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Cited by in F6Publishing
For: Taebi A. Deep Learning for Computational Hemodynamics: A Brief Review of Recent Advances. Fluids 2022;7:197. [DOI: 10.3390/fluids7060197] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
Number Citing Articles
1 Wang S, Wu D, Li G, Zhang Z, Xiao W, Li R, Qiao A, Jin L, Liu H. Deep learning-based hemodynamic prediction of carotid artery stenosis before and after surgical treatments. Front Physiol 2022;13:1094743. [PMID: 36703930 DOI: 10.3389/fphys.2022.1094743] [Reference Citation Analysis]
2 Albadawi M, Abuouf Y, Elsagheer S, Sekiguchi H, Ookawara S, Ahmed M. Influence of Rigid–Elastic Artery Wall of Carotid and Coronary Stenosis on Hemodynamics. Bioengineering 2022;9:708. [DOI: 10.3390/bioengineering9110708] [Reference Citation Analysis]
3 Panchigar D, Kar K, Shukla S, Mathew RM, Chadha U, Selvaraj SK. Machine learning-based CFD simulations: a review, models, open threats, and future tactics. Neural Comput & Applic. [DOI: 10.1007/s00521-022-07838-6] [Reference Citation Analysis]
4 Totorean AF, Totorean IC, Bernad SI, Ciocan T, Malita DC, Gaita D, Bernad ES. Patient-Specific Image-Based Computational Fluid Dynamics Analysis of Abdominal Aorta and Branches. J Pers Med 2022;12. [PMID: 36143287 DOI: 10.3390/jpm12091502] [Reference Citation Analysis]
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