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For: Morrison TM, Pathmanathan P, Adwan M, Margerrison E. Advancing Regulatory Science With Computational Modeling for Medical Devices at the FDA's Office of Science and Engineering Laboratories. Front Med (Lausanne) 2018;5:241. [PMID: 30356350 DOI: 10.3389/fmed.2018.00241] [Cited by in Crossref: 60] [Cited by in F6Publishing: 65] [Article Influence: 15.0] [Reference Citation Analysis]
Number Citing Articles
1 Febrer-nafría M, Nasr A, Ezati M, Brown P, Font-llagunes JM, Mcphee J. Predictive multibody dynamic simulation of human neuromusculoskeletal systems: a review. Multibody Syst Dyn 2022. [DOI: 10.1007/s11044-022-09852-x] [Reference Citation Analysis]
2 Rowald A, Amft O. A computational roadmap to electronic drugs. Front Neurorobot 2022;16. [DOI: 10.3389/fnbot.2022.983072] [Reference Citation Analysis]
3 Baumann AP, Hsieh M, Dmitriev AE, Lotz JC. The relative influence of model parameters on finite element analysis simulations of intervertebral body fusion device static compression performance. Computer Methods in Biomechanics and Biomedical Engineering 2022. [DOI: 10.1080/10255842.2022.2139145] [Reference Citation Analysis]
4 Fresiello L, Muthiah K, Goetschalckx K, Hayward C, Rocchi M, Bezy M, Pauls JP, Meyns B, Donker DW, Zieliński K. Initial clinical validation of a hybrid in silico—in vitro cardiorespiratory simulator for comprehensive testing of mechanical circulatory support systems. Front Physiol 2022;13:967449. [DOI: 10.3389/fphys.2022.967449] [Reference Citation Analysis]
5 Gacek E, Mahutga RR, Barocas VH. Hybrid Discrete-Continuum Multiscale Model of Tissue Growth and Remodeling. Acta Biomater 2022:S1742-7061(22)00600-6. [PMID: 36155097 DOI: 10.1016/j.actbio.2022.09.040] [Reference Citation Analysis]
6 Wellnhofer E. Real-World and Regulatory Perspectives of Artificial Intelligence in Cardiovascular Imaging. Front Cardiovasc Med 2022;9. [DOI: 10.3389/fcvm.2022.890809] [Reference Citation Analysis]
7 Phillips C, Kortschot M, Azhari F. Towards standardizing the preparation of test specimens made with material extrusion: Review of current techniques for tensile testing. Additive Manufacturing 2022. [DOI: 10.1016/j.addma.2022.103050] [Reference Citation Analysis]
8 Nagaraja S, Paranjape HM, Cheng CP. Durability of Nitinol Cardiovascular Devices. Shap Mem Superelasticity 2022;8:40-4. [DOI: 10.1007/s40830-022-00370-5] [Reference Citation Analysis]
9 Van Hoof L, Verbrugghe P, Jones EAV, Humphrey JD, Janssens S, Famaey N, Rega F. Understanding Pulmonary Autograft Remodeling After the Ross Procedure: Stick to the Facts. Front Cardiovasc Med 2022;9:829120. [DOI: 10.3389/fcvm.2022.829120] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
10 Collin CB, Gebhardt T, Golebiewski M, Karaderi T, Hillemanns M, Khan FM, Salehzadeh-yazdi A, Kirschner M, Krobitsch S, Kuepfer L; EU-STANDS4PM consortium. Computational Models for Clinical Applications in Personalized Medicine—Guidelines and Recommendations for Data Integration and Model Validation. JPM 2022;12:166. [DOI: 10.3390/jpm12020166] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
11 Kalozoumis PG, Marino M, Carniel EL, Iakovidis DK. Towards the Development of a Digital Twin for Endoscopic Medical Device Testing. Studies in Systems, Decision and Control 2022. [DOI: 10.1007/978-3-030-96802-1_7] [Reference Citation Analysis]
12 Geris L. Realizing Personalized Medicine Using In Silico Tools: A Community Effort. Personalized Medicine in the Making 2022. [DOI: 10.1007/978-3-030-74804-3_10] [Reference Citation Analysis]
13 Dong Q, Zhao C, Tian M. Study on Heterogeneous Model Framework Library for Complex System Modeling. Lecture Notes in Electrical Engineering 2022. [DOI: 10.1007/978-981-19-6226-4_85] [Reference Citation Analysis]
14 Anagnostakou V, Epshtein M, Kühn AL, King RM, Puri A, Gounis MJ. Preclinical modeling of mechanical thrombectomy. J Biomech 2022;130:110894. [PMID: 34915309 DOI: 10.1016/j.jbiomech.2021.110894] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
