Computational investigation and machine learning prediction of nanoparticle effects on hemodynamics in patient-specific aortic aneurysms.

Authors

Mohammad Abdul Karim Khan, Kazi Ekramul Hoque, Mohammad Osman Gani, Nayema Islam Nima, Mohammed Abdul Hannan

Published in

PloS one. Volume 21. Issue 10. Pages e0350224. Epub Oct 07, 2026.

Abstract

This study presents a novel computational framework for nanoparticle-assisted drug delivery in abdominal aortic aneurysm (AAA) by characterizing patient-specific hemodynamics under realistic physiological conditions with integrating an artificial neural network (ANN). The model incorporates silver (Ag) nanoparticles within a fully transient, multiphase computational fluid dynamics (CFD) environment, where pulsatile blood flow is imposed using advanced user-defined function. Blood is modeled as a non-Newtonian fluid to capture its shear-dependent rheological behavior, enabling a more accurate representation of vascular flow dynamics. High-resolution patient-specific geometries are developed to investigate four distinct cases, including healthy and AAA arteries with varying geometrical complexities and multiple daughter vessels, both with and without nanoparticle infusion. The analysis focuses on key clinically relevant hemodynamic indicators, including velocity, normal and tangential wall shear stress (WSS). The results indicate that arterial geometry and blood rheology play a dominant role in governing flow behavior, with the most complex vascular configuration exhibiting the strongest hemodynamic response. Among the investigated cases, model 3 shows the highest velocity, decreasing by 52.94% compared to the healthy artery (model 1), while models 2 and 4 exhibit decreases of 82.35% and 54.90%, respectively at left iliac. The introduction of Ag nanoparticles enhances WSS across all models by 34.72%, 47.32%, 51.92%, and 39.67%, respectively, indicating improved flow modulation and more favorable conditions for drug transport and vascular interaction. Flow visualization reveals distinct regions of acceleration, recirculation, and nanoparticle accumulation, which are critical for understanding localized delivery mechanisms. The ANN confirming the robustness, accuracy, and predictive reliability of the developed models. Finally, the proposed framework establishes a clear relationship between arterial morphology, non-Newtonian blood behavior, and nanoparticle-driven hemodynamic modulation, offering valuable insights for optimizing patient-specific, nanoparticle-based therapeutic strategies in cardiovascular disease management.

PMID:
42842584
Bibliographic data and abstract were imported from PubMed on 08 Oct 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.