteri, R. J., Improving the stability and efficiency of operator-splitting methods. SIAM Journal on Scientific Computing 2025;47:A1888–A1906. doi:10.1137/24m1683184.
26. Alba, V., Aumage, O., Barthou, D., Counilh, M.-C., and Guermouche, A., Performance portability of generated cardiac simulation kernels through automatic dimensioning and load balancing on heterogeneous nodes. The Journal of Supercomputing 2025;81: doi:10.1007/s11227-025-07510-5.
27. Antoniou, C.-K., Karampinos, K., Tsiachris, D., Kordalis, A., Arsenos, P., Doundoulakis, I., Dilaveris, P., Milaras, N., Sideris, S., Kariki, O., Kasiakogias, A., Vlachopoulos, C., Toutouzas, K., Tsioufis, K., et al., Current trends in virtual electrophysiology use for risk stratification and treatment of ventricular arrhythmias. Frontiers in Cardiovascular Medicine 2025;12: doi:10.3389/fcvm.2025.1709175.
28. Barrios Espinosa, C., Sánchez, J., Appel, S., Becker, S., Krauß, J., Martínez Díaz, P., Unger, L., Houillon, M., and Loewe, A., A cyclical fast iterative method for simulating reentries in cardiac electrophysiology using an eikonal-based model. Engineering with Computers 2025;41:2335–2358. doi:10.1007/s00366-024-02094-9.
29. Bezerra, A. S., Hendrickx, S., Van den Abeele, R., Wülfers, E. M., Verstraeten, B., Lootens, S., Okenov, A., Nezlobinsky, T., Knecht, S., Duytschaever, M., Segers, V. F. M., and Vandersickel, N., Cross-correlation as an alternative for local activation times for the analysis of reentries in directed graph mapping. Biomedical Signal Processing and Control 2025;106:107716. doi:10.1016/j.bspc.2025.107716.
30. 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 2025a;185:109529. doi:10.1016/j.compbiomed.2024.109529.
31. Biasi, N., Vultaggio, D. M., Seghetti, P., Laurino, M., and Tognetti, A., A matlab platform for pacemaker algorithm assessment based on anatomically detailed closed-loop computer modeling. Engineering with Computers 2025b;41:4511–4527. doi:10.1007/s00366-025-02214-z.
32. Bifulco, S. F., Magoon, M. J., Chahine, Y., Kim, I., Macheret, F., Akoum, N., and Boyle, P. M., Predicting arrhythmia recurrence post-ablation in atrial fibrillation using explainable machine learning. Communications Medicine 2025;5: doi:10.1038/s43856-025-01058-4.
33. Cairns, D. I., Comstock, M. R., Fenton, F. H., and Cherry, E. M., CardioFit: A WebGL-based tool for fast and efficient parametrization of cardiac action potential models to fit user-provided data. Royal Society Open Science 2025;12: doi:10.1098/rsos.250048.
34. Campos, F. O., Wijesuriya, N., Elliott, M. K., Vere, F. de, Howell, S., Strocchi, M., Monaci, S., Whitaker, J., Plank, G., Rinaldi, C. A., and Bishop, M. J., In silico pace mapping identifies pacing sites more accurately than inverse body surface potential mapping. Heart Rhythm 2025;22:1790–1799. doi:10.1016/j.hrthm.2024.12.036.
35. Canino, G., Di Costanzo, A., Salerno, N., Leo, I., Cannataro, M., Guzzi, P. H., Veltri, P., Sorrentino, S., De Rosa, S., and Torella, D., Artificial intelligence in cardiac electrophysiology: A clinically oriented review with engineering primers. Bioengineering 2025;12:1102. doi:10.3390/bioengineering12101102.
36. Centofanti, E., Huynh, N. M. M., Pavarino, L. F., and Scacchi, S., Parallel algebraic multigrid solvers for composite discontinuous galerkin discretization of the cardiac EMI model in heterogeneous media. Computer Methods in Applied Mechanics and Engineering 2025;442:118001. doi:10.1016/j.cma.2025.118001.
37. Costa de Almeida, R., Almonfrey, D., Squara, F., and Zarzoso, V., Generation of realistic synthetic electrograms for atrial fibrillation analysis. 2025 in 2025 Computing in Cardiology Conference (CinC) CinC2025. (Computing in Cardiology). doi:10.22489/cinc.2025.436.
