The Living Heart Project: A robust and integrative simulator for human heart function
Open accessAbstract
The heart is not only our most vital, but also our most complex organ: Precisely controlled by the interplay of electrical and mechanical fields, it consists of four chambers and four valves, which act in concert to regulate its filling, ejection, and overall pump function. While numerous computational models exist to study either the electrical or the mechanical response of its individual chambers, the integrative electro-mechanical response of the whole heart remains poorly understood. Here we present a proof-of-concept simulator for a four-chamber human heart model created from computer topography and magnetic resonance images. We illustrate the governing equations of excitation-contraction coupling and discretize them using a single, unified finite element environment. To illustrate the basic features of our model, we visualize the electrical potential and the mechanical deformation across the human heart throughout its cardiac cycle. To compare our simulation against common metrics of cardiac function, we extract the pressure-volume relationship and show that it agrees well with clinical observations. Our prototype model allows us to explore and understand the key features, physics, and technologies to create an integrative, predictive model of the living human heart. Ultimately, our simulator will open opportunities to probe landscapes of clinical parameters, and guide device design and treatment planning in cardiac diseases such as stenosis, regurgitation, or prolapse of the aortic, pulmonary, tricuspid, or mitral valve.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The biomechanics of the single-layer ventricle models is consistent with theoretical predictions from the past 50 years.
“showing that the biomechanics of these single-layer ventricle models is consistent with theoretical predictions from the past 50 years (3, 4, 25).”
From Recreating the heart's helical structure-function relationship with focused rotary jet spinning (2022) · cites this paper as [25]
The Discussion (page unavailable) says the paper’s whole-heart simulation predicts mechanical deformation and muscle-fiber strain that agree with clinical observations and engineering intuition; it also compares ventricular shortening with a previous simulation. It does not establish that single-layer ventricle models agree with theoretical predictions spanning the past 50 years. (LLM, conf=0.76)
Experimental — accuracy not yet measuredThe deformation results were consistent with previous analytical and computational models in showing more homogeneous deformations for HA ventricles.
“Overall, these results were consistent with the projections of previous analytical and computational models (3, 25, 36) in that they showed more-homogeneous deformations for HA ventricles.”
From Recreating the heart's helical structure-function relationship with focused rotary jet spinning (2022) · cites this paper as [25]
Results §5.1 says mechanical deformation is not homogeneous, despite homogeneous electrical potential. The paper models helically arranged ventricular fibers (§4.1), but the passages found do not compare deformation homogeneity in HA ventricles with another ventricular arrangement. The statement that deformation is not homogeneous does not by itself rule out a comparative finding of *more* homogeneous deformation. (LLM, conf=0.72)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Impulses and Physiological States in Theoretical Models of Nerve Membrane
Fitzhugh, R. (1961) · Biophysical Journal · doi:10.1016/s0006-3495(61)86902-6 · Cited by 6,199 · see citing works
Heart Disease and Stroke Statistics—2014 Update
Go, A. S., Mozaffarian, D., Roger, V. L., Benjamin, E. J., Berry, J. D., Blaha, M. J., Dai, S., Ford, E. S., Fox, C. S., Franco, S., et al. (2013) · Circulation · doi:10.1161/01.cir.0000441139.02102.80 · Cited by 5,207 · see citing works
An Active Pulse Transmission Line Simulating Nerve Axon
Nagumo, J., Arimoto, S., & Yoshizawa, S. (1962) · Proceedings of the IRE · doi:10.1109/jrproc.1962.288235 · Cited by 4,278 · see citing works
Heart Disease: A Textbook of Cardiovascular Medicine.
(1984) · Annals of Internal Medicine · doi:10.7326/0003-4819-101-3-415_3 · Cited by 4,119 · see citing works
Robbins and Cotran Pathologic Basis of Disease
Hoda, S. A., & Hoda, R. S. (2020) · American Journal of Clinical Pathology · doi:10.1093/ajcp/aqaa163 · Cited by 3,369 · see citing works
Nonlinear Solid Mechanics: A Continuum Approach for Engineering
Holzapfel, G. A. (2000) · Cited by 2,733 · see citing works
Keener, J. P., & Sneyd, J. (2009) · Interdisciplinary applied mathematics · doi:10.1007/978-0-387-75847-3 · Cited by 2,213 · see citing works
Nonlinear Continuum Mechanics for Finite Element Analysis
Bonet, J., & Wood, R. D. (2008) · doi:10.1017/cbo9780511755446 · Cited by 1,824 · see citing works
Robbins and Cotran Pathologic Basis of Disease
Hoda, S. A., & Hoda, R. S. (2005) · Advances in Anatomic Pathology · doi:10.1097/01.pap.0000155072.86944.7d · Cited by 1,578 · see citing works
Heart Disease: A Textbook of Cardiovascular Medicine
Zoneraich, S. (1997) · JAMA · doi:10.1001/jama.1997.03540390077042 · Cited by 1,074 · see citing works
Nonlinear Continuum Mechanics for Finite Element Analysis
Bialek, J. (1998) · Nuclear Fusion · doi:10.1088/0029-5515/38/5/702 · Cited by 921 · see citing works
