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3D Printed Organ Models for Surgical Applications

Overview of attention for article published in Annual Review of Analytical Chemistry, March 2018
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • One of the highest-scoring outputs from this source (#6 of 215)
  • High Attention Score compared to outputs of the same age (94th percentile)
  • High Attention Score compared to outputs of the same age and source (88th percentile)

Mentioned by

news
5 news outlets
blogs
1 blog
twitter
4 X users
facebook
1 Facebook page

Citations

dimensions_citation
66 Dimensions

Readers on

mendeley
165 Mendeley
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Title
3D Printed Organ Models for Surgical Applications
Published in
Annual Review of Analytical Chemistry, March 2018
DOI 10.1146/annurev-anchem-061417-125935
Pubmed ID
Authors

Kaiyan Qiu, Ghazaleh Haghiashtiani, Michael C McAlpine

Abstract

Medical errors are a major concern in clinical practice, suggesting the need for advanced surgical aids for preoperative planning and rehearsal. Conventionally, CT and MRI scans, as well as 3D visualization techniques, have been utilized as the primary tools for surgical planning. While effective, it would be useful if additional aids could be developed and utilized in particularly complex procedures involving unusual anatomical abnormalities that could benefit from tangible objects providing spatial sense, anatomical accuracy, and tactile feedback. Recent advancements in 3D printing technologies have facilitated the creation of patient-specific organ models with the purpose of providing an effective solution for preoperative planning, rehearsal, and spatiotemporal mapping. Here, we review the state-of-the-art in 3D printed, patient-specific organ models with an emphasis on 3D printing material systems, integrated functionalities, and their corresponding surgical applications and implications. Prior limitations, current progress, and future perspectives in this important area are also broadly discussed. Expected final online publication date for the Annual Review of Analytical Chemistry Volume 11 is June 12, 2018. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

X Demographics

X Demographics

The data shown below were collected from the profiles of 4 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 165 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 165 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 30 18%
Student > Bachelor 19 12%
Researcher 16 10%
Student > Master 16 10%
Student > Doctoral Student 11 7%
Other 16 10%
Unknown 57 35%
Readers by discipline Count As %
Engineering 45 27%
Medicine and Dentistry 17 10%
Materials Science 7 4%
Chemistry 4 2%
Agricultural and Biological Sciences 3 2%
Other 20 12%
Unknown 69 42%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 45. 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 14 July 2020.
All research outputs
#917,883
of 25,382,440 outputs
Outputs from Annual Review of Analytical Chemistry
#6
of 215 outputs
Outputs of similar age
#20,634
of 344,304 outputs
Outputs of similar age from Annual Review of Analytical Chemistry
#1
of 9 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 215 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.7. This one has done particularly well, scoring higher than 97% 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 344,304 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 94% of its contemporaries.
We're also able to compare this research output to 9 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them