Uncertainty quantification implementations in human hemodynamic flows. (May 2021)

• Record Type: Journal Article
• Authors: Ninos, G.; Bartzis, V.; Merlemis, N.; Sarris, I. E.
• Is Part Of: Computer methods and programs in biomedicine. Volume 203(2021)
• Publication Date: 2021-05
• Subjects: Biomedical sciences -- Hemodynamic flows -- Uncertainty quantification -- Polynomial chaos -- Computational fluid dynamics; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2021.106021
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: It is found that more research needs to be done in bioengineering applications with respect to UQ Carefully estimation and analysis of the input parameters uncertainty is of crucial importance for biomedical sciences. Physiological UQ needs realistic probability distributions calibrated according to specific clinical data Limitations exist for UQ calibration, reduction of computational cost and increase numerical accuracy Abstract: Background and objective: Human hemodynamic modeling is usually influenced by uncertainties occurring from a considerable unavailability of information linked to the boundary conditions and the physical properties used in the numerical models. Calculating the effect of these uncertainties on the numerical findings along the cardiovascular system is a demanding process due to the complexity of the morphology of the body and the area dynamics. To cope with all these difficulties, Uncertainty Quantification (UQ) methods seem to be an ideal tool. Results: This study focuses on analyzing and summarizing some of the recent research efforts and directions of implementing UQ in human hemodynamic flows by analyzing 139 research papers. Initially, the suitability of applying this approach is analyzed and demonstrated. Then, an overview of the most significant research work in various fields of biomedical hemodynamic engineering is presented. Finally, it is attempted to identify any possible forthcoming directions for research and methodological progress of UQ in biomedical sciences. Conclusion: This review concludes that by finding the best statistical methods and parameters to represent the propagated uncertainties, while achieving a good interpretation of the interaction between input–output, is crucial for implementing UQ in biomedical sciences.