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Machine learning derived segmentation of phase velocity encoded cardiovascular magnetic resonance for fully automated aortic flow quantification

Overview of attention for article published in Critical Reviews in Diagnostic Imaging, January 2019
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (86th percentile)
  • High Attention Score compared to outputs of the same age and source (84th percentile)

Mentioned by

twitter
27 X users

Citations

dimensions_citation
70 Dimensions

Readers on

mendeley
140 Mendeley
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Title
Machine learning derived segmentation of phase velocity encoded cardiovascular magnetic resonance for fully automated aortic flow quantification
Published in
Critical Reviews in Diagnostic Imaging, January 2019
DOI 10.1186/s12968-018-0509-0
Pubmed ID
Authors

Alex Bratt, Jiwon Kim, Meridith Pollie, Ashley N. Beecy, Nathan H. Tehrani, Noel Codella, Rocio Perez-Johnston, Maria Chiara Palumbo, Javid Alakbarli, Wayne Colizza, Ian R. Drexler, Clerio F. Azevedo, Raymond J. Kim, Richard B. Devereux, Jonathan W. Weinsaft

X Demographics

X Demographics

The data shown below were collected from the profiles of 27 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 140 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 140 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 23 16%
Student > Ph. D. Student 21 15%
Student > Master 17 12%
Student > Bachelor 11 8%
Other 8 6%
Other 18 13%
Unknown 42 30%
Readers by discipline Count As %
Medicine and Dentistry 35 25%
Engineering 27 19%
Computer Science 11 8%
Unspecified 3 2%
Business, Management and Accounting 2 1%
Other 9 6%
Unknown 53 38%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 14. 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 21 December 2019.
All research outputs
#2,630,523
of 25,523,622 outputs
Outputs from Critical Reviews in Diagnostic Imaging
#125
of 1,379 outputs
Outputs of similar age
#59,201
of 446,051 outputs
Outputs of similar age from Critical Reviews in Diagnostic Imaging
#4
of 26 outputs
Altmetric has tracked 25,523,622 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,379 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.3. This one has done particularly well, scoring higher than 90% 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 446,051 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 86% of its contemporaries.
We're also able to compare this research output to 26 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 84% of its contemporaries.