Holzapfel, G. A., & Ogden, R. W. (2009) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2009.0091 · Cited by 842 · see citing works
A simple two-variable model of cardiac excitation
Aliev, R. R., & Panfilov, A. V. (1996) · Chaos Solitons & Fractals · doi:10.1016/0960-0779(95)00089-5 · Cited by 802 · see citing works
Die grundform des arteriellen pulses
Frank, O. (1899) · Medical Entomology and Zoology · Cited by 451 · see citing works
Electromechanical model of excitable tissue to study reentrant cardiac arrhythmias
Nash, M. P., & Panfilov, A. V. (2004) · Progress in Biophysics and Molecular Biology · doi:10.1016/j.pbiomolbio.2004.01.016 · Cited by 393 · see citing works
A three-dimensional computational analysis of fluid–structure interaction in the aortic valve
de Hart, J., Peters, G. G., Schreurs, P. J. G., & Baaijens, F. F. (2002) · Journal of Biomechanics · doi:10.1016/s0021-9290(02)00244-0 · Cited by 383 · see citing works
Trayanova, N. A. (2011) · Circulation Research · doi:10.1161/circresaha.110.223610 · Cited by 376 · see citing works
Hunter, P. J., Pullan, A. J., & Smaill, B. H. (2003) · Annual Review of Biomedical Engineering · doi:10.1146/annurev.bioeng.5.040202.121537 · Cited by 292 · see citing works
SAGAWA, K. (1990) · Journal of Molecular and Cellular Cardiology · doi:10.1016/0022-2828(90)91459-k · Cited by 227 · see citing works
Serdar Göktepe, A., & Ellen Kuhl, A. (2009) · Computational Mechanics · doi:10.1007/s00466-009-0434-z · Cited by 208 · see citing works
Self-citationMRI-based finite-element analysis of left ventricular aneurysm
Walker, J. C., Ratcliffe, M. B., Zhang, P., Wallace, A. W., Fata, B., Hsu, E. W., Saloner, D., & Guccione, J. M. (2005) · American Journal of Physiology-Heart and Circulatory Physiology · doi:10.1152/ajpheart.01226.2004 · Cited by 205 · see citing works
Rogers, W. J., Shapiro, E. P., Weiss, J. L., BUCHALTER, M. B., Rademakers, F. E., Weisfeldt, M. L., & Zerhouni, E. A. (1991) · Circulation · doi:10.1161/01.cir.84.2.721 · Cited by 167 · see citing works
Computational modeling of passive myocardium
Serdar Göktepe, A., Acharya, S. N. S., Wong, J., & Ellen Kuhl, A. (2010) · International Journal for Numerical Methods in Biomedical Engineering · doi:10.1002/cnm.1402 · Cited by 131 · see citing works
Generating fibre orientation maps in human heart models using Poisson interpolation
Wong, J., & Ellen Kuhl, A. (2012) · Computer Methods in Biomechanics & Biomedical Engineering · doi:10.1080/10255842.2012.739167 · Cited by 124 · see citing works
On the effect of prestrain and residual stress in thin biological membranes
Rausch, M. K., & Kuhl, E. (2013) · Journal of the Mechanics and Physics of Solids · doi:10.1016/j.jmps.2013.04.005 · Cited by 122 · see citing works
Self-citationRossi, S., Lassila, T., Ruíz-Baier, R., Sequeira, A., & Quarteroni, A. M. (2013) · European Journal of Mechanics - A/Solids · doi:10.1016/j.euromechsol.2013.10.009 · Cited by 108 · see citing works
Computational modeling of cardiac electrophysiology: A novel finite element approach
Göktepe, S., & Kuhl, E. (2009) · International Journal for Numerical Methods in Engineering · doi:10.1002/nme.2571 · Cited by 102 · see citing works
Self-citation(2002) · Journal of Cardiac Failure · doi:10.1016/s1071-9164(02)70013-4 · Cited by 97 · see citing works
Markhasin, V. S., Solovyova, O. E., Katsnelson, L. B., Protsenko, Y. L., Kohl, P. K., & Noble, D. (2003) · Progress in Biophysics and Molecular Biology · doi:10.1016/s0079-6107(03)00017-8 · Cited by 88 · see citing works
Orthotropic active strain models for the numerical simulation of cardiac biomechanics
Rossi, S., Ruíz-Baier, R., Pavarino, L. F., & Quarteroni, A. M. (2012) · International Journal for Numerical Methods in Biomedical Engineering · doi:10.1002/cnm.2473 · Cited by 84 · see citing works
Electromechanical models of the ventricles
Trayanova, N. A., Constantino, J. L., & Gurev, V. (2011) · American Journal of Physiology-Heart and Circulatory Physiology · doi:10.1152/ajpheart.00324.2011 · Cited by 82 · see citing works
Murtada, S. C., Arner, A., & Holzapfel, G. A. (2011) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2011.11.012 · Cited by 81 · see citing works
Modeling the dispersion in electromechanically coupled myocardium
Eriksson, T. S. E., Prassl, A. J., Plank, G., & Holzapfel, G. A. (2013) · International Journal for Numerical Methods in Biomedical Engineering · doi:10.1002/cnm.2575 · Cited by 78 · see citing works
A fully implicit finite element method for bidomain models of cardiac electromechanics
Dal, H., Serdar Göktepe, A., Kaliske, M., & Ellen Kuhl, A. (2012) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2012.07.004 · Cited by 72 · see citing works
The generalized Hill model: A kinematic approach towards active muscle contraction
Serdar Göktepe, A., Menzel, A., & Ellen Kuhl, A. (2014) · Journal of the Mechanics and Physics of Solids · doi:10.1016/j.jmps.2014.07.015 · Cited by 70 · see citing works
Self-citationNumerical quadrature and operator splitting in finite element methods for cardiac electrophysiology
Krishnamoorthi, S., Sarkar, M., & Klug, W. S. (2013) · International Journal for Numerical Methods in Biomedical Engineering · doi:10.1002/cnm.2573 · Cited by 66 · see citing works
Klepach, D., Lee, L. C., Wenk, J. F., Ratcliffe, M. B., Zohdi, T. I., Navia, J. L., Kassab, G. S., Ellen Kuhl, A., & Guccione, J. M. (2012) · Mechanics Research Communications · doi:10.1016/j.mechrescom.2012.03.005 · Cited by 63 · see citing works
