Evidence map›Paper›PMID 37483801›Full record

ArticleHeliyon2023

A multiphysics-based artificial neural networks model for atherosclerosis.

M Soleimani, B Dashtbozorg, M Mirkhalaf, S M Mirkhalaf

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.6field-weighted citation impact, top 30% 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

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. 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

4 authors at 4 institutions in 4 countries.

M SoleimaniInstitute of Continuum Mechanics, Leibniz Universität Hannover, Hannover, Germany.
B DashtbozorgDepartment of Surgical Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands.
M MirkhalafSchool of Mechanical, Medical and Process Engineering, Queensland University of Technology, Brisbane, Australia.
S M MirkhalafDepartment of Physics, University of Gothenburg, Gothenburg, Sweden.
Leibniz University Hannover · DEQueensland University of Technology · AUThe Netherlands Cancer Institute · NLUniversity of Gothenburg · SE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Atherosclerosis is a medical condition involving the hardening and/or thickening of arteries' walls. Mathematical multi-physics models have been developed to predict the development of atherosclerosis under different conditions. However, these models are typically computationally expensive. In this study, we used machine learning techniques, particularly artificial neural networks (ANN), to enhance the computational efficiency of these models. A database of multi-physics Finite Element Method (FEM) simulations was created and used for training and validating an ANN model. The model is capable of quick and accurate prediction of atherosclerosis development. A remarkable computational gain is obtained using the ANN model compared to the original FEM simulations.

Indexed as

Artificial neural networksAtherosclerosisFinite Element ModelingMulti-physics

Identifiers

PMID37483801
PMCPMC10362161
OpenAlexW4383532515

What OpenQuestion 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 OpenQuestion graph.