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A deep learning-based approach for rectus abdominis segmentation and distance measurement in ultrasonography

Overview of attention for article published in Frontiers in Physiology, September 2023
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Title
A deep learning-based approach for rectus abdominis segmentation and distance measurement in ultrasonography
Published in
Frontiers in Physiology, September 2023
DOI 10.3389/fphys.2023.1246994
Pubmed ID
Authors

Fei Wang, Rongsong Mao, Laifa Yan, Shan Ling, Zhenyu Cai

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Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 September 2023.
All research outputs
#21,864,239
of 24,393,999 outputs
Outputs from Frontiers in Physiology
#10,159
of 14,978 outputs
Outputs of similar age
#131,721
of 161,003 outputs
Outputs of similar age from Frontiers in Physiology
#84
of 206 outputs
Altmetric has tracked 24,393,999 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 14,978 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.8. This one is in the 1st percentile – i.e., 1% 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 161,003 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 206 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.