| Title |
An artificial intelligence-based deep learning algorithm for the diagnosis of diabetic neuropathy using corneal confocal microscopy: a development and validation study
|
|---|---|
| Published in |
Diabetologia, November 2019
|
| DOI | 10.1007/s00125-019-05023-4 |
| Pubmed ID | |
| Authors |
Bryan M. Williams, Davide Borroni, Rongjun Liu, Yitian Zhao, Jiong Zhang, Jonathan Lim, Baikai Ma, Vito Romano, Hong Qi, Maryam Ferdousi, Ioannis N. Petropoulos, Georgios Ponirakis, Stephen Kaye, Rayaz A. Malik, Uazman Alam, Yalin Zheng |
X Demographics
The data shown below were collected from the profiles of 33 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 Kingdom | 11 | 33% |
| Spain | 3 | 9% |
| United States | 2 | 6% |
| Qatar | 1 | 3% |
| Pakistan | 1 | 3% |
| United Arab Emirates | 1 | 3% |
| Denmark | 1 | 3% |
| Australia | 1 | 3% |
| Unknown | 12 | 36% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 21 | 64% |
| Scientists | 7 | 21% |
| Practitioners (doctors, other healthcare professionals) | 4 | 12% |
| Unknown | 1 | 3% |
Mendeley readers
The data shown below were compiled from readership statistics for 130 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 130 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 17 | 13% |
| Student > Bachelor | 11 | 8% |
| Student > Doctoral Student | 11 | 8% |
| Researcher | 9 | 7% |
| Student > Master | 8 | 6% |
| Other | 16 | 12% |
| Unknown | 58 | 45% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 16 | 12% |
| Medicine and Dentistry | 13 | 10% |
| Biochemistry, Genetics and Molecular Biology | 5 | 4% |
| Nursing and Health Professions | 5 | 4% |
| Unspecified | 4 | 3% |
| Other | 17 | 13% |
| Unknown | 70 | 54% |
Attention Score in Context
This research output has an Altmetric Attention Score of 28. 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 01 October 2021.
All research outputs
#1,402,107
of 25,732,188 outputs
Outputs from Diabetologia
#746
of 5,376 outputs
Outputs of similar age
#29,627
of 376,177 outputs
Outputs of similar age from Diabetologia
#21
of 61 outputs
Altmetric has tracked 25,732,188 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,376 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 24.7. This one has done well, scoring higher than 86% 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 376,177 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 92% of its contemporaries.
We're also able to compare this research output to 61 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 65% of its contemporaries.