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Diagnostic potential of automated subcortical volume segmentation in atypical parkinsonism

Overview of attention for article published in Neurology, March 2016
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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 (90th percentile)
  • Good Attention Score compared to outputs of the same age and source (74th percentile)

Mentioned by

news
1 news outlet
blogs
1 blog
twitter
4 X users

Citations

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89 Dimensions

Readers on

mendeley
110 Mendeley
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Title
Diagnostic potential of automated subcortical volume segmentation in atypical parkinsonism
Published in
Neurology, March 2016
DOI 10.1212/wnl.0000000000002518
Pubmed ID
Authors

Christoph Scherfler, Georg Göbel, Christoph Müller, Michael Nocker, Gregor K Wenning, Michael Schocke, Werner Poewe, Klaus Seppi

Abstract

To determine whether automated and observer-independent volumetric MRI analysis is able to discriminate among patients with Parkinson disease (PD), multiple system atrophy (MSA), and progressive supranuclear palsy (PSP) in early to moderately advanced stages of disease. T1-weighted volumetric MRI from patients with clinically probable PD (n = 40), MSA (n = 40), and PSP (n = 30) and a mean disease duration of 2.8 ± 1.7 y were examined using automated volume measures of 22 subcortical regions. The clinical follow-up period was 2.5 ± 1.2 years. The data were split into a training (n = 72) and a test set (n = 38). The training set was used to build a C4.5 decision tree model in order to classify patients as MSA, PSP, or PD. The classification algorithm was examined by the test set using the final clinical diagnosis at last follow-up as diagnostic gold standard. The midbrain and putaminal volume as well as the cerebellar gray matter compartment were identified as the most significant brain regions to construct a prediction model. The diagnostic accuracy for PD vs MSA or PSP was 97.4%. In contrast, diagnostic accuracy based on validated clinical consensus criteria at the time of MRI acquisition was 62.9%. Volume segmentation of subcortical brain areas differentiates PD from MSA and PSP and improves diagnostic accuracy in patients presenting with early to moderately advanced stage parkinsonism. This study provides Class III evidence that automated MRI analysis accurately discriminates among early-stage PD, MSA, and PSP.

X Demographics

X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 110 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 <1%
Unknown 109 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 20 18%
Researcher 14 13%
Student > Master 9 8%
Student > Doctoral Student 9 8%
Student > Postgraduate 7 6%
Other 21 19%
Unknown 30 27%
Readers by discipline Count As %
Medicine and Dentistry 25 23%
Neuroscience 17 15%
Engineering 7 6%
Agricultural and Biological Sciences 4 4%
Psychology 4 4%
Other 11 10%
Unknown 42 38%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 20. 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 24 June 2016.
All research outputs
#1,853,268
of 25,377,790 outputs
Outputs from Neurology
#3,500
of 21,010 outputs
Outputs of similar age
#29,594
of 312,881 outputs
Outputs of similar age from Neurology
#70
of 278 outputs
Altmetric has tracked 25,377,790 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 21,010 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 23.7. This one has done well, scoring higher than 83% 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 312,881 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 90% of its contemporaries.
We're also able to compare this research output to 278 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 74% of its contemporaries.