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New approaches to the analysis of eye movement behaviour across expertise while viewing brain MRIs

Overview of attention for article published in Cognitive Research: Principles and Implications, April 2018
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  • Good Attention Score compared to outputs of the same age (68th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (62nd percentile)

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Title
New approaches to the analysis of eye movement behaviour across expertise while viewing brain MRIs
Published in
Cognitive Research: Principles and Implications, April 2018
DOI 10.1186/s41235-018-0097-4
Pubmed ID
Authors

Emily M. Crowe, Iain D. Gilchrist, Christopher Kent

Abstract

Brain tumour detection and diagnosis requires clinicians to inspect and analyse brain magnetic resonance images. Eye-tracking is commonly used to examine observers' gaze behaviour during such medical image interpretation tasks, but analysis of eye movement sequences is limited. We therefore used ScanMatch, a novel technique that compares saccadic eye movement sequences, to examine the effect of expertise and diagnosis on the similarity of scanning patterns. Diagnostic accuracy was also recorded. Thirty-five participants were classified as Novices, Medics and Experts based on their level of expertise. Participants completed two brain tumour detection tasks. The first was a whole-brain task, which consisted of 60 consecutively presented slices from one patient; the second was an independent-slice detection task, which consisted of 32 independent slices from five different patients. Experts displayed the highest accuracy and sensitivity followed by Medics and then Novices in the independent-slice task. Experts showed the highest level of scanning pattern similarity, with medics engaging in the least similar scanning patterns, for both the whole-brain and independent-slice task. In the independent-slice task, scanning patterns were the least similar for false negatives across all expertise levels and most similar for experts when they responded correctly. These results demonstrate the value of using ScanMatch in the medical image perception literature. Future research adopting this tool could, for example, identify cases that yield low scanning similarity and so provide insight into why diagnostic errors occur and ultimately help in training radiologists.

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The data shown below were collected from the profiles of 7 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 42 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 42 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 10 24%
Student > Bachelor 8 19%
Researcher 4 10%
Student > Master 3 7%
Student > Doctoral Student 1 2%
Other 6 14%
Unknown 10 24%
Readers by discipline Count As %
Psychology 11 26%
Engineering 7 17%
Medicine and Dentistry 5 12%
Computer Science 4 10%
Agricultural and Biological Sciences 2 5%
Other 3 7%
Unknown 10 24%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 05 September 2018.
All research outputs
#6,083,463
of 23,567,572 outputs
Outputs from Cognitive Research: Principles and Implications
#167
of 329 outputs
Outputs of similar age
#104,013
of 327,693 outputs
Outputs of similar age from Cognitive Research: Principles and Implications
#3
of 8 outputs
Altmetric has tracked 23,567,572 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 329 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 44.0. This one is in the 49th percentile – i.e., 49% of its peers scored the same or lower than it.
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 327,693 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 68% of its contemporaries.
We're also able to compare this research output to 8 others from the same source and published within six weeks on either side of this one. This one has scored higher than 5 of them.