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Exploring deep learning radiomics for classifying osteoporotic vertebral fractures in X-ray images

Overview of attention for article published in Frontiers in endocrinology, March 2024
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Title
Exploring deep learning radiomics for classifying osteoporotic vertebral fractures in X-ray images
Published in
Frontiers in endocrinology, March 2024
DOI 10.3389/fendo.2024.1370838
Pubmed ID
Authors

Jun Zhang, Liang Xia, Jiayi Liu, Xiaoying Niu, Jun Tang, Jianguo Xia, Yongkang Liu, Weixiao Zhang, Zhipeng Liang, Xueli Zhang, Guangyu Tang, Lin Zhang

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user 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 1 Mendeley reader of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 1 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 1 100%
Readers by discipline Count As %
Medicine and Dentistry 1 100%
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 28 March 2024.
All research outputs
#23,154,856
of 25,806,763 outputs
Outputs from Frontiers in endocrinology
#8,518
of 13,284 outputs
Outputs of similar age
#211,689
of 265,061 outputs
Outputs of similar age from Frontiers in endocrinology
#176
of 368 outputs
Altmetric has tracked 25,806,763 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 13,284 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. 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 265,061 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 368 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.