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Automated Retinal Image Analysis for Diabetic Retinopathy in Telemedicine

  • Microvascular Complications—Retinopathy (JK Sun, Section Editor)
  • Published:
Current Diabetes Reports Aims and scope Submit manuscript

Abstract

There will be an estimated 552 million persons with diabetes globally by the year 2030. Over half of these individuals will develop diabetic retinopathy, representing a nearly insurmountable burden for providing diabetes eye care. Telemedicine programmes have the capability to distribute quality eye care to virtually any location and address the lack of access to ophthalmic services. In most programmes, there is currently a heavy reliance on specially trained retinal image graders, a resource in short supply worldwide. These factors necessitate an image grading automation process to increase the speed of retinal image evaluation while maintaining accuracy and cost effectiveness. Several automatic retinal image analysis systems designed for use in telemedicine have recently become commercially available. Such systems have the potential to substantially improve the manner by which diabetes eye care is delivered by providing automated real-time evaluation to expedite diagnosis and referral if required. Furthermore, integration with electronic medical records may allow a more accurate prognostication for individual patients and may provide predictive modelling of medical risk factors based on broad population data.

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Abbreviations

ARIA:

Automated retinal image analysis

JVN:

Joslin Vision Network

CADe:

Computer-aided detection

CADx:

Computer-aided diagnosis

ETDRS:

Early treatment diabetic retinopathy study

ARIS:

Automated Retinal Imaging System

PCA:

Principal component analysis

DRS:

Diabetic retinopathy study

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Acknowledgments

The research was supported by a grant from Fight for Sight, UK (Grant number 1987), The Special Trustees of Moorfields Eye Hospital and the National Institute for Health Research (NIHR) Biomedical Research Centre based at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology.

Compliance with Ethics Guidelines

ᅟ

Conflict of Interest

Dawn