38. Denham, N. C., Suszko, A. M., Nemaire, M., Bhaskaran, A., Massé, S., Nanthakumar, K., Haïssaguerre, M., Downar, E., Vigmond, E., and Chauhan, V. S., Microvolt activation alternans identifies conduction velocity heterogeneity and the entrance site of ischemic ventricular tachycardia. JACC: Clinical Electrophysiology 2025;11:1114–1128. doi:10.1016/j.jacep.2025.02.014.
39. Finsberg, H., Fenicsx-beat - an open source simulation framework for cardiac electrophysiology. Journal of Open Source Software 2025;10:8416. doi:10.21105/joss.08416.
40. Houillon, M., Klar, J., Boutanios, Z., Stary, T., Cojean, T., Anzt, H., and Loewe, A., FACILE-RS: Archiving and long-term preservation of research software repositories made easy. Journal of Open Source Software 2025;10:7330. doi:10.21105/joss.07330.
41. Huethorst, E., Bishop, M. J., Burton, F. L., Denning, C., Gadegaard, N., Myles, R. C., and Smith, G. L., Evidence for intermittent coupling of intramyocardial small, engineered heart tissues acutely implanted into rabbit myocardium. Cardiovascular Research 2025;121:1697–1711. doi:10.1093/cvr/cvaf034.
42. 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.
43. Jung, A., Augustin, C. M., Voglhuber-Höller, J., Kiessling, M., Ljubojevic-Holzer, S., Mirams, G. R., Niederer, S. A., and Plank, G., Computational modelling for improved translation of cardiac inotropic and lusitropic drug effects from rats to humans. Journal of Pharmacological and Toxicological Methods 2025;134:107747. doi:10.1016/j.vascn.2025.107747.
44. Kabus, D. A., Dierckx, H., and De Coster, T., Pigreads: The python-integrated GPU-enabled reaction-diffusion solver using OpenCL for cardiac electrophysiology and other applications. 2025; doi:10.2139/ssrn.5761824.
45. Kariman, M., Gillette, K., Gsell, M. A. F., Prassl, A. J., Plank, G., and Augustin, C. M., Computational modelling of the impact of anatomical changes on ECGs in left ventricular hypertrophy. The Journal of Physiology 2025;603:5387–5413. doi:10.1113/jp287954.
46. Langen, J. S., Boyle, P. M., Malan, D., and Sasse, P., Optogenetic quantification of cardiac excitability and electrical coupling in intact hearts to explain cardiac arrhythmia initiation. Science Advances 2025;11: doi:10.1126/sciadv.adt4103.
47. Lee, J. D., Nguyen, A., Gibbs, C. E., Jin, Z. R., Wang, Y., Moghadasi, A., Wait, S. J., Choi, H., Evitts, K. M., Asencio, A., Bremner, S. B., Zuniga, S., Chavan, V., Pranoto, I. K. A., et al., Monitoring in real time and far-red imaging of H2O2 dynamics with subcellular resolution. Nature Chemical Biology 2025;22:556–567. doi:10.1038/s41589-025-01891-7.
48. 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. The Journal of Physiology 2025; doi:10.1113/jp287557.
49. Lootens, S., Van den Abeele, R., Kappadan, V., Handa, B., Duytschaever, M., Knecht, S., Luik, A., Haas, A., Wülfers, E. M., Bezerra, A. S., Verstraeten, B., Hendrickx, S., Okenov, A., Nezlobinsky, T., et al., Detection of regular rotational activity during cardiac arrhythmia using the helmholtz decomposition for directed graphs. Journal of Molecular and Cellular Cardiology 2025;204:40–54. doi:10.1016/j.yjmcc.2025.05.002.
50. Lydon, H., Kazemi, M., Bishop, M., and Paoletti, N., SimICD: A closed-loop simulation framework for ICD therapy. 2025 in 2025 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (IEEE), 1–7. doi:10.1109/embc58623.2025.11254859.
51. Myklebust, L., Arevalo, H., Daversin-Catty, C., Wall, S. T., and Finsberg, H. N. T., Impact of segregation scheme on performance of a strongly coupled cardiac electromechanical solver. Computer Methods in Applied Mechanics and Engineering 2025;446:118270. doi:10.1016/j.cma.2025.118270.
52. Obada, G., Ogbomo-Harmitt, S., Deprez, M., Van Den Abeelen, R., Vandersickel, N., and Aslanidi, O., “Rotor core projection ablation (RCPA): Novel computational approach to catheter ablation therapy for atrial fibrillation,” 2025 in Statistical Atlases and Computational Models of the Heart. Workshop, CMRxRecon and MBAS Challenge Papers. (Springer Nature Switzerland), 313–322. doi:10.1007/978-3-031-87756-8_31.
