Evidence map›Paper›PMID 36385239›Full record

ArticleCardiovascular engineering and technology2023

The Role of Immersion for Improving Extended Reality Analysis of Personalized Flow Simulations.

Harvey Shi, Madhurima Vardhan, Amanda Randles

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Article in Cardiovascular engineering and technology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Harvey ShiDepartment of Biomedical Engineering, Duke University, 101 Science Drive, Campus Box 90281, Durham, NC, 27708, USA.
Madhurima VardhanDepartment of Biomedical Engineering, Duke University, 101 Science Drive, Campus Box 90281, Durham, NC, 27708, USA.
Amanda RandlesDepartment of Biomedical Engineering, Duke University, 101 Science Drive, Campus Box 90281, Durham, NC, 27708, USA. amanda.randles@duke.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeComputational models of flow in patient-derived arterial geometries have become a key paradigm of biomedical research. These fluid models are often challenging to visualize due to high spatial heterogeneity and visual complexity. Virtual immersive environments can offer advantageous visualization of spatially heterogeneous and complex systems. However, as different VR devices offer varying levels of immersion, there remains a crucial lack of understanding regarding what level of immersion is best suited for interactions with patient-specific flow models.

methodsWe conducted a quantitative user evaluation with multiple VR devices testing an important use of hemodynamic simulations-analysis of surface parameters within complex patient-specific geometries. This task was compared for the semi-immersive zSpace 3D monitor and the fully immersive HTC Vive system.

resultsThe semi-immersive device was more accurate than the fully immersive device. The two devices showed similar results for task duration and performance (accuracy/duration). The accuracy of the semi-immersive device was also higher for arterial geometries of greater complexity and branching.

conclusionThis assessment demonstrates that the level of immersion plays a significant role in the accuracy of assessing arterial flow models. We found that the semi-immersive VR device was a generally optimal choice for arterial visualization.

Indexed as

HemodynamicsImmersionHumansArterial simulationsCoronary modelsImmersive visualizationUser study

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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 OpenQuestion graph.