| Title |
Automated detection of proliferative retinopathy in clinical practice
|
|---|---|
| Published in |
Clinical Ophthalmology, March 2008
|
| DOI | 10.2147/opth.s1579 |
| Pubmed ID | |
| Authors |
Audrey Karperien, Herbert F Jelinek, Jorge JG Leandro, João VB Soares, Roberto M Cesar, Alan Luckie |
| Abstract |
Timely intervention for diabetic retinopathy (DR) lessens the possibility of blindness and can save considerable costs to health systems. To ensure that interventions are timely and effective requires methods of screening and monitoring pathological changes, including assessing outcomes. Fractal analysis, one method that has been studied for assessing DR, is potentially relevant in today's world of telemedicine because it provides objective indices from digital images of complex patterns such as are seen in retinal vasculature, which is affected in DR. We introduce here a protocol to distinguish between nonproliferative (NPDR) and proliferative (PDR) changes in retinal vasculature using a fractal analysis method known as local connected dimension (D(conn)) analysis. The major finding is that compared to other fractal analysis methods, D(conn) analysis better differentiates NPDR from PDR (p = 0.05). In addition, we are the first to show that fractal analysis can be used to differentiate between NPDR and PDR using automated vessel identification. Overall, our results suggest this protocol can complement existing methods by including an automated and objective measure obtainable at a lower level of expertise that experts can then use in screening for and monitoring DR. |
Mendeley readers
The data shown below were compiled from readership statistics for 62 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 2 | 3% |
| India | 2 | 3% |
| Germany | 1 | 2% |
| France | 1 | 2% |
| United States | 1 | 2% |
| Poland | 1 | 2% |
| Unknown | 54 | 87% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 11 | 18% |
| Student > Bachelor | 10 | 16% |
| Student > Master | 9 | 15% |
| Researcher | 7 | 11% |
| Student > Doctoral Student | 4 | 6% |
| Other | 12 | 19% |
| Unknown | 9 | 15% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 15 | 24% |
| Engineering | 11 | 18% |
| Agricultural and Biological Sciences | 6 | 10% |
| Biochemistry, Genetics and Molecular Biology | 3 | 5% |
| Computer Science | 2 | 3% |
| Other | 10 | 16% |
| Unknown | 15 | 24% |
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 18 December 2023.
All research outputs
#7,355,485
of 25,373,627 outputs
Outputs from Clinical Ophthalmology
#632
of 3,712 outputs
Outputs of similar age
#30,187
of 95,556 outputs
Outputs of similar age from Clinical Ophthalmology
#6
of 9 outputs
Altmetric has tracked 25,373,627 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 3,712 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 95,556 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 9 others from the same source and published within six weeks on either side of this one. This one has scored higher than 3 of them.