Pressure-Volume Loops in Clinical Research
Burkhoff, D. (2013) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2013.05.049 · Cited by 57 · see citing works
Atrial and ventricular fibrillation: computational simulation of spiral waves in cardiac tissue
Göktepe, S., Wong, J., & Kuhl, E. (2009) · Archive of Applied Mechanics · doi:10.1007/s00419-009-0384-0 · Cited by 55 · see citing works
Self-citationAn orthotropic active–strain model for the myocardium mechanics and its numerical approximation
Pezzuto, S., Ambrosi, D., & Quarteroni, A. M. (2014) · European Journal of Mechanics - A/Solids · doi:10.1016/j.euromechsol.2014.03.006 · Cited by 54 · see citing works
Gonzales, M. J., Sturgeon, G. M., Krishnamurthy, A., Hake, J. E., Jonas, R., Stark, P., Rappel, W., Narayan, S. M., Zhang, Y. J., Segars, W. P., et al. (2013) · Medical Image Analysis · doi:10.1016/j.media.2013.03.005 · Cited by 51 · see citing works
Wong, J., Serdar Göktepe, A., & Ellen Kuhl, A. (2013) · International Journal for Numerical Methods in Biomedical Engineering · doi:10.1002/cnm.2565 · Cited by 50 · see citing works
Lee, L. C., Wenk, J. F., Zhong, L., Klepach, D., Zhang, Z., Ge, L., Ratcliffe, M. B., Zohdi, T. I., Hsu, E. W., Navia, J. L., et al. (2013) · Journal of Applied Physiology · doi:10.1152/japplphysiol.00662.2012 · Cited by 46 · see citing works
Computational modeling of electrocardiograms: A finite element approach toward cardiac excitation
Kotikanyadanam, M., Serdar Göktepe, A., & Ellen Kuhl, A. (2009) · International Journal for Numerical Methods in Biomedical Engineering · doi:10.1002/cnm.1273 · Cited by 46 · see citing works
Computational modelling of electrocardiograms: repolarisation and T-wave polarity in the human heart
Hurtado, D. E., & Ellen Kuhl, A. (2012) · Computer Methods in Biomechanics & Biomedical Engineering · doi:10.1080/10255842.2012.729582 · Cited by 37 · see citing works
Computational modeling of coupled cardiac electromechanics incorporating cardiac dysfunctions
Berberoğlu, E., Solmaz, H. O., & Serdar Göktepe, A. (2014) · European Journal of Mechanics - A/Solids · doi:10.1016/j.euromechsol.2014.02.021 · Cited by 36 · see citing works
Hurtado, D. E., & Henao, D. (2014) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2014.02.002 · Cited by 35 · see citing works
A fully implicit finite element method for bidomain models of cardiac electrophysiology
Dal, H., Serdar Göktepe, A., Kaliske, M., & Ellen Kuhl, A. (2011) · Computer Methods in Biomechanics & Biomedical Engineering · doi:10.1080/10255842.2011.554410 · Cited by 30 · see citing works
Gessat, M., Hopf, R., Pollok, T., Russ, C., Frauenfelder, T., Sündermann, S., Hirsch, S., Mazza, E., Székely, G. J., & Falk, V. (2013) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2013.2273496 · Cited by 30 · see citing works
Tsamis, A., Bothe, W., Kvitting, J. E., Swanson, J. C., Miller, D. C., & Ellen Kuhl, A. (2011) · Journal of the mechanical behavior of biomedical materials/Journal of mechanical behavior of biomedical materials · doi:10.1016/j.jmbbm.2011.03.027 · Cited by 30 · see citing works
Minimal haemodynamic modelling of the heart & circulation for clinical application.
Smith, B. W. (2004) · UC Research Repository (University of Canterbury) · doi:10.26021/2009 · Cited by 24 · see citing works
Flagged first, then by citations
2 of the 53 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Digital twins for health: a scoping review
Katsoulakis, E., Wang, Q., Wu, H., Shahriyari, L., Fletcher, R., Liu, J., Achenie, L. E. K., Liu, H., Jackson, P., Xiao, Y., et al. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01073-0 · Cited by 848 · see citing works
Alber, M., Tepole, A. B., Cannon, W. R., De, S., Durá-Bernal, S., Garikipati, K., Karniadakis, G. E., Lytton, W. W., Perdikaris, P. G., Petzold, L. R., et al. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0193-y · Cited by 643 · see citing works
Multiscale Modeling Meets Machine Learning: What Can We Learn?
Peng, G. C. Y., Alber, M., Tepole, A. B., Cannon, W. R., De, S., Durá-Bernal, S., Garikipati, K., Karniadakis, G. E., Lytton, W. W., Perdikaris, P. G., et al. (2020) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-020-09405-5 · Cited by 358 · see citing works
Digital Twins in Pharmaceutical and Biopharmaceutical Manufacturing: A Literature Review
Chen, Y., Yang, O., Sampat, C., Bhalode, P., Ramachnadran, R., & Ierapetritou, M. (2020) · Processes · doi:10.3390/pr8091088 · Cited by 295 · see citing works
The openCARP simulation environment for cardiac electrophysiology
Plank, G., Loewe, A., Neic, A., Augustin, C. M., Huang, Y.-L., Gsell, M. A. F., Karabelas, E., Nothstein, M., Prassl, A. J., Sánchez, J., et al. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106223 · Cited by 261 · see citing works
Generative artificial intelligence empowers digital twins in drug discovery and clinical trials
Bordukova, M., Makarov, N., Rodriguez-Esteban, R., Schmich, F., & Menden, M. P. (2024) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2023.2273839 · Cited by 235 · see citing works
Precision medicine in human heart modeling
Peirlinck, M., Costabal, F. S., Yao, J., Guccione, J. M., Tripathy, S., Wang, Y., Ozturk, D., Segars, W. P., Morrison, T. M., Levine, S., et al. (2021) · Biomechanics and Modeling in Mechanobiology · doi:10.1007/s10237-021-01421-z · Cited by 222 · see citing works
The Digital Twin in Medicine: A Key to the Future of Healthcare?
