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Cited by in F6Publishing
For: Miller R, Kerfoot E, Mauger C, Ismail TF, Young AA, Nordsletten DA. An Implementation of Patient-Specific Biventricular Mechanics Simulations With a Deep Learning and Computational Pipeline. Front Physiol 2021;12:716597. [PMID: 34603077 DOI: 10.3389/fphys.2021.716597] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
Number Citing Articles
1 Nobrega IAP, Mao W. A Deep Learning Model for the Identification of Active Contraction Properties of the Myocardium Using Limited Clinical Metrics.. [DOI: 10.21203/rs.3.rs-2405609/v1] [Reference Citation Analysis]
2 Stimm J, Nordsletten DA, Jilberto J, Miller R, Berberoğlu E, Kozerke S, Stoeck CT. Personalization of biomechanical simulations of the left ventricle by in-vivo cardiac DTI data: Impact of fiber interpolation methods. Front Physiol 2022;13. [DOI: 10.3389/fphys.2022.1042537] [Reference Citation Analysis]
3 Kakaletsis S, Lejeune E, Rausch MK. Can machine learning accelerate soft material parameter identification from complex mechanical test data? Biomech Model Mechanobiol 2022. [PMID: 36229697 DOI: 10.1007/s10237-022-01631-z] [Reference Citation Analysis]
4 Tossas-betancourt C, Li NY, Shavik SM, Afton K, Beckman B, Whiteside W, Olive MK, Lim HM, Lu JC, Phelps CM, Gajarski RJ, Lee S, Nordsletten DA, Grifka RG, Dorfman AL, Baek S, Lee LC, Figueroa CA. Data-driven computational models of ventricular-arterial hemodynamics in pediatric pulmonary arterial hypertension. Front Physiol 2022;13:958734. [DOI: 10.3389/fphys.2022.958734] [Reference Citation Analysis]
5 Fanni BM, Pizzuto A, Santoro G, Celi S. Introduction of a Novel Image-Based and Non-Invasive Method for the Estimation of Local Elastic Properties of Great Vessels. Electronics 2022;11:2055. [DOI: 10.3390/electronics11132055] [Reference Citation Analysis]