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Three-dimensional cardiac computational modelling: methods, features and applications

Overview of attention for article published in BioMedical Engineering OnLine, April 2015
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  • High Attention Score compared to outputs of the same age and source (85th percentile)

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1 X user
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1 patent
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1 Google+ user

Citations

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124 Dimensions

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281 Mendeley
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Title
Three-dimensional cardiac computational modelling: methods, features and applications
Published in
BioMedical Engineering OnLine, April 2015
DOI 10.1186/s12938-015-0033-5
Pubmed ID
Authors

Alejandro Lopez-Perez, Rafael Sebastian, Jose M Ferrero

Abstract

The combination of computational models and biophysical simulations can help to interpret an array of experimental data and contribute to the understanding, diagnosis and treatment of complex diseases such as cardiac arrhythmias. For this reason, three-dimensional (3D) cardiac computational modelling is currently a rising field of research. The advance of medical imaging technology over the last decades has allowed the evolution from generic to patient-specific 3D cardiac models that faithfully represent the anatomy and different cardiac features of a given alive subject. Here we analyse sixty representative 3D cardiac computational models developed and published during the last fifty years, describing their information sources, features, development methods and online availability. This paper also reviews the necessary components to build a 3D computational model of the heart aimed at biophysical simulation, paying especial attention to cardiac electrophysiology (EP), and the existing approaches to incorporate those components. We assess the challenges associated to the different steps of the building process, from the processing of raw clinical or biological data to the final application, including image segmentation, inclusion of substructures and meshing among others. We briefly outline the personalisation approaches that are currently available in 3D cardiac computational modelling. Finally, we present examples of several specific applications, mainly related to cardiac EP simulation and model-based image analysis, showing the potential usefulness of 3D cardiac computational modelling into clinical environments as a tool to aid in the prevention, diagnosis and treatment of cardiac diseases.

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 281 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 <1%
Spain 1 <1%
Japan 1 <1%
Unknown 277 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 73 26%
Researcher 42 15%
Student > Master 29 10%
Student > Bachelor 23 8%
Student > Doctoral Student 18 6%
Other 50 18%
Unknown 46 16%
Readers by discipline Count As %
Engineering 113 40%
Computer Science 32 11%
Medicine and Dentistry 20 7%
Agricultural and Biological Sciences 10 4%
Physics and Astronomy 9 3%
Other 36 13%
Unknown 61 22%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 04 October 2023.
All research outputs
#6,793,645
of 24,657,405 outputs
Outputs from BioMedical Engineering OnLine
#172
of 852 outputs
Outputs of similar age
#74,860
of 269,793 outputs
Outputs of similar age from BioMedical Engineering OnLine
#4
of 20 outputs
Altmetric has tracked 24,657,405 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 852 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one has done well, scoring higher than 79% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 269,793 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.
We're also able to compare this research output to 20 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 85% of its contemporaries.