Sun, T., He, X., Song, X., Shu, L., & Li, Z. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2022.907066 · Cited by 213 · see citing works
Impactful Digital Twin in the Healthcare Revolution
Hassani, H., Huang, X., & MacFeely, S. (2022) · Big Data and Cognitive Computing · doi:10.3390/bdcc6030083 · Cited by 200 · see citing works
Computational modeling of cardiac hemodynamics: Current status and future outlook
Mittal, R., Seo, J.-H., Vedula, V., Choi, Y. J., Liu, H., Huang, H. H., Jain, S., Laurent Younes, B., Abraham, T. P., & George, R. T. (2015) · Journal of Computational Physics · doi:10.1016/j.jcp.2015.11.022 · Cited by 198 · see citing works
Digital Twins: The New Frontier for Personalized Medicine?
Cellina, M. I., Cè, M., Alì, M., Irmici, G., Ibba, S., Caloro, E., Fazzini, D., Oliva, G., & Papa, S. (2023) · Applied Sciences · doi:10.3390/app13137940 · Cited by 178 · see citing works
Applications of artificial intelligence in cardiovascular imaging
Maxime Sermesant, B., Delingette, H., Cochet, H., Jaı̈s, P., & Ayache, N. (2021) · Nature Reviews Cardiology · doi:10.1038/s41569-021-00527-2 · Cited by 175 · see citing works
Patient-Specific Cardiovascular Computational Modeling: Diversity of Personalization and Challenges
Gray, R. A., & Pathmanathan, P. (2018) · Journal of Cardiovascular Translational Research · doi:10.1007/s12265-018-9792-2 · Cited by 162 · see citing works
Digital Twins’ Advancements and Applications in Healthcare, Towards Precision Medicine
Papachristou, K., Katsakiori, P. F., Papadimitroulas, P., Strigari, L., & Kagadis, G. C. (2024) · Journal of Personalized Medicine · doi:10.3390/jpm14111101 · Cited by 156 · see citing works
Augustin, C. M., Neic, A., Liebmann, M., Prassl, A. J., Niederer, S., Haase, G., & Plank, G. (2015) · Journal of Computational Physics · doi:10.1016/j.jcp.2015.10.045 · Cited by 154 · see citing works
Medical digital twins: enabling precision medicine and medical artificial intelligence
Sadée, C., Testa, S., Barba, T., Hartmann, K., Schuessler, M., Thieme, A. H., Church, G. M., Okoye, I. J., Hernandez‐Boussard, T., Hood, L., et al. (2025) · The Lancet Digital Health · doi:10.1016/j.landig.2025.02.004 · Cited by 153 · see citing works
Genet, M., Lee, L. C., Nguyen, R., Haraldsson, H., Acevedo‐Bolton, G., Zhang, Z., Ge, L., Ordovas, K. G., Kozerke, S., & Guccione, J. M. (2014) · Journal of Applied Physiology · doi:10.1152/japplphysiol.00255.2014 · Cited by 143 · see citing works
Wu, C., Lorenzo, G., Hormuth, D. A., Lima, E. A. B. F., Slavkova, K. P., DiCarlo, J. C., Virostko, J. M., Phillips, C. M., Patt, D. A., Chung, C. D., et al. (2022) · Biophysics Reviews · doi:10.1063/5.0086789 · Cited by 138 · see citing works
Pfaller, M. R., Hörmann, J. M., Weigl, M., Nagler, A., Chabiniok, R., Bertoglio, C., & Wall, W. A. (2018) · Biomechanics and Modeling in Mechanobiology · doi:10.1007/s10237-018-1098-4 · Cited by 133 · see citing works
Recreating the heart's helical structure-function relationship with focused rotary jet spinning
Chang, H., Liu, Q., Zimmerman, J. F., Lee, K. Y., Jin, Q., Peters, M. M., Rosnach, M., Choi, S., Kim, S. L., Ardoña, H. A. M., et al. (2022) · Science · doi:10.1126/science.abl6395 · Cited by 131 · see citing works
Fedele, M., Piersanti, R., Regazzoni, F., Salvador, M., Africa, P. C., Bucelli, M., Zingaro, A., Dede’, L., & Quarteroni, A. M. (2023) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2023.115983 · Cited by 126 · see citing works
Modeling cardiac muscle fibers in ventricular and atrial electrophysiology simulations
Piersanti, R., Africa, P. C., Fedele, M., Vergara, C., Dede’, L., Corno, A. F., & Quarteroni, A. M. (2020) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2020.113468 · Cited by 125 · see citing works
Moingeon, P. E., Chenel, M., Rousseau, C. F., Voisin, E., & Guedj, M. (2023) · Drug Discovery Today · doi:10.1016/j.drudis.2023.103605 · Cited by 124 · see citing works
Generating Purkinje networks in the human heart
Costabal, F. S., Hurtado, D. E., & Ellen Kuhl, A. (2015) · Journal of Biomechanics · doi:10.1016/j.jbiomech.2015.12.025 · Cited by 121 · see citing works
Strocchi, M., Augustin, C. M., Gsell, M. A. F., Karabelas, E., Neic, A., Gillette, K., Razeghi, O., Prassl, A. J., Vigmond, E. J., Behar, J. M., et al. (2020) · PLoS ONE · doi:10.1371/journal.pone.0235145 · Cited by 112 · see citing works
Bucelli, M., Zingaro, A., Africa, P. C., Fumagalli, I., Dede’, L., & Quarteroni, A. M. (2022) · International Journal for Numerical Methods in Biomedical Engineering · doi:10.1002/cnm.3678 · Cited by 102 · see citing works
