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Machine learning in cardiovascular radiology: ESCR position statement on design requirements, quality assessment, current applications, opportunities, and challenges

Overview of attention for article published in European Radiology, November 2020
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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 (89th percentile)
  • High Attention Score compared to outputs of the same age and source (96th percentile)

Mentioned by

twitter
26 X users

Citations

dimensions_citation
19 Dimensions

Readers on

mendeley
39 Mendeley
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Title
Machine learning in cardiovascular radiology: ESCR position statement on design requirements, quality assessment, current applications, opportunities, and challenges
Published in
European Radiology, November 2020
DOI 10.1007/s00330-020-07417-0
Pubmed ID
Authors

Thomas Weikert, Marco Francone, Suhny Abbara, Bettina Baessler, Byoung Wook Choi, Matthias Gutberlet, Elizabeth M. Hecht, Christian Loewe, Elie Mousseaux, Luigi Natale, Konstantin Nikolaou, Karen G. Ordovas, Charles Peebles, Claudia Prieto, Rodrigo Salgado, Birgitta Velthuis, Rozemarijn Vliegenthart, Jens Bremerich, Tim Leiner

X Demographics

X Demographics

The data shown below were collected from the profiles of 26 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 39 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 39 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 23%
Student > Ph. D. Student 4 10%
Student > Master 3 8%
Lecturer 2 5%
Student > Postgraduate 2 5%
Other 5 13%
Unknown 14 36%
Readers by discipline Count As %
Medicine and Dentistry 7 18%
Engineering 4 10%
Computer Science 3 8%
Agricultural and Biological Sciences 2 5%
Business, Management and Accounting 1 3%
Other 4 10%
Unknown 18 46%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 18. 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 06 May 2023.
All research outputs
#1,933,331
of 24,736,359 outputs
Outputs from European Radiology
#152
of 4,721 outputs
Outputs of similar age
#52,293
of 518,212 outputs
Outputs of similar age from European Radiology
#5
of 106 outputs
Altmetric has tracked 24,736,359 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,721 research outputs from this source. They receive a mean Attention Score of 4.5. This one has done particularly well, scoring higher than 96% 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 518,212 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 89% of its contemporaries.
We're also able to compare this research output to 106 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 96% of its contemporaries.