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BioPARR: A software system for estimating the rupture potential index for abdominal aortic aneurysms

Overview of attention for article published in Scientific Reports, July 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (72nd percentile)
  • Good Attention Score compared to outputs of the same age and source (71st percentile)

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Title
BioPARR: A software system for estimating the rupture potential index for abdominal aortic aneurysms
Published in
Scientific Reports, July 2017
DOI 10.1038/s41598-017-04699-1
Pubmed ID
Authors

Grand Roman Joldes, Karol Miller, Adam Wittek, Rachael O. Forsythe, David E. Newby, Barry J. Doyle

Abstract

An abdominal aortic aneurysm (AAA) is a permanent and irreversible dilation of the lower region of the aorta. It is a symptomless condition that, if left untreated, can expand until rupture. Despite ongoing efforts, an efficient tool for accurate estimation of AAA rupture risk is still not available. Furthermore, a lack of standardisation across current approaches and specific obstacles within computational workflows limit the translation of existing methods to the clinic. This paper presents BioPARR (Biomechanics based Prediction of Aneurysm Rupture Risk), a software system to facilitate the analysis of AAA using a finite element analysis based approach. Except semi-automatic segmentation of the AAA and intraluminal thrombus (ILT) from medical images, the entire analysis is performed automatically. The system is modular and easily expandable, allows the extraction of information from images of different modalities (e.g. CT and MRI) and the simulation of different modelling scenarios (e.g. with/without thrombus). The software uses contemporary methods that eliminate the need for patient-specific material properties, overcoming perhaps the key limitation to all previous patient-specific analysis methods. The software system is robust, free, and will allow researchers to perform comparative evaluation of AAA using a standardised approach. We report preliminary data from 48 cases.

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The data shown below were collected from the profiles of 8 X users 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 81 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 81 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 16 20%
Researcher 14 17%
Student > Master 9 11%
Student > Postgraduate 4 5%
Student > Bachelor 3 4%
Other 12 15%
Unknown 23 28%
Readers by discipline Count As %
Engineering 26 32%
Medicine and Dentistry 12 15%
Computer Science 3 4%
Biochemistry, Genetics and Molecular Biology 2 2%
Agricultural and Biological Sciences 2 2%
Other 7 9%
Unknown 29 36%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 12 July 2017.
All research outputs
#6,074,348
of 24,940,046 outputs
Outputs from Scientific Reports
#41,191
of 136,634 outputs
Outputs of similar age
#87,612
of 318,489 outputs
Outputs of similar age from Scientific Reports
#1,443
of 5,038 outputs
Altmetric has tracked 24,940,046 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 136,634 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 18.7. This one has gotten more attention than average, scoring higher than 69% 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 318,489 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 72% of its contemporaries.
We're also able to compare this research output to 5,038 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.