Strocchi, M., Gsell, M. A. F., Augustin, C. M., Razeghi, O., Roney, C. H., Prassl, A. J., Vigmond, E. J., Behar, J. M., Gould, J., Rinaldi, C. A., et al. (2020) · Journal of Biomechanics · doi:10.1016/j.jbiomech.2020.109645 · Cited by 100 · see citing works
Samant, S., Bakhos, J. J., Wu, W. G., Zhao, S., Kassab, G. S., Khan, B. A., Panagopoulos, A. N., Makadia, J., Oguz, U. M., Banga, A., et al. (2023) · JACC: Cardiovascular Interventions · doi:10.1016/j.jcin.2023.07.022 · Cited by 98 · see citing works
GPU accelerated digital twins of the human heart open new routes for cardiovascular research
Viola, F., Corso, G. D., Paulis, R. D., & Verzicco, R. (2023) · Scientific Reports · doi:10.1038/s41598-023-34098-8 · Cited by 97 · see citing works
Modeling Pathologies of Diastolic and Systolic Heart Failure
Genet, M., Lee, L. C., Baillargeon, B. P., Guccione, J. M., & Ellen Kuhl, A. (2015) · Annals of Biomedical Engineering · doi:10.1007/s10439-015-1351-2 · Cited by 97 · see citing works
Self-citationCostabal, F. S., Matsuno, K., Jiang, Y. D., Perdikaris, P. G., & Ellen Kuhl, A. (2019) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2019.01.033 · Cited by 91 · see citing works
Principles of TAVR valve design, modelling, and testing
Rotman, O. M., Bianchi, M., Ghosh, R. P., Kovarovic, B. J., & Bluestein, D. (2018) · Expert Review of Medical Devices · doi:10.1080/17434440.2018.1536427 · Cited by 90 · see citing works
Sack, K. L., Aliotta, E., Ennis, D. B., Choy, J. S., Kassab, G. S., Guccione, J. M., & Franz, T. (2018) · Frontiers in Physiology · doi:10.3389/fphys.2018.00539 · Cited by 90 · see citing works
Kadem, M., Garber, L., Abdelkhalek, M., Al-Khazraji, B. K., & Keshavarz-Motamed, Z. (2023) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2022.3142058 · Cited by 89 · see citing works
Towards Personalized Cardiology: Multi-Scale Modeling of the Failing Heart
Kayvanpour, E., Mansi, T., Sedaghat‐Hamedani, F., Amr, A., Neumann, D., Georgescu, B., Seegerer, P., Kamen, A., Haas, J., Frese, K., et al. (2015) · PLoS ONE · doi:10.1371/journal.pone.0134869 · Cited by 87 · see citing works
Augustin, C. M., Gsell, M. A. F., Karabelas, E., Willemen, E., Prinzen, F. W., Lumens, J., Vigmond, E. J., & Plank, G. (2021) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2021.114092 · Cited by 85 · see citing works
Using machine learning to characterize heart failure across the scales
Peirlinck, M., Costabal, F. S., Sack, K. L., Choy, J. S., Kassab, G. S., Guccione, J. M., Beule, M. D., Segers, P., & Ellen Kuhl, A. (2019) · Biomechanics and Modeling in Mechanobiology · doi:10.1007/s10237-019-01190-w · Cited by 84 · see citing works
Traction force microscopy of engineered cardiac tissues
Pasqualini, F. S., Agarwal, A., O’Connor, B. B., Liu, Q., Sheehy, S. P., & Parker, K. K. (2018) · PLoS ONE · doi:10.1371/journal.pone.0194706 · Cited by 77 · see citing works
Towards continuous monitoring in personalized healthcare through digital twins
Rivera, L. F. Z., Jiménez, M., Angara, P. P., Villegas, N. M., Tamura, G., & Müller, H. A. (2019) · Conference of the Centre for Advanced Studies on Collaborative Research · Cited by 76 · see citing works
Beyond CFD: Emerging methodologies for predictive simulation in cardiovascular health and disease
Schwarz, E. L., Pegolotti, L., Pfaller, M. R., & Marsden, A. L. (2023) · Biophysics Reviews · doi:10.1063/5.0109400 · Cited by 75 · see citing works
Gao, H., Aderhold, A., Mangion, K., Luo, X.-Y., Husmeier, D., & Berry, C. (2017) · Journal of the Royal Society Interface · doi:10.1098/rsif.2017.0203 · Cited by 77 · see citing works
Data-driven modeling of the mechanical behavior of anisotropic soft biological tissue
Taç, V., Sree, V. D., Rausch, M. K., & Tepole, A. B. (2022) · Engineering With Computers · doi:10.1007/s00366-022-01733-3 · Cited by 73 · see citing works
Ghosh, R. P., Marom, G., Bianchi, M., D'Souza, K., Zietak, W., & Bluestein, D. (2020) · Biomechanics and Modeling in Mechanobiology · doi:10.1007/s10237-020-01304-9 · Cited by 72 · see citing works
Digital Twins in Personalized Medicine: Bridging Innovation and Clinical Reality
Silva, A., & Vale, N. (2025) · Journal of Personalized Medicine · doi:10.3390/jpm15110503 · Cited by 72 · see citing works
Baillargeon, B. P., Costa, I., Leach, J., Lee, L. C., Genet, M., Toutain, A., Wenk, J. F., Rausch, M. K., Rebelo, N. M. R. S., Acevedo‐Bolton, G., et al. (2015) · Cardiovascular Engineering and Technology · doi:10.1007/s13239-015-0216-z · Cited by 68 · see citing works
Self-citationOn the Simulation of Mitral Valve Function in Health, Disease, and Treatment
