| Title |
Crowdsourcing and Automated Retinal Image Analysis for Diabetic Retinopathy
|
|---|---|
| Published in |
Current Diabetes Reports, September 2017
|
| DOI | 10.1007/s11892-017-0940-x |
| Pubmed ID | |
| Authors |
Lucy I. Mudie, Xueyang Wang, David S. Friedman, Christopher J. Brady |
| Abstract |
As the number of people with diabetic retinopathy (DR) in the USA is expected to increase threefold by 2050, the need to reduce health care costs associated with screening for this treatable disease is ever present. Crowdsourcing and automated retinal image analysis (ARIA) are two areas where new technology has been applied to reduce costs in screening for DR. This paper reviews the current literature surrounding these new technologies. Crowdsourcing has high sensitivity for normal vs abnormal images; however, when multiple categories for severity of DR are added, specificity is reduced. ARIAs have higher sensitivity and specificity, and some commercial ARIA programs are already in use. Deep learning enhanced ARIAs appear to offer even more improvement in ARIA grading accuracy. The utilization of crowdsourcing and ARIAs may be a key to reducing the time and cost burden of processing images from DR screening. |
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 71 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 71 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 9 | 13% |
| Student > Master | 7 | 10% |
| Researcher | 6 | 8% |
| Student > Postgraduate | 4 | 6% |
| Student > Bachelor | 3 | 4% |
| Other | 13 | 18% |
| Unknown | 29 | 41% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 19 | 27% |
| Computer Science | 9 | 13% |
| Engineering | 3 | 4% |
| Agricultural and Biological Sciences | 2 | 3% |
| Economics, Econometrics and Finance | 1 | 1% |
| Other | 7 | 10% |
| Unknown | 30 | 42% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 25 September 2017.
All research outputs
#20,448,386
of 23,003,906 outputs
Outputs from Current Diabetes Reports
#932
of 1,013 outputs
Outputs of similar age
#279,161
of 319,601 outputs
Outputs of similar age from Current Diabetes Reports
#33
of 35 outputs
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