Evidence map›Paper›PMID 35573253›Full record

ReviewFrontiers in bioengineering and biotechnology2022

Medical Image-Based Computational Fluid Dynamics and Fluid-Structure Interaction Analysis in Vascular Diseases.

Yong He, Hannah Northrup, Ha Le, Alfred K Cheung, Scott A Berceli, Yan Tin Shiu

Open access · goldAbstract readReview
In one paragraph

Review in Frontiers in bioengineering and biotechnology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
8.7field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

19 citing papers in PubMed, 46 citations in OpenAlex.

  1. Article
  2. Observational
  3. Mechanomedicine.Nature reviews bioengineering · 2026
    Article
  4. Article
  5. Review
  6. Article
  7. Review
  8. Article
  9. Review
  10. Uncertainty quantification of the pressure waveform using a Windkessel model.International journal for numerical methods in biomedical engineering · 2024
    Article
  11. Review
  12. Article
  13. Review
  14. Article
  15. Review
  16. Article
  17. Review
  18. Article
  19. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 3 institutions in 1 country.

Yong HeDivision of Vascular Surgery and Endovascular Therapy, University of Florida, Gainesville, FL, United States.
Hannah NorthrupDepartment of Biomedical Engineering, University of Utah, Salt Lake City, UT, United States.
Ha LeDivision of Nephrology and Hypertension, Department of Internal Medicine, University of Utah, Salt Lake City, UT, United States.
Alfred K CheungDivision of Nephrology and Hypertension, Department of Internal Medicine, University of Utah, Salt Lake City, UT, United States.
Scott A BerceliDivision of Vascular Surgery and Endovascular Therapy, University of Florida, Gainesville, FL, United States.
Yan Tin ShiuDivision of Nephrology and Hypertension, Department of Internal Medicine, University of Utah, Salt Lake City, UT, United States.
University of Florida · USUniversity of Utah · USVA Salt Lake City Healthcare System · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Hemodynamic factors, induced by pulsatile blood flow, play a crucial role in vascular health and diseases, such as the initiation and progression of atherosclerosis. Computational fluid dynamics, finite element analysis, and fluid-structure interaction simulations have been widely used to quantify detailed hemodynamic forces based on vascular images commonly obtained from computed tomography angiography, magnetic resonance imaging, ultrasound, and optical coherence tomography. In this review, we focus on methods for obtaining accurate hemodynamic factors that regulate the structure and function of vascular endothelial and smooth muscle cells. We describe the multiple steps and recent advances in a typical patient-specific simulation pipeline, including medical imaging, image processing, spatial discretization to generate computational mesh, setting up boundary conditions and solver parameters, visualization and extraction of hemodynamic factors, and statistical analysis. These steps have not been standardized and thus have unavoidable uncertainties that should be thoroughly evaluated. We also discuss the recent development of combining patient-specific models with machine-learning methods to obtain hemodynamic factors faster and cheaper than conventional methods. These critical advances widen the use of biomechanical simulation tools in the research and potential personalized care of vascular diseases.

Indexed as

computational fluid dynamics (CFD)finite element analysisfluid-structure interaction (FSI)image-based simulationpatient-specific analysis

Identifiers

PMID35573253
PMCPMC9091352
OpenAlexW4224937265

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.