15 Cockrell C, Ozik J, Collier N, An G. Nested active learning for efficient model contextualization and parameterization: pathway to generating simulated populations using multi-scale computational models. Simulation 2021;97:287-96. [PMID: 34744189 DOI: 10.1177/0037549720975075] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 5.0] [Reference Citation Analysis]
16 Inkol KA, Mcphee J. Simulating Human Upper and Lower Limb Balance Recovery Responses Using Nonlinear Model Predictive Control. 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) 2021. [DOI: 10.1109/embc46164.2021.9630208] [Reference Citation Analysis]
17 Riebel LL, Passini E, Margara F, Paci M, Biasetti J, Rodriguez B. In Silico Identification of the Key Ionic Currents Modulating Human Pluripotent Stem Cell-Derived Cardiomyocytes Towards an Adult Phenotype. 2021 Computing in Cardiology (CinC) 2021. [DOI: 10.23919/cinc53138.2021.9662683] [Reference Citation Analysis]
18 Carbonaro D, Gallo D, Morbiducci U, Audenino A, Chiastra C. In silico biomechanical design of the metal frame of transcatheter aortic valves: multi-objective shape and cross-sectional size optimization. Struct Multidisc Optim 2021;64:1825-42. [DOI: 10.1007/s00158-021-02944-w] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
19 Niederer SA, Sacks MS, Girolami M, Willcox K. Scaling digital twins from the artisanal to the industrial. Nat Comput Sci 2021;1:313-20. [DOI: 10.1038/s43588-021-00072-5] [Cited by in Crossref: 23] [Cited by in F6Publishing: 38] [Article Influence: 23.0] [Reference Citation Analysis]
20 Corral-Acero J, Margara F, Marciniak M, Rodero C, Loncaric F, Feng Y, Gilbert A, Fernandes JF, Bukhari HA, Wajdan A, Martinez MV, Santos MS, Shamohammdi M, Luo H, Westphal P, Leeson P, DiAchille P, Gurev V, Mayr M, Geris L, Pathmanathan P, Morrison T, Cornelussen R, Prinzen F, Delhaas T, Doltra A, Sitges M, Vigmond EJ, Zacur E, Grau V, Rodriguez B, Remme EW, Niederer S, Mortier P, McLeod K, Potse M, Pueyo E, Bueno-Orovio A, Lamata P. The 'Digital Twin' to enable the vision of precision cardiology. Eur Heart J. 2020;41:4556-4564. [PMID: 32128588 DOI: 10.1093/eurheartj/ehaa159] [Cited by in Crossref: 154] [Cited by in F6Publishing: 164] [Article Influence: 154.0] [Reference Citation Analysis]
21 Arima H, Kano S. Integrated Analytical Framework for the Development of Artificial Intelligence-Based Medical Devices. Ther Innov Regul Sci 2021;55:853-65. [PMID: 33876397 DOI: 10.1007/s43441-021-00292-x] [Reference Citation Analysis]
22 Fregly BJ. A Conceptual Blueprint for Making Neuromusculoskeletal Models Clinically Useful. Applied Sciences 2021;11:2037. [DOI: 10.3390/app11052037] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 7.0] [Reference Citation Analysis]
23 Baumann AP, Graf T, Peck JH, Dmitriev AE, Coughlan D, Lotz JC. Assessing the use of finite element analysis for mechanical performance evaluation of intervertebral body fusion devices. JOR Spine 2021;4:e1137. [PMID: 33778409 DOI: 10.1002/jsp2.1137] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
24 An G, Vodovotz Y. The Rationale and Implementation of Model-Based Precision Medicine for Inflammatory Diseases. Complex Systems and Computational Biology Approaches to Acute Inflammation 2021. [DOI: 10.1007/978-3-030-56510-7_16] [Reference Citation Analysis]
25 Elmeliegy M, Ghobrial O. Model-informed drug development and discovery: an overview of current practices. Remington 2021. [DOI: 10.1016/b978-0-12-820007-0.00014-3] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
26 Elezović A, Cvijić S, Elezović A, Pilipović S, Parojčić J. Particle Deposition in Respiratory Tract: Where are the Limits? IFMBE Proceedings 2021. [DOI: 10.1007/978-3-030-73909-6_74] [Reference Citation Analysis]
27 Siu R, Abbas JJ, Fuller DD, Gomes J, Renaud S, Jung R. Autonomous control of ventilation through closed-loop adaptive respiratory pacing. Sci Rep 2020;10:21903. [PMID: 33318547 DOI: 10.1038/s41598-020-78834-w] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