Sacks, M. S., Drach, A., Lee, C., Khalighi, A. H., Rego, B. V., Zhang, W., Ayoub, S., Yoganathan, A. P., Gorman, R. C., & Gorman, J. H. (2019) · Journal of Biomechanical Engineering · doi:10.1115/1.4043552 · Cited by 66 · see citing works
Image-Based Personalization of Cardiac Anatomy for Coupled Electromechanical Modeling
Crozier, A., Augustin, C. M., Neic, A., Prassl, A. J., Höller, M., Fastl, T. E., Hennemuth, A., Bredies, K., Kühne, T., Bishop, M. J., et al. (2015) · Annals of Biomedical Engineering · doi:10.1007/s10439-015-1474-5 · Cited by 65 · see citing works
Spandan, V., Meschini, V., Ostilla–Mónico, R., Lohse, D., Querzoli, G., Tullio, M. D. D., & Verzicco, R. (2017) · Journal of Computational Physics · doi:10.1016/j.jcp.2017.07.036 · Cited by 65 · see citing works
Validation and Trustworthiness of Multiscale Models of Cardiac Electrophysiology
Pathmanathan, P., & Gray, R. A. (2018) · Frontiers in Physiology · doi:10.3389/fphys.2018.00106 · Cited by 63 · see citing works
The importance of mechano-electrical feedback and inertia in cardiac electromechanics
Costabal, F. S., Concha, F., Hurtado, D. E., & Ellen Kuhl, A. (2017) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2017.03.015 · Cited by 63 · see citing works
A Multiphysics Biventricular Cardiac Model: Simulations With a Left-Ventricular Assist Device
Bakir, A. A., Abed, A. A., Stevens, M. C., Lovell, N. H., & Dokos, S. (2018) · Frontiers in Physiology · doi:10.3389/fphys.2018.01259 · Cited by 61 · see citing works
Digital twin systems for musculoskeletal applications: A current concepts review
Diniz, P., Grimm, B., Garcia, F., Fayad, J., Ley, C., Mouton, C., Oeding, J. F., Hirschmann, M. T., Samuelsson, K., & Seil, R. (2025) · Knee Surgery, Sports Traumatology, Arthroscopy · doi:10.1002/ksa.12627 · Cited by 60 · see citing works
Winter, P., & Chico, T. (2023) · Sensors · doi:10.3390/s23146333 · Cited by 60 · see citing works
Sack, K. L., Dabiri, Y., Franz, T., Solomon, S. D., Burkhoff, D., & Guccione, J. M. (2018) · Frontiers in Physiology · doi:10.3389/fphys.2018.00520 · Cited by 59 · see citing works
Biomechanics of soft biological tissues and organs, mechanobiology, homeostasis and modelling
Holzapfel, G. A., Humphrey, J. D., & Ogden, R. W. (2025) · Journal of The Royal Society Interface · doi:10.1098/rsif.2024.0361 · Cited by 58 · see citing works
Prediction of Left Ventricular Mechanics Using Machine Learning
Dabiri, Y., der Velden, A. V., Sack, K. L., Choy, J. S., Kassab, G. S., & Guccione, J. M. (2019) · Frontiers in Physics · doi:10.3389/fphy.2019.00117 · Cited by 57 · see citing works
Geometric description for the anatomy of the mitral valve: A review
Oliveira, D., Srinivasan, J., Espino, D. M., Buchan, K., Dawson, D. K., & Shepherd, D. E. T. (2020) · Journal of Anatomy · doi:10.1111/joa.13196 · Cited by 57 · see citing works
Enabling Digital Twins to Support the UN SDGs
Hassani, H., Huang, X., & MacFeely, S. (2022) · Big Data and Cognitive Computing · doi:10.3390/bdcc6040115 · Cited by 55 · see citing works
A monolithic algorithm for the simulation of cardiac electromechanics in the human left ventricle
Gerbi, A., Dede’, L., & Quarteroni, A. M. (2018) · Mathematics in Engineering · doi:10.3934/mine.2018.1.1 · Cited by 55 · see citing works
Wildeboer, R. R., Sammali, F., van Sloun, R. J. G., Huang, Y., Chen, P., Bruce, M. F., Rabotti, C., Shulepov, S. Y., Salomon, G., Schoot, B. C., et al. (2020) · IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control · doi:10.1109/tuffc.2020.2975483 · Cited by 55 · see citing works
Study of cardiovascular function using a coupled left ventricle and systemic circulation model
Chen, W. W., Gao, H., Luo, X., & Hill, N. A. (2016) · Journal of Biomechanics · doi:10.1016/j.jbiomech.2016.03.009 · Cited by 54 · see citing works
On the AIC-based model reduction for the general Holzapfel–Ogden myocardial constitutive law
Guan, D., Faizan, A., Theobald, P. S., Soe, S. P., Luo, X., & Gao, H. (2019) · Biomechanics and Modeling in Mechanobiology · doi:10.1007/s10237-019-01140-6 · Cited by 54 · see citing works
Computational systems mechanobiology of wound healing
Tepole, A. B. (2016) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2016.04.034 · Cited by 51 · see citing works
Jung, A., Gsell, M. A. F., Augustin, C. M., & Plank, G. (2022) · Mathematics · doi:10.3390/math10050823 · Cited by 50 · see citing works
Structural modelling of the cardiovascular system
Owen, B., Bojdo, N., Jivkov, A. P., Keavney, B. D., & Revell, A. J. (2018) · Biomechanics and Modeling in Mechanobiology · doi:10.1007/s10237-018-1024-9 · Cited by 50 · see citing works
