| Title |
Applying Densely Connected Convolutional Neural Networks for Staging Osteoarthritis Severity from Plain Radiographs
|
|---|---|
| Published in |
Journal of Digital Imaging, October 2018
|
| DOI | 10.1007/s10278-018-0098-3 |
| Pubmed ID | |
| Authors |
Berk Norman, Valentina Pedoia, Adam Noworolski, Thomas M. Link, Sharmila Majumdar |
X Demographics
The data shown below were collected from the profiles of 8 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Greece | 1 | 13% |
| Singapore | 1 | 13% |
| Germany | 1 | 13% |
| United States | 1 | 13% |
| Spain | 1 | 13% |
| Netherlands | 1 | 13% |
| Unknown | 2 | 25% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 5 | 63% |
| Practitioners (doctors, other healthcare professionals) | 3 | 38% |
Mendeley readers
The data shown below were compiled from readership statistics for 118 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 118 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 19 | 16% |
| Student > Ph. D. Student | 15 | 13% |
| Student > Bachelor | 12 | 10% |
| Professor | 8 | 7% |
| Other | 7 | 6% |
| Other | 21 | 18% |
| Unknown | 36 | 31% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 27 | 23% |
| Computer Science | 19 | 16% |
| Engineering | 14 | 12% |
| Nursing and Health Professions | 5 | 4% |
| Biochemistry, Genetics and Molecular Biology | 4 | 3% |
| Other | 7 | 6% |
| Unknown | 42 | 36% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 04 May 2019.
All research outputs
#7,576,904
of 23,106,390 outputs
Outputs from Journal of Digital Imaging
#345
of 1,067 outputs
Outputs of similar age
#135,528
of 346,058 outputs
Outputs of similar age from Journal of Digital Imaging
#12
of 33 outputs
Altmetric has tracked 23,106,390 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,067 research outputs from this source. They receive a mean Attention Score of 4.6. This one is in the 45th percentile – i.e., 45% 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 346,058 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 54% of its contemporaries.
We're also able to compare this research output to 33 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 63% of its contemporaries.