28 Pathmanathan P, Galappaththige SK, Cordeiro JM, Kaboudian A, Fenton FH, Gray RA. Data-Driven Uncertainty Quantification for Cardiac Electrophysiological Models: Impact of Physiological Variability on Action Potential and Spiral Wave Dynamics. Front Physiol 2020;11:585400. [PMID: 33329034 DOI: 10.3389/fphys.2020.585400] [Cited by in Crossref: 7] [Cited by in F6Publishing: 8] [Article Influence: 3.5] [Reference Citation Analysis]
29 Lamb R, Hand B, Kavner A. Computational Modeling of the Effects of the Science Writing Heuristic on Student Critical Thinking in Science Using Machine Learning. J Sci Educ Technol 2021;30:283-97. [DOI: 10.1007/s10956-020-09871-3] [Cited by in Crossref: 7] [Cited by in F6Publishing: 4] [Article Influence: 3.5] [Reference Citation Analysis]
30 Redaelli A, Votta E. Cardiovascular patient-specific modeling: Where are we now and what does the future look like? APL Bioeng 2020;4:040401. [PMID: 33195957 DOI: 10.1063/5.0031452] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 1.5] [Reference Citation Analysis]
31 Akhlaghi N, Pfefer TJ, Wear KA, Garra BS, Vogt WC. Multidomain computational modeling of photoacoustic imaging: verification, validation, and image quality prediction. J Biomed Opt 2019;24:1-12. [PMID: 31705636 DOI: 10.1117/1.JBO.24.12.121910] [Cited by in Crossref: 3] [Cited by in F6Publishing: 5] [Article Influence: 1.5] [Reference Citation Analysis]
32 Affatato S, Ruggiero A. A Perspective on Biotribology in Arthroplasty: From In Vitro toward the Accurate In Silico Wear Prediction. Applied Sciences 2020;10:6312. [DOI: 10.3390/app10186312] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 2.5] [Reference Citation Analysis]
33 de Jaegere P, de Ronde M, den Heijer P, Weger A, Baan J. The history of transcatheter aortic valve implantation: The role and contribution of an early believer and adopter, the Netherlands. Neth Heart J 2020;28:128-35. [PMID: 32780343 DOI: 10.1007/s12471-020-01468-0] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
34 Brunak S, Bjerre Collin C, Eva Ó Cathaoir K, Golebiewski M, Kirschner M, Kockum I, Moser H, Waltemath D. Towards standardization guidelines for in silico approaches in personalized medicine. J Integr Bioinform 2020;17. [PMID: 32827396 DOI: 10.1515/jib-2020-0006] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 2.5] [Reference Citation Analysis]
35 Margara F, Wang ZJ, Levrero-Florencio F, Santiago A, Vázquez M, Bueno-Orovio A, Rodriguez B. In-silico human electro-mechanical ventricular modelling and simulation for drug-induced pro-arrhythmia and inotropic risk assessment. Prog Biophys Mol Biol 2021;159:58-74. [PMID: 32710902 DOI: 10.1016/j.pbiomolbio.2020.06.007] [Cited by in Crossref: 32] [Cited by in F6Publishing: 33] [Article Influence: 16.0] [Reference Citation Analysis]
36 Kozlov M, Horner M, Kainz W. Modeling radiofrequency responses of realistic multi-electrode leads containing helical and straight wires. MAGMA 2020;33:421-37. [PMID: 31745756 DOI: 10.1007/s10334-019-00793-9] [Cited by in Crossref: 4] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
37 Clayton RH, Aboelkassem Y, Cantwell CD, Corrado C, Delhaas T, Huberts W, Lei CL, Ni H, Panfilov AV, Roney C, Dos Santos RW. An audit of uncertainty in multi-scale cardiac electrophysiology models. Philos Trans A Math Phys Eng Sci 2020;378:20190335. [PMID: 32448070 DOI: 10.1098/rsta.2019.0335] [Cited by in Crossref: 15] [Cited by in F6Publishing: 15] [Article Influence: 7.5] [Reference Citation Analysis]
38 Capogrosso M, Lempka SF. A computational outlook on neurostimulation. Bioelectron Med 2020;6:10. [PMID: 32490037 DOI: 10.1186/s42234-020-00047-3] [Cited by in Crossref: 11] [Cited by in F6Publishing: 12] [Article Influence: 5.5] [Reference Citation Analysis]
39 Niederer SA, Aboelkassem Y, Cantwell CD, Corrado C, Coveney S, Cherry EM, Delhaas T, Fenton FH, Panfilov AV, Pathmanathan P, Plank G, Riabiz M, Roney CH, Dos Santos RW, Wang L. Creation and application of virtual patient cohorts of heart models. Philos Trans A Math Phys Eng Sci 2020;378:20190558. [PMID: 32448064 DOI: 10.1098/rsta.2019.0558] [Cited by in Crossref: 24] [Cited by in F6Publishing: 28] [Article Influence: 12.0] [Reference Citation Analysis]
