Title |
Automated retinal imaging and trend analysis – a tool for health monitoring
|
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Published in |
Clinical Ophthalmology, May 2017
|
DOI | 10.2147/opth.s116265 |
Pubmed ID | |
Authors |
Karin Roesch, Tristan Swedish, Ramesh Raskar |
Abstract |
Most current diagnostic devices are expensive, require trained specialists to operate and gather static images with sparse data points. This leads to preventable diseases going undetected until late stage, resulting in greatly narrowed treatment options. This is especially true for retinal imaging. Future solutions are low cost, portable, self-administered by the patient, and capable of providing multiple data points, population analysis, and trending. This enables preventative interventions through mass accessibility, constant monitoring, and predictive modeling. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 1 | 100% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 29 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 5 | 17% |
Researcher | 4 | 14% |
Student > Master | 4 | 14% |
Student > Doctoral Student | 3 | 10% |
Student > Bachelor | 2 | 7% |
Other | 2 | 7% |
Unknown | 9 | 31% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 7 | 24% |
Computer Science | 5 | 17% |
Psychology | 3 | 10% |
Nursing and Health Professions | 2 | 7% |
Unspecified | 1 | 3% |
Other | 1 | 3% |
Unknown | 10 | 34% |
Attention Score in Context
This research output has an Altmetric Attention Score of 8. 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 12 April 2019.
All research outputs
#4,721,253
of 25,382,440 outputs
Outputs from Clinical Ophthalmology
#392
of 3,714 outputs
Outputs of similar age
#76,591
of 324,557 outputs
Outputs of similar age from Clinical Ophthalmology
#11
of 46 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. Compared to these this one has done well and is in the 81st percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
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 89% 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 324,557 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 76% of its contemporaries.
We're also able to compare this research output to 46 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 76% of its contemporaries.