Current Diabetes Reports

, 17:106 | Cite as

Crowdsourcing and Automated Retinal Image Analysis for Diabetic Retinopathy

  • Lucy I. Mudie
  • Xueyang Wang
  • David S. Friedman
  • Christopher J. Brady
Microvascular Complications—Retinopathy (JK Sun and PS Silva, Section Editors)
Part of the following topical collections:
  1. Topical Collection on Microvascular Complications—Retinopathy

Abstract

Purpose of Review

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.

Recent Findings

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.

Summary

The utilization of crowdsourcing and ARIAs may be a key to reducing the time and cost burden of processing images from DR screening.

Keywords

Crowdsourcing Diabetic retinopathy Amazon Mechanical Turk Automated retinal image analysis Telemedicine 

Notes

Compliance with Ethical Standards

Conflict of Interest

Lucy I. Mudie, Xueyang Wang, David S. Friedman, and Christopher J. Brady declare that they have no conflict of interest.

Human and Animal Rights and Informed Consent

This manuscript does not include any studies with human or animal subjects performed by any of the authors. The Johns Hopkins University School of Medicine Institutional Review Board (IRB) has deemed the crowdsourcing work of the authors IRB-exempt as non-human subjects research. Disclaimer

Disclaimer

Its contents are solely the responsibility of the authors and do not necessarily represent the official view of the Johns Hopkins ICTR, NCATS or NIH.

References

Papers of particular interest, published recently, have been highlighted as: ••Of major importance

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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Lucy I. Mudie
    • 1
  • Xueyang Wang
    • 1
  • David S. Friedman
    • 1
  • Christopher J. Brady
    • 1
  1. 1.Wilmer Eye InstituteJohns Hopkins University School of MedicineBaltimoreUSA

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