40 van Riel N, Müller R, Dall’ara E. The Digital Mouse: why computational modelling of mouse models of disease can improve translation.. [DOI: 10.1101/2020.05.04.075812] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
41 Marom G, Einav S. New Insights into Valve Hemodynamics. Rambam Maimonides Med J 2020;11. [PMID: 32374253 DOI: 10.5041/RMMJ.10400] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
42 Erdemir A, Besier TF, Halloran JP, Imhauser CW, Laz PJ, Morrison TM, Shelburne KB. Deciphering the "Art" in Modeling and Simulation of the Knee Joint: Overall Strategy. J Biomech Eng 2019;141. [PMID: 31166589 DOI: 10.1115/1.4043346] [Cited by in Crossref: 19] [Cited by in F6Publishing: 22] [Article Influence: 9.5] [Reference Citation Analysis]
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44 Nishimura T, Shimojo Y, Awazu K. Computer-aided Regulatory Science for Laser Medicine. JJSLSM 2020;41:37-43. [DOI: 10.2530/jslsm.jslsm-41_0001] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
45 Morrison TM, Hariharan P, Funkhouser CM, Afshari P, Goodin M, Horner M. Assessing Computational Model Credibility Using a Risk-Based Framework: Application to Hemolysis in Centrifugal Blood Pumps. ASAIO J 2019;65:349-60. [PMID: 30973403 DOI: 10.1097/MAT.0000000000000996] [Cited by in Crossref: 27] [Cited by in F6Publishing: 29] [Article Influence: 13.5] [Reference Citation Analysis]
46 Mengoni M. Using inverse finite element analysis to identify spinal tissue behaviour in situ. Methods 2021;185:105-9. [PMID: 32036039 DOI: 10.1016/j.ymeth.2020.02.004] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
47 Nishimura T, Shimojo Y, Hazama H, Awazu K. A Method of Computational Clinical Trial of a Nanosecond Pulsed Laser Skin Treatment Device by Numerical Simulation of Photothermal Damage. JJSLSM 2020;40:301-308. [DOI: 10.2530/jslsm.jslsm-40_0050] [Reference Citation Analysis]
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50 Sutanto H, Laudy L, Clerx M, Dobrev D, Crijns HJ, Heijman J. Maastricht antiarrhythmic drug evaluator (MANTA): A computational tool for better understanding of antiarrhythmic drugs. Pharmacological Research 2019;148:104444. [DOI: 10.1016/j.phrs.2019.104444] [Cited by in Crossref: 16] [Cited by in F6Publishing: 10] [Article Influence: 5.3] [Reference Citation Analysis]
51 Pitto L, Kainz H, Falisse A, Wesseling M, Van Rossom S, Hoang H, Papageorgiou E, Hallemans A, Desloovere K, Molenaers G, Van Campenhout A, De Groote F, Jonkers I. SimCP: A Simulation Platform to Predict Gait Performance Following Orthopedic Intervention in Children With Cerebral Palsy. Front Neurorobot 2019;13:54. [PMID: 31379550 DOI: 10.3389/fnbot.2019.00054] [Cited by in Crossref: 19] [Cited by in F6Publishing: 19] [Article Influence: 6.3] [Reference Citation Analysis]
52 Bader D, Worsley P, Gefen A. Bioengineering considerations in the prevention of medical device-related pressure ulcers. Clinical Biomechanics 2019;67:70-7. [DOI: 10.1016/j.clinbiomech.2019.04.018] [Cited by in Crossref: 24] [Cited by in F6Publishing: 25] [Article Influence: 8.0] [Reference Citation Analysis]
53 Pathmanathan P, Cordeiro JM, Gray RA. Comprehensive Uncertainty Quantification and Sensitivity Analysis for Cardiac Action Potential Models. Front Physiol 2019;10:721. [PMID: 31297060 DOI: 10.3389/fphys.2019.00721] [Cited by in Crossref: 40] [Cited by in F6Publishing: 42] [Article Influence: 13.3] [Reference Citation Analysis]
54 Cockrell C, Ozik J, Collier N, An G. Nested Active Learning for Efficient Model Contextualization and Parameterization: Pathway to generating simulated populations using multi-scale computational models.. [DOI: 10.1101/644401] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]
55 Reina-romo E, Papantoniou I, Bloemen V, Geris L. Computational design of tissue engineering scaffolds. Handbook of Tissue Engineering Scaffolds: Volume One 2019. [DOI: 10.1016/b978-0-08-102563-5.00004-6] [Cited by in Crossref: 2] [Article Influence: 0.7] [Reference Citation Analysis]