Ovcharenko, E. A., Klyshnikov, K. Y., Yuzhalin, A. E., Savrasov, G. V., Коков, А. Н., Викторович, Б. А., Ganyukov, V. I., & Kudryavtseva, Y. А. (2015) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2015.12.001 · Cited by 49 · see citing works
Marx, L., Niestrawska, J. A., Gsell, M. A. F., Caforio, F., Plank, G., & Augustin, C. M. (2022) · Journal of Computational Physics · doi:10.1016/j.jcp.2022.111266 · Cited by 48 · see citing works
A virtual sizing tool for mitral valve annuloplasty
Rausch, M. K., Zöllner, A. M., Genet, M., Baillargeon, B. P., Bothe, W., & Ellen Kuhl, A. (2016) · International Journal for Numerical Methods in Biomedical Engineering · doi:10.1002/cnm.2788 · Cited by 48 · see citing works
Self-citationSack, K. L., Davies, N. H., Guccione, J. M., & Franz, T. (2016) · Heart Failure Reviews · doi:10.1007/s10741-016-9528-9 · Cited by 46 · see citing works
Predicting drug‐induced arrhythmias by multiscale modeling
Costabal, F. S., Jiang, Y. D., & Ellen Kuhl, A. (2018) · International Journal for Numerical Methods in Biomedical Engineering · doi:10.1002/cnm.2964 · Cited by 46 · see citing works
Discrete Subaortic Stenosis: Perspective Roadmap to a Complex Disease
Massé, D. D., Shar, J. A., Brown, K. N., Keswani, S. G., Grande-Allen, K. J., & Sucosky, P. (2018) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2018.00122 · Cited by 46 · see citing works
Sex Differences in Drug-Induced Arrhythmogenesis
Peirlinck, M., Costabal, F. S., & Ellen Kuhl, A. (2021) · Frontiers in Physiology · doi:10.3389/fphys.2021.708435 · Cited by 45 · see citing works
Automated model discovery for human cardiac tissue: Discovering the best model and parameters
Martonová, D., Peirlinck, M., Linka, K., Holzapfel, G. A., Leyendecker, S., & Ellen Kuhl, A. (2024) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2024.117078 · Cited by 45 · see citing works
A machine learning model to estimate myocardial stiffness from EDPVR
Babaei, H., Mendiola, E. A., Neelakantan, S., Xiang, Q., Vang, A., Dixon, R. A. F., Shah, D. J., Vanderslice, P., Choudhary, G., & Avazmohammadi, R. (2022) · Scientific Reports · doi:10.1038/s41598-022-09128-6 · Cited by 44 · see citing works
Khodaei, S., Henstock, A., Sadeghi, R., Sellers, S., Blanke, P., Leipsic, J. A., Emadi, A., & Keshavarz‐Motamed, Z. (2021) · Scientific Reports · doi:10.1038/s41598-021-85500-2 · Cited by 44 · see citing works
In vivo estimation of passive biomechanical properties of human myocardium
Palit, A., Bhudia, S. K., Arvanitis, T. N., Turley, G. A., & Williams, M. A. (2018) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-017-1768-x · Cited by 43 · see citing works
Patient-Specific Airway Wall Remodeling in Chronic Lung Disease
Eskandari, M., Kuschner, W. G., & Ellen Kuhl, A. (2015) · Annals of Biomedical Engineering · doi:10.1007/s10439-015-1306-7 · Cited by 42 · see citing works
Lee, P., Calvo, C. J., Alfonso-Almazán, J. M., Quintanilla, J. G., Chorro, F. J., Yan, P., Loew, L. M., Rama, D. F., & Millet, J. (2017) · Scientific Reports · doi:10.1038/srep43217 · Cited by 42 · see citing works
Achille, P. D., Harouni, A., Khamzin, S., Solovyova, O. E., Rice, J. J., & Gurev, V. (2018) · Frontiers in Physiology · doi:10.3389/fphys.2018.01002 · Cited by 40 · see citing works
Rosalia, L., Öztürk, Ç., Coll‐Font, J., Fan, Y., Nagata, Y., Singh, M., Goswami, D., Mauskapf, A., Chen, S., Eder, R. A., et al. (2022) · Nature Biomedical Engineering · doi:10.1038/s41551-022-00937-8 · Cited by 40 · see citing works
Peirlinck, M., Beule, M. D., Segers, P., & Rebelo, N. M. R. S. (2018) · Journal of the mechanical behavior of biomedical materials/Journal of mechanical behavior of biomedical materials · doi:10.1016/j.jmbbm.2018.05.032 · Cited by 38 · see citing works
Self-citationA fast computational model for the electrophysiology of the whole human heart
Corso, G. D., Verzicco, R., & Viola, F. (2022) · Journal of Computational Physics · doi:10.1016/j.jcp.2022.111084 · Cited by 38 · see citing works
Bappy, M. A. (2024) · American Journal of Science and Learning for Development · doi:10.51699/ajsld.v3i2.3459 · Cited by 37 · see citing works
Whole-heart modelling with valves in a fluid–structure interaction framework
Feng, L., Gao, H., & Luo, X. (2024) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2023.116724 · Cited by 36 · see citing works
Quest for cardiovascular interventions: precise modeling and 3D printing of heart valves
Vashistha, R., Kumar, P., Dangi, A. K., Sharma, N., Chhabra, D., & Shukla, P. (2019) · Journal of Biological Engineering · doi:10.1186/s13036-018-0132-5 · Cited by 36 · see citing works
Classifying Drugs by their Arrhythmogenic Risk Using Machine Learning
