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For: Lee AW, Crozier A, Hyde ER, Lamata P, Truong M, Sohal M, Jackson T, Behar JM, Claridge S, Shetty A, Sammut E, Plank G, Rinaldi CA, Niederer S. Biophysical Modeling to Determine the Optimization of Left Ventricular Pacing Site and AV/VV Delays in the Acute and Chronic Phase of Cardiac Resynchronization Therapy. J Cardiovasc Electrophysiol 2017;28:208-15. [PMID: 27885749 DOI: 10.1111/jce.13134] [Cited by in Crossref: 16] [Cited by in F6Publishing: 12] [Article Influence: 3.2] [Reference Citation Analysis]
Number Citing Articles
1 Menshykau D, Tanaka S. Mechanistic Image-Based Modelling: Concepts and Applications. Handb Exp Pharmacol 2019;260:231-61. [PMID: 31823072 DOI: 10.1007/164_2019_328] [Cited by in Crossref: 1] [Article Influence: 0.3] [Reference Citation Analysis]
2 Niederer SA, Campbell KS, Campbell SG. A short history of the development of mathematical models of cardiac mechanics. J Mol Cell Cardiol 2019;127:11-9. [PMID: 30503754 DOI: 10.1016/j.yjmcc.2018.11.015] [Cited by in Crossref: 12] [Cited by in F6Publishing: 11] [Article Influence: 3.0] [Reference Citation Analysis]
3 Ushenin K, Kalinin V, Gitinova S, Sopov O, Solovyova O. Parameter variations in personalized electrophysiological models of human heart ventricles. PLoS One 2021;16:e0249062. [PMID: 33909606 DOI: 10.1371/journal.pone.0249062] [Reference Citation Analysis]
4 Jackson T, Claridge S, Behar J, Sieniewicz B, Gould J, Porter B, Sidhu B, Yao C, Lee A, Niederer S, Rinaldi CA. Differential effect with septal and apical RV pacing on ventricular activation in patients with left bundle branch block assessed by non-invasive electrical imaging and in silico modelling. J Interv Card Electrophysiol 2020;57:115-23. [PMID: 31201592 DOI: 10.1007/s10840-019-00567-2] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
5 Sieniewicz BJ, Gould J, Porter B, Sidhu BS, Behar JM, Claridge S, Niederer S, Rinaldi CA. Optimal site selection and image fusion guidance technology to facilitate cardiac resynchronization therapy. Expert Rev Med Devices 2018;15:555-70. [PMID: 30019954 DOI: 10.1080/17434440.2018.1502084] [Cited by in Crossref: 5] [Cited by in F6Publishing: 2] [Article Influence: 1.3] [Reference Citation Analysis]
6 Niederer SA, Lumens J, Trayanova NA. Computational models in cardiology. Nat Rev Cardiol 2019;16:100-11. [PMID: 30361497 DOI: 10.1038/s41569-018-0104-y] [Cited by in Crossref: 96] [Cited by in F6Publishing: 73] [Article Influence: 48.0] [Reference Citation Analysis]
7 Infante T, Francone M, De Rimini ML, Cavaliere C, Canonico R, Catalano C, Napoli C. Machine learning and network medicine: a novel approach for precision medicine and personalized therapy in cardiomyopathies. Journal of Cardiovascular Medicine 2021;22:429-40. [DOI: 10.2459/jcm.0000000000001103] [Cited by in Crossref: 2] [Article Influence: 1.0] [Reference Citation Analysis]
8 Carpio EF, Gomez JF, Sebastian R, Lopez-Perez A, Castellanos E, Almendral J, Ferrero JM, Trenor B. Optimization of Lead Placement in the Right Ventricle During Cardiac Resynchronization Therapy. A Simulation Study. Front Physiol 2019;10:74. [PMID: 30804805 DOI: 10.3389/fphys.2019.00074] [Cited by in Crossref: 6] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
9 Fan L, Choy JS, Raissi F, Kassab GS, Lee LC. Optimization of cardiac resynchronization therapy based on a cardiac electromechanics-perfusion computational model. Comput Biol Med 2021;:105050. [PMID: 34823858 DOI: 10.1016/j.compbiomed.2021.105050] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
10 Corrado C, Avezzù A, Lee AWC, Mendoca Costa C, Roney CH, Strocchi M, Bishop M, Niederer SA. Using cardiac ionic cell models to interpret clinical data. Wiley Interdiscip Rev Syst Biol Med 2020;:e1508. [PMID: 33027553 DOI: 10.1002/wsbm.1508] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
11 Lee AWC, Nguyen UC, Razeghi O, Gould J, Sidhu BS, Sieniewicz B, Behar J, Mafi-Rad M, Plank G, Prinzen FW, Rinaldi CA, Vernooy K, Niederer S. A rule-based method for predicting the electrical activation of the heart with cardiac resynchronization therapy from non-invasive clinical data. Med Image Anal 2019;57:197-213. [PMID: 31326854 DOI: 10.1016/j.media.2019.06.017] [Cited by in Crossref: 12] [Cited by in F6Publishing: 9] [Article Influence: 4.0] [Reference Citation Analysis]
12 Park J, Wu Z, Steiner PR, Zhu B, Zhang JXJ. Heart-on-Chip for Combined Cellular Dynamics Measurements and Computational Modeling Towards Clinical Applications. Ann Biomed Eng 2022. [PMID: 35039976 DOI: 10.1007/s10439-022-02902-7] [Reference Citation Analysis]
13 O'Regan DP. Putting machine learning into motion: applications in cardiovascular imaging. Clin Radiol 2020;75:33-7. [PMID: 31079952 DOI: 10.1016/j.crad.2019.04.008] [Cited by in Crossref: 7] [Cited by in F6Publishing: 3] [Article Influence: 2.3] [Reference Citation Analysis]
14 Lee AWC, Costa CM, Strocchi M, Rinaldi CA, Niederer SA. Computational Modeling for Cardiac Resynchronization Therapy. J Cardiovasc Transl Res 2018;11:92-108. [PMID: 29327314 DOI: 10.1007/s12265-017-9779-4] [Cited by in Crossref: 23] [Cited by in F6Publishing: 14] [Article Influence: 5.8] [Reference Citation Analysis]