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A Systematic Review of Three-Dimensional Printing in Liver Disease

Overview of attention for article published in Journal of Digital Imaging, April 2018
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (61st percentile)
  • Good Attention Score compared to outputs of the same age and source (70th percentile)

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1 policy source
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Citations

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58 Dimensions

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89 Mendeley
Title
A Systematic Review of Three-Dimensional Printing in Liver Disease
Published in
Journal of Digital Imaging, April 2018
DOI 10.1007/s10278-018-0067-x
Pubmed ID
Authors

Elizabeth Rose Perica, Zhonghua Sun

Abstract

The purpose of this review is to analyse current literature related to the clinical applications of 3D printed models in liver disease. A search of the literature was conducted to source studies from databases with the aim of determining the applications and feasibility of 3D printed models in liver disease. 3D printed model accuracy and costs associated with 3D printing, the ability to replicate anatomical structures and delineate important characteristics of hepatic tumours, and the potential for 3D printed liver models to guide surgical planning are analysed. Nineteen studies met the selection criteria for inclusion in the analysis. Seventeen of them were case reports and two were original studies. Quantitative assessment measuring the accuracy of 3D printed liver models was analysed in five studies with mean difference between 3D printed models and original source images ranging from 0.2 to 20%. Fifteen studies provided qualitative assessment with results showing the usefulness of 3D printed models when used as clinical tools in preoperative planning, simulation of surgical or interventional procedures, medical education, and training. The cost and time associated with 3D printed liver model production was reported in 11 studies, with costs ranging from US$13 to US$2000, duration of production up to 100 h. This systematic review shows that 3D printed liver models demonstrate hepatic anatomy and tumours with high accuracy. The models can assist with preoperative planning and may be used in the simulation of surgical procedures for the treatment of malignant hepatic tumours.

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The data shown below were collected from the profiles of 2 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 89 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 89 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 12%
Student > Postgraduate 10 11%
Student > Bachelor 10 11%
Student > Master 8 9%
Lecturer 5 6%
Other 18 20%
Unknown 27 30%
Readers by discipline Count As %
Medicine and Dentistry 30 34%
Engineering 7 8%
Biochemistry, Genetics and Molecular Biology 4 4%
Agricultural and Biological Sciences 3 3%
Nursing and Health Professions 2 2%
Other 11 12%
Unknown 32 36%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 16 April 2019.
All research outputs
#7,041,787
of 23,045,021 outputs
Outputs from Journal of Digital Imaging
#312
of 1,064 outputs
Outputs of similar age
#123,072
of 329,299 outputs
Outputs of similar age from Journal of Digital Imaging
#9
of 31 outputs
Altmetric has tracked 23,045,021 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 1,064 research outputs from this source. They receive a mean Attention Score of 4.6. This one has gotten more attention than average, scoring higher than 70% 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 329,299 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 61% of its contemporaries.
We're also able to compare this research output to 31 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 70% of its contemporaries.