Title |
Evaluating the Viability of a Smartphone-Based Annotation Tool for Faster and Accurate Image Labelling for Artificial Intelligence in Diabetic Retinopathy
|
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Published in |
Clinical Ophthalmology, March 2021
|
DOI | 10.2147/opth.s289425 |
Pubmed ID | |
Authors |
Arvind Kumar Morya, Jaitra Gowdar, Abhishek Kaushal, Nachiket Makwana, Saurav Biswas, Puneeth Raj, Shabnam Singh, Sharat Hegde, Raksha Vaishnav, Sharan Shetty, Vidyambika S P, Vedang Shah, Sabita Paul, Sonali Muralidhar, Girish Velis, Winston Padua, Tushar Waghule, Nazneen Nazm, Sangeetha Jeganathan, Ayyappa Reddy Mallidi, Dona Susan John, Sagnik Sen, Sandeep Choudhary, Nishant Parashar, Bhavana Sharma, Pankaja Raghav, Raghuveer Udawat, Sampat Ram, Umang P Salodia |
X Demographics
The data shown below were collected from the profiles of 5 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 | 2 | 40% |
Netherlands | 1 | 20% |
Unknown | 2 | 40% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 3 | 60% |
Practitioners (doctors, other healthcare professionals) | 2 | 40% |
Mendeley readers
The data shown below were compiled from readership statistics for 57 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 57 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Bachelor | 10 | 18% |
Student > Master | 6 | 11% |
Researcher | 5 | 9% |
Lecturer | 3 | 5% |
Unspecified | 2 | 4% |
Other | 6 | 11% |
Unknown | 25 | 44% |
Readers by discipline | Count | As % |
---|---|---|
Unspecified | 11 | 19% |
Computer Science | 6 | 11% |
Medicine and Dentistry | 3 | 5% |
Nursing and Health Professions | 2 | 4% |
Social Sciences | 2 | 4% |
Other | 4 | 7% |
Unknown | 29 | 51% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 20 March 2021.
All research outputs
#15,181,325
of 25,387,668 outputs
Outputs from Clinical Ophthalmology
#1,158
of 3,714 outputs
Outputs of similar age
#228,600
of 451,361 outputs
Outputs of similar age from Clinical Ophthalmology
#34
of 152 outputs
Altmetric has tracked 25,387,668 research outputs across all sources so far. This one is in the 38th percentile – i.e., 38% of other outputs scored the same or lower than it.
So far Altmetric has tracked 3,714 research outputs from this source. They receive a mean Attention Score of 4.9. This one has gotten more attention than average, scoring higher than 66% 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 451,361 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 152 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 73% of its contemporaries.