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Automated Analysis of Diabetic Retinopathy Images: Principles, Recent Developments, and Emerging Trends

  • Microvascular Complications-Retinopathy (JK Sun, Section Editor)
  • Published:
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Abstract

Diabetic retinopathy (DR) is a vision-threatening complication of diabetes. Timely diagnosis and intervention are essential for treatment that reduces the risk of vision loss. A good color retinal (fundus) photograph can be used as a surrogate for face-to-face evaluation of DR. The use of computers to assist or fully automate DR evaluation from retinal images has been studied for many years. Early work showed promising results for algorithms in detecting and classifying DR pathology. Newer techniques include those that adapt machine learning technology to DR image analysis. Challenges remain, however, that must be overcome before fully automatic DR detection and analysis systems become practical clinical tools.

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Acknowledgments

This work was supported in part by the Agency for Healthcare Research and Quality, Grant R21 HS19792-02.

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Conflict of Interest

Baoxin Li and Helen K. Li declare that they have no conflict of interest.

Human and Animal Rights and Informed Consent

This article does not contain any studies with human or animal subjects performed by any of the authors.

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Authors and Affiliations

  1. School of Computing, Informatics & Decision Systems Engineering, Arizona State University, 699 S Mill AVE #553, Tempe, AZ, 85281, USA

    Baoxin Li

  2. Weill Cornell Medical College / The Methodist Hospital, Houston, TX, USA

    Helen K. Li

  3. School of Biomedical Informatics, The University of Texas Health Science Center, Houston, TX, USA

    Helen K. Li

  4. Department of Ophthalmology, Thomas Jefferson University, Philadelphia, PA, USA

    Helen K. Li

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  1. Baoxin Li
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Correspondence to Baoxin Li.

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Li, B., Li, H.K. Automated Analysis of Diabetic Retinopathy Images: Principles, Recent Developments, and Emerging Trends. Curr Diab Rep 13, 453–459 (2013). https://doi.org/10.1007/s11892-013-0393-9

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