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Bibliography 211
Bibliography
1. M. Grieves, Digital Twin: Manufacturing Mastering the game of go with deep neural
Excellence Through Virtual Factory Replication, networks and tree search, Nature 529 (7587) (2016)
White paper, 2014, pp. 1–7. 484.
2. E. Glaessgen, D. Stargel, The digital twin paradigm 12. A. Hodgkin, A. Huxley, A quantitative description
for future NASA and us air force vehicles, in: 53rd of ion currents and its applications to conduction
AIAA/ASME/ASCE/AHS/ASC Structures, and excitation in nerve membranes, Journal of
Structural Dynamics and Materials Conference Physiology 117 (4) (1952) 500–544.
20th AIAA/ASME/AHS Adaptive Structures 13. K. Ten Tusscher, D. Noble, P.-J. Noble, A.V. Panfilov,
Conference 14th AIAA, 2012, p. 1818. A model for human ventricular tissue, American
3. M. Andrychowicz,B.Baker,M.Chociej,R. Journal of Physiology. Heart and Circulatory
Jozefowicz, B. McGrew, J. Pachocki, A. Petron, M. Physiology 286 (4) (2004) H1573–H1589.
Plappert, G. Powell, A. Ray, et al., Learning 14. J.J. Rice, P.P. De Tombe, Approaches to modeling
dexterous in-hand manipulation, arXiv preprint, crossbridges and calcium-dependent activation in
arXiv:1808.00177, 2018. cardiac muscle, Progress in Biophysics and
4. D. Comaniciu, K. Engel, B. Georgescu, T. Mansi, Molecular Biology 85 (2–3) (2004) 179–195.
Shaping the Future Through Innovations: From 15. D.M. Bers, Cardiac excitation–contraction
Medical Imaging to Precision Medicine, 2016. coupling, Nature 415 (6868) (2002) 198.
16. D.A. Beard, Modeling of oxygen transport and
5. N.A. Trayanova, P.M. Boyle, P.P. Nikolov, cellular energetics explains observations on in
Personalized imaging and modeling strategies for vivo cardiac energy metabolism, PLoS
arrhythmia prevention and therapy, Current Computational Biology 2 (9) (2006) e107.
Opinion in Biomedical Engineering 5 (2018) 17. C.M. Lloyd, J.R. Lawson, P.J. Hunter, P.F. Nielsen,
21–28. The cellml model repository, Bioinformatics
6. E. Kayvanpour, T. Mansi, F. Sedaghat-Hamedani, 24 (18) (2008) 2122–2123.
A. Amr, D. Neumann, B. Georgescu, P. Seegerer, A.
18. M. Sermesant, R. Chabiniok, P. Chinchapatnam, T.
Kamen, J. Haas,K.S.Frese,M.Irawati,E.Wirsz,V.
Mansi, F. Billet, P. Moireau, J.-M. Peyrat, K. Wong, J.
King, S. Buss, D. Mereles, E. Zitron, A. Keller, H.A. Relan, K. Rhode, et al., Patient-specific
Katus, D. Comaniciu, B. Meder, Towards electromechanical models of the heart for the
personalized cardiology: multi-scale modeling of prediction of pacing acute effects in crt: a
the failing heart, PLoS ONE 10 (7) (2015) 1–18. preliminary clinical validation, Medical Image
7. P.J. Hunter, T.K. Borg, Integration from proteins to Analysis 16 (1) (2012) 201–215.
organs: the physiome project, Nature Reviews. 19. A.Prakosa,H.J.Arevalo,D.Deng, P.M. Boyle, P.P.
Molecular Cell Biology 4 (3) (2003) 237. Nikolov, H. Ashikaga, J.J. Blauer, E. Ghafoori, C.J.
8. H. Ashikaga, H. Arevalo, F. Vadakkumpadan, R.C. Park, R.C. Blake, et al., Personalized virtual-heart
Blake III, J.D. Bayer, S. Nazarian, M.M. Zviman, H. technology for guiding the ablation of
Tandri, R.D. Berger, H. Calkins, et al., Feasibility of infarct-related ventricular tachycardia, Nature
image-based simulation to estimate ablation Biomedical Engineering 2 (10) (2018) 732.
target in human ventricular arrhythmia, Heart 20. N. Cedilnik, J. Duchateau, R. Dubois, F. Sacher, P.
Rhythm 10 (8) (2013) 1109–1116. Jaïs, H. Cochet, M. Sermesant, Fast personalized
9. A.W. Lee, C.M. Costa, M. Strocchi, C.A. Rinaldi, electrophysiological models from computed
S.A. Niederer, Computational modeling for cardiac tomography images for ventricular tachycardia
resynchronization therapy, Journal of ablation planning, EP Europace 20 (suppl 3)
Cardiovascular Translational Research 11 (2) (2018), iii94–iii101.
(2018) 92–108. 21. P.M.Boyle,T.Zghaib, S. Zahid, R.L. Ali, D. Deng,
10. M.Tegmark,Life3.0:Being Humaninthe Ageof W.H. Franceschi, J.B. Hakim, M.J. Murphy, A.
Artificial Intelligence, Knopf, 2017. Prakosa, S.L. Zimmerman, et al., Computationally
11. D. Silver, A. Huang, C.J. Maddison, A. Guez, L. guided personalized targeted ablation of
Sifre, G. Van Den Driessche, J. Schrittwieser, I. persistent atrial fibrillation, Nature Biomedical
Antonoglou, V. Panneershelvam, M. Lanctot, et al., Engineering (2019) 1–10.