53. Osta, N. van, Acker, G. van den, Loon, T. van, Arts, T., Delhaas, T., and Lumens, J., Numerical accuracy of closed-loop steady state in a zero-dimensional cardiovascular model. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 2025;383: doi:10.1098/rsta.2024.0208.
54. Pikunov, A. V., Syunyaev, R. A., Ali, R., Prakosa, A., Gams, A., Boyle, P. M., Steckmeister, V., Kutschka, I., Rytkin, E., Voigt, N., Trayanova, N., and Efimov, I. R., Role of structural versus cellular remodeling in atrial arrhythmogenesis: Insights from personalized digital twins. Circulation: Arrhythmia and Electrophysiology 2025;18: doi:10.1161/circep.125.013898.
55. Telle, Å., Kassar, A., Chamoun, N., Haykal, R., Gonzalo, A., Hensley, T., Chahine, Y., Flores, O., Álamo, J. C. del, Akoum, N., Augustin, C. M., and Boyle, P. M., Systematic computational assessment of atrial function impairment due to fibrotic remodeling in electromechanical properties. 2025; doi:10.1101/2025.06.24.661244.
56. Van Den Abeele, R., Lootens, S., Verstraeten, B., Bezerra, A. S., Okenov, A., Nezlobinskii, T., Van Nieuwenhuize, V., Hendrickx, S., and Vandersickel, N., Paired reentries maintain ventricular tachycardia: A topological analysis of arrhythmic mechanisms using the index theorem. Frontiers in Network Physiology 2025b;5: doi:10.3389/fnetp.2025.1638085.
57. Waight, M. C., Prakosa, A., Li, A. C., Truong, A., Bunce, N., Marciniak, A., Trayanova, N. A., and Saba, M. M., Heart digital twins predict features of invasive reentrant circuits and ablation lesions in scar-dependent ventricular tachycardia. Circulation: Arrhythmia and Electrophysiology 2025;18: doi:10.1161/circep.124.013660.
58. Yang, H., Cheng, Y., Zhao, P., Cai, J., Yin, Z., Chen, S., Guo, G., Zhu, C., Liu, K., and Zu, L., Uncover hidden physical information of soft matter by observing large deformation. Advanced Science 2025;12: doi:10.1002/advs.202414526.
59. Zenger, B., Smith, T. W., Hicks, S., Ng, S., Pavek, T., Knutson, N., Samson, P. P., Zheng, J., Berberet, C., Ibrahim, E.-S. H., Jani, V., Tabor, J., Wilson, L. D., Jordan, S. D., et al., STAR locally prolongs effective refractory period and increases ventricular tachycardia cycle length without short-term scar formation or functional decline: Insights from a translational porcine model study. Circulation: Arrhythmia and Electrophysiology 2025;18: doi:10.1161/circep.124.013684.
60. Zhang, Y., Prakosa, A., Zhang, K., Carrick, R., Chrispin, J., Zimmerman, S., Aronis, K., Kholmovski, E., Tichnell, C., Murray, B., James, C., Calkins, H., and Trayanova, N., Genotype-specific digital twins for arrhythmia ablation targeting in arrhythmogenic right ventricular cardiomyopathy. 2025; doi:10.21203/rs.3.rs-8006874/v1.
61. Zhu, E., and Yang, S., Towards human digital twin: Reviewing human modelling and simulation. Journal of Industrial Information Integration 2025;48:100975. doi:10.1016/j.jii.2025.100975.
62. 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.
63. 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.
64. 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.
65. 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.
66. 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.
67. Colebank, M. J., Oomen, P. A., Witzenburg, C. M., Grosberg, A., Beard, D. A., Husmeier, D., Olufsen, M. S., and Chesler, N. C., Guidelines for mechanistic modeling and analysis in cardiovascular research. American Journal of Physiology-Heart and Circulatory Physiology 2024;327:H473–H503. doi:10.1152/ajpheart.00766.2023.
68. 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.
69. 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;182:1125–1142. doi:10.1111/bph.17394.
70. Finsberg, H., and Hake, J., Gotranx: General ODE translator. Journal of Open Source Software 2024;9:7063. doi:10.21105/joss.07063.
71. 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.
72. 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;602:6789–6812. doi:10.1113/jp287011.
73. Grzelak, J., Ogbomo-Harmitt, S., King, A., and Aslanidi, O., In-silico investigation of the right and left atrial contributions to the p-wave morphology in ECG of healthy and atrial fibrillation patients. 2024 in 2024 Computing in Cardiology Conference (CinC) CinC2024. (Computing in Cardiology). doi:10.22489/cinc.2024.122.
