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Mendeley readers
Attention Score in Context
| Title |
Automated classifiers for early detection and diagnosis of retinopathy in diabetic eyes
|
|---|---|
| Published in |
BMC Bioinformatics, April 2014
|
| DOI | 10.1186/1471-2105-15-106 |
| Pubmed ID | |
| Authors |
Gábor Márk Somfai, Erika Tátrai, Lenke Laurik, Boglárka Varga, Veronika Ölvedy, Hong Jiang, Jianhua Wang, William E Smiddy, Anikó Somogyi, Delia Cabrera DeBuc |
| Abstract |
Artificial neural networks (ANNs) have been used to classify eye diseases, such as diabetic retinopathy (DR) and glaucoma. DR is the leading cause of blindness in working-age adults in the developed world. The implementation of DR diagnostic routines could be feasibly improved by the integration of structural and optical property test measurements of the retinal structure that provide important and complementary information for reaching a diagnosis. In this study, we evaluate the capability of several structural and optical features (thickness, total reflectance and fractal dimension) of various intraretinal layers extracted from optical coherence tomography images to train a Bayesian ANN to discriminate between healthy and diabetic eyes with and with no mild retinopathy. |
X Demographics
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.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Norway | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 50% |
| Scientists | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 53 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 53 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 9 | 17% |
| Researcher | 8 | 15% |
| Student > Master | 7 | 13% |
| Student > Bachelor | 5 | 9% |
| Student > Doctoral Student | 5 | 9% |
| Other | 10 | 19% |
| Unknown | 9 | 17% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 9 | 17% |
| Computer Science | 9 | 17% |
| Medicine and Dentistry | 7 | 13% |
| Nursing and Health Professions | 3 | 6% |
| Arts and Humanities | 2 | 4% |
| Other | 11 | 21% |
| Unknown | 12 | 23% |
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 13 April 2014.
All research outputs
#18,370,767
of 22,753,345 outputs
Outputs from BMC Bioinformatics
#6,302
of 7,269 outputs
Outputs of similar age
#164,393
of 226,854 outputs
Outputs of similar age from BMC Bioinformatics
#91
of 115 outputs
Altmetric has tracked 22,753,345 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,269 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 5th percentile – i.e., 5% 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 226,854 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 14th percentile – i.e., 14% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 115 others from the same source and published within six weeks on either side of this one. This one is in the 5th percentile – i.e., 5% of its contemporaries scored the same or lower than it.