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Artificial Intelligence Screening for Diabetic Retinopathy: the Real-World Emerging Application

Current Diabetes Reports volume 19, Article number: 72 (2019) Cite this article

Abstract

Purpose of Review

This paper systematically reviews the recent progress in diabetic retinopathy screening. It provides an integrated overview of the current state of knowledge of emerging techniques using artificial intelligence integration in national screening programs around the world. Existing methodological approaches and research insights are evaluated. An understanding of existing gaps and future directions is created.

Recent Findings

Over the past decades, artificial intelligence has emerged into the scientific consciousness with breakthroughs that are sparking increasing interest among computer science and medical communities. Specifically, machine learning and deep learning (a subtype of machine learning) applications of artificial intelligence are spreading into areas that previously were thought to be only the purview of humans, and a number of applications in ophthalmology field have been explored. Multiple studies all around the world have demonstrated that such systems can behave on par with clinical experts with robust diagnostic performance in diabetic retinopathy diagnosis. However, only few tools have been evaluated in clinical prospective studies.

Summary

Given the rapid and impressive progress of artificial intelligence technologies, the implementation of deep learning systems into routinely practiced diabetic retinopathy screening could represent a cost-effective alternative to help reduce the incidence of preventable blindness around the world.

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Funding

Funding from Research Grants Council-General Research Fund, Hong Kong (Ref: 14102418); National Medical Research Council Health Service Research Grant, Large Collaborative Grant, Ministry of Health, Singapore; the SingHealth Foundation; and the Tanoto Foundation.

Author information

Author notes

  1. Valentina Bellemo and Gilbert Lim contributed equally to this work.

Authors and Affiliations

  1. Singapore National Eye Centre, Singapore Eye Research Institute, 11 Third Hospital Avenue, Singapore, 168751, Singapore

    Valentina Bellemo, Gilbert Lim, Tyler Hyungtaek Rim, Gavin S. W. Tan & Daniel Shu Wei Ting

  2. School of Computing, National University of Singapore, Singapore, Singapore

    Gilbert Lim, Mong Li Lee & Wynne Hsu

  3. Duke-NUS Medical School, Singapore, Singapore

    Tyler Hyungtaek Rim, Gavin S. W. Tan & Daniel Shu Wei Ting

  4. Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong

    Carol Y. Cheung

  5. Doheny Eye Institute, University of California, Los Angeles, CA, USA

    SriniVas Sadda

  6. Center of Eye Research Australia, Melbourne, Victoria, Australia

    Ming-guang He

  7. Moorfields Eye Hospital & Institute of Ophthalmology, UCL, London, UK

    Adnan Tufail

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  1. Valentina Bellemo
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Contributions

VB, GL, and DT contributed to the initial drafting of the manuscript. VB, GL, TR, GT, CC, SS, MH, AT, ML, WH, and DT all contributed to the critical review and final approval for this manuscript.

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Correspondence to Daniel Shu Wei Ting.

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Conflict of Interest

Dr. Gilbert Lim, Prof. Mong Li Lee, Prof. Wynne Hsu, and Dr. Daniel S.W. Ting are the co-inventors of the deep learning system for detection of retinal diseases (patent on Automated Retinal Image Analysis Software for Referable Diabetic Retinopathy, Glaucoma Suspect and Age-Related Macular Degeneration 10201706186V [Singapore]). Dr. Ming-guang He is a co-inventor of another deep learning system for detection of retinal diseases (patent on managing color fundus images using deep learning models [ZL201510758675.5]). Valentina Bellemo, Dr. Tyler Hyungtaek Rim, Dr. Gavin S.W. Tan, Dr. Carol Y. Cheung, Dr. SriniVas Sadda, Prof. Ming-guang He, and Prof. Adnan Tufail declare that they have no conflict of interest.

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

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This article is part of the Topical Collection on Microvascular Complications—Retinopathy

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Bellemo, V., Lim, G., Rim, T.H. et al. Artificial Intelligence Screening for Diabetic Retinopathy: the Real-World Emerging Application. Curr Diab Rep 19, 72 (2019). https://doi.org/10.1007/s11892-019-1189-3

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  • DOI: https://doi.org/10.1007/s11892-019-1189-3

Keywords

  • Artificial intelligence
  • Deep learning
  • Diabetic retinopathy screening
  • Retinal images
  • Tele-medicine
  • Survey