74. Hirschvogel, M., Ambit – a FEniCS-based cardiovascular multi-physics solver. Journal of Open Source Software 2024;9:5744. doi:10.21105/joss.05744.
75. 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.
76. 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.
77. 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.
78. 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.
79. 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.
80. Marti Roig, A., Gillette, K., Ramirez, E., Castells, F., Millet, J., Plank, G., Gsell, M., and Vigmond, E., Accessory pathway localisation optimisation: In silico heart model evaluation. 2024 in 2024 Computing in Cardiology Conference (CinC) CinC2024. (Computing in Cardiology). doi:10.22489/cinc.2024.470.
81. Martinez Diaz, P., Maierhofer, P., Beigl, M., Loewe, A., and Doessel, O., Insights from explainable machine learning on biatrial arrhythmia vulnerability assessment. 2024 in 2024 Computing in Cardiology Conference (CinC) CinC2024. (Computing in Cardiology). doi:10.22489/cinc.2024.308.
82. 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 2024a;26: doi:10.1093/europace/euae215.
83. Martínez Díaz, P., Sánchez, J., Fitzen, N., Ravens, U., Dössel, O., and Loewe, A., The right atrium affects in silico arrhythmia vulnerability in both atria. Heart Rhythm 2024b;21:799–805. doi:10.1016/j.hrthm.2024.01.047.
84. Myklebust, L., Maleckar, M. M., and Arevalo, H., Fibrosis modeling choice affects morphology of ventricular arrhythmia in non-ischemic cardiomyopathy. Frontiers in Physiology 2024;15: doi:10.3389/fphys.2024.1370795.
85. 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.
86. 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.
87. 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.
88. 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.
89. 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.
90. 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 2024;10:2347–2358. doi:10.1016/j.jacep.2024.07.002.
91. 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.
92. 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;4:928–940. doi:10.1038/s43588-024-00732-2.
93. 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.
94. 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.
95. 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.
96. 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.
97. 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.
98. 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.
99. 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.
100. 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.
101. 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.
102. 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.
103. 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.
104. 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.
105. 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.
106. 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.
107. 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.
108. 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.
109. 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.
110. 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.
111. 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.
112. 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.
113. 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.
114. 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.
115. 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.
116. 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.
117. 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.
118. 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.
119. 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.
120. 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 2022;60:2463–2478. doi:10.1007/s11517-022-02621-0.
121. 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.
122. 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.
123. Alba, V., Load balancing and precision analysis for cardiac simulation M2 Internship report. 2022; Available at: https://hal.inria.fr/hal-03784546.
124. Alberto Barrios Espinosa, A., “Cristian and Sánchez, Jorge and Doessel, Olaf and Loewe”, 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.
125. 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.
126. 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.
127. 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.
128. 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.
129. 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.
130. 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.
131. 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;
132. 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.
133. Klein, V. S., Modeling and measuring cardiac magnetostimulation. 2022;
134. Lee, C. H., Application of neural networks to predict patient-specific cellular parameters in computational cardiac models. 2022;
135. Reimer, J. A., A comparison of the bidomain and EMI models in refractory cardiac tissue. 2022; Available at: https://hdl.handle.net/10388/14430.
136. 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.
137. Ryzhii, M., and Ryzhii, E., Pacemaking function of two simplified cell models. PLOS ONE 2022;17:e0257935. doi:10.1371/journal.pone.0257935.
138. 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).
139. 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.
140. 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.
141. 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.
142. 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.
143. 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.
144. 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.
145. 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.
146. 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.
147. 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.
148. 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.
149. 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.
150. Monbaliu, R., A meandering spiral due to early afterdepolarizations as possible mechanism of torsade de pointes. 2021;
151. 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.
152. 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.
153. 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.
154. 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.
155. 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.
156. 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.
157. 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.
158. 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.
159. 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.
160. 2014 in Enterprise Integration and Information Architecture (Auerbach Publications), 320–355. doi:10.1201/b17156-11.
161. Camargo, M. L. A., Bassani, J. W. M., Bassani, R. A., and Silva, R. R., ForceLAB simulator update: A study on the positive cooperativity of cross-bridge formation in ventricular myocyte. EasyChair, 2022;
162. 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.
163. 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.
164. 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.

Publications using CARP/CARPentry

For a list of publications using CARP/CARPentry, the predecessor of openCARP, see the website of the Computational Cardiology Lab (Medical University of Graz, Austria).

© Copyright 2020 openCARP project    Supported by DFG and EuroHPC    Contact    Imprint and data protection