Costabal, F. S., Seo, K., Ashley, E. A., & Ellen Kuhl, A. (2020) · Biophysical Journal · doi:10.1016/j.bpj.2020.01.012 · Cited by 36 · see citing works
Kinematic boundary conditions substantially impact in silico ventricular function
Peirlinck, M., Sack, K. L., BACKER, P. D., Morais, P., Segers, P., Franz, T., & Beule, M. D. (2018) · International Journal for Numerical Methods in Biomedical Engineering · doi:10.1002/cnm.3151 · Cited by 35 · see citing works
Multipatch Isogeometric Analysis for electrophysiology: Simulation in a human heart
Bucelli, M., Salvador, M., Dede’, L., & Quarteroni, A. M. (2021) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2021.113666 · Cited by 35 · see citing works
Urban, M. W., Qiang, B., Song, P., Nenadic, I., Chen, S., & Greenleaf, J. F. (2015) · Physics in Medicine and Biology · doi:10.1088/0031-9155/61/1/365 · Cited by 34 · see citing works
Computational modeling of acute myocardial infarction
Sáez, P., & Ellen Kuhl, A. (2015) · Computer Methods in Biomechanics & Biomedical Engineering · doi:10.1080/10255842.2015.1105965 · Cited by 33 · see citing works
Simulating cardiac fluid dynamics in the human heart
Davey, M., Puelz, C., Rossi, S., Smith, M. A., Wells, D., Sturgeon, G. M., Segars, W. P., Vavalle, J. P., Peskin, C. S., & Griffith, B. E. (2024) · PNAS Nexus · doi:10.1093/pnasnexus/pgae392 · Cited by 32 · see citing works
Gao, H., Mangion, K., Carrick, D., Husmeier, D., Luo, X.-Y., & Berry, C. (2017) · Scientific Reports · doi:10.1038/s41598-017-13635-2 · Cited by 26 · see citing works
Epistemic Rights and Responsibilities of Digital Simulacra for Biomedicine
Cho, M. K., & Martinez‐Martin, N. (2022) · The American Journal of Bioethics · doi:10.1080/15265161.2022.2146785 · Cited by 30 · see citing works
Computational Modeling of Healthy Myocardium in Diastole
Nikou, A., Dorsey, S. M., McGarvey, J. R., Gorman, J. H., Burdick, J. A., Pilla, J. J., Gorman, R. C., & Wenk, J. F. (2015) · Annals of Biomedical Engineering · doi:10.1007/s10439-015-1403-7 · Cited by 29 · see citing works
Sack, K. L., Baillargeon, B. P., Acevedo‐Bolton, G., Genet, M., Rebelo, N. M. R. S., Ellen Kuhl, A., Klein, L., Weiselthaler, G. M., Burkhoff, D., Franz, T., et al. (2016) · The International Journal of Artificial Organs · doi:10.5301/ijao.5000520 · Cited by 29 · see citing works
Self-citationDabiri, Y., Sack, K. L., Shaul, S., Sengupta, P. P., & Guccione, J. M. (2018) · Frontiers in Physiology · doi:10.3389/fphys.2018.01003 · Cited by 29 · see citing works
Puniya, B. L. (2025) · Journal of Molecular Biology · doi:10.1016/j.jmb.2025.169181 · Cited by 29 · see citing works
Lazarus, A., Dalton, D., Husmeier, D., & Gao, H. (2022) · Biomechanics and Modeling in Mechanobiology · doi:10.1007/s10237-022-01571-8 · Cited by 28 · see citing works
A Short Review of Advances in the Modelling of Blood Rheology and Clot Formation
Anand, M., & Rajagopal, K. R. (2017) · Fluids · doi:10.3390/fluids2030035 · Cited by 28 · see citing works
Bridging silicon and carbon worlds with digital twins and on-chip systems in drug discovery
Akbarialiabad, H., Seyyedi, M. S., Paydar, S., Habibzadeh, A., Haghighi, A., & Kvedar, J. C. (2024) · npj Systems Biology and Applications · doi:10.1038/s41540-024-00476-9 · Cited by 27 · see citing works
- Authors
- Baillargeon, B., Rebelo, N., Fox, D. D., Taylor, R. L., & Kuhl, E.
- Affiliations
- Dassault Systèmes (United States) (US)
- ORCID
- Kuhl, Ellen
- Venue
- European Journal of Mechanics - A/Solids
- Publisher
- Elsevier BV
- Published
- 2014-11
- Volume
- 48
- Pages
- 38-47
- ISSN
- 0997-7538 (ISSN-L)
- PMC
- PMC4175454
- OpenAlex
- W2131414969
- Cited by
- 370
- References
- 53
- Citations by year
- 2026: 33 · 2025: 38 · 2024: 40 · 2023: 37 · 2022: 43 · 2021: 38 · 2020: 29 · 2019: 27 · 2018: 34 · 2017: 18 · 2016: 12 · 2015: 17 · 2014: 4
- FWCI
- 8.82
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- European Journal of Mechanics - A/Solids (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (U54 GM072970)
- National Science Foundation (CMMI 0952021, 1233054)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Elasticity and Material Modeling
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiac electrophysiology and arrhythmias
- Physical Sciences › Engineering › Computational Mechanics › Advanced Numerical Methods in Computational Mathematics
- Keywords
- electromechanical coupling, excitation-contraction coupling, cardiac simulation, cardiac cycle, pressure-volume relationship, heart valves, magnetic resonance imaging, finite element model, heart disease, valve stenosis, valvular regurgitation, mitral valve prolapse
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review