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Major automatic diabetic retinopathy screening systems and related core algorithms: a review

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Abstract

Diabetic retinopathy (DR), one of the major and long-term microvascular complications of diabetes, is the most common cause of vision loss and blindness in the working population of the world. Even with the management of diabetes, most patients will develop some forms of DR after approximately 20 years. However, DR is a treatable disease throughout the disease progression. To provide appropriate DR management, the USA and European countries have successfully implemented systematic early DR screening programs. At the same time, some computer-aided DR screening systems, which combine advanced DR detection algorithms and telemedicine technology, have also been developed for early-stage DR detection. Some of them have been tested in the DR screening programs. In this paper, we focus on a review of the major automatic DR screening systems which have performed large-scale evaluation rather than give an extensive review of all published DR grading algorithms. We first present the structures of the automatic systems and their supporting algorithms developed by the research groups, as well as the practices of the systems in their screening programs. We further present a more detailed review of the DR lesion detection algorithms in each system and reveal how the DR screening systems successfully practiced in clinical trials or large-scale screening programs. We also review recently new research areas as well as deep learning-based DR screening systems and compare them with the traditional lesion detection-based DR screening systems. The performances of the systems in the trials are summarized by considering the specificity and sensitivity with respect to the scale of testing datasets. At last, we will discuss future challenges.

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Acknowledgements

We would like to thank NHMRC Australia and Diabetes Research Western Australia for funding the project (NHMRC Development Grant APP1093682, Diabetes Research Western Australia Grant 2018).

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Authors and Affiliations

  1. The Australian e-Health Research Centre, Health & Biosecurity Flagship, CSIRO, 147 Underwood Ave., Floreat, WA, 6014, Australia

    Di Xiao, Shaun Frost, Janardhan Vignarajan & Yogesan Kanagasingam

  2. iHealthScreen Inc., Richmond Hill, NY, 11367, USA

    Alauddin Bhuiyan

  3. The Royal Perth Hospital, 197 Wellington Street, Perth, WA, 6000, Australia

    Mei-Ling Tay-Kearney

Authors
  1. Di Xiao
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  2. Alauddin Bhuiyan
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  3. Shaun Frost
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  4. Janardhan Vignarajan
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  5. Mei-Ling Tay-Kearney
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  6. Yogesan Kanagasingam
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Corresponding author

Correspondence to Di Xiao.

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Xiao, D., Bhuiyan, A., Frost, S. et al. Major automatic diabetic retinopathy screening systems and related core algorithms: a review. Machine Vision and Applications 30, 423–446 (2019). https://doi.org/10.1007/s00138-018-00998-3

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  • DOI: https://doi.org/10.1007/s00138-018-00998-3

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