| Title |
Artificial Intelligence to Identify Retinal Fundus Images, Quality Validation, Laterality Evaluation, Macular Degeneration, and Suspected Glaucoma
|
|---|---|
| Published in |
Clinical Ophthalmology, February 2020
|
| DOI | 10.2147/opth.s235751 |
| Pubmed ID | |
| Authors |
Miguel Angel Zapata, Dídac Royo-Fibla, Octavi Font, José Ignacio Vela, Ivanna Marcantonio, Eduardo Ulises Moya-Sánchez, Abraham Sánchez-Pérez, Darío Garcia-Gasulla, Ulises Cortés, Eduard Ayguadé, Jesus Labarta |
X Demographics
The data shown below were collected from the profiles of 9 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 33% |
| United Kingdom | 2 | 22% |
| Spain | 1 | 11% |
| India | 1 | 11% |
| Belgium | 1 | 11% |
| Unknown | 1 | 11% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 7 | 78% |
| Scientists | 1 | 11% |
| Practitioners (doctors, other healthcare professionals) | 1 | 11% |
Mendeley readers
The data shown below were compiled from readership statistics for 84 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 84 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 10 | 12% |
| Student > Master | 10 | 12% |
| Student > Doctoral Student | 6 | 7% |
| Student > Bachelor | 5 | 6% |
| Researcher | 4 | 5% |
| Other | 13 | 15% |
| Unknown | 36 | 43% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 22 | 26% |
| Computer Science | 9 | 11% |
| Engineering | 4 | 5% |
| Nursing and Health Professions | 2 | 2% |
| Agricultural and Biological Sciences | 2 | 2% |
| Other | 3 | 4% |
| Unknown | 42 | 50% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. 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 27 March 2020.
All research outputs
#7,360,834
of 25,387,668 outputs
Outputs from Clinical Ophthalmology
#633
of 3,714 outputs
Outputs of similar age
#152,716
of 470,256 outputs
Outputs of similar age from Clinical Ophthalmology
#13
of 105 outputs
Altmetric has tracked 25,387,668 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 3,714 research outputs from this source. They receive a mean Attention Score of 4.9. This one has done well, scoring higher than 82% of its peers.
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 470,256 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 66% of its contemporaries.
We're also able to compare this research output to 105 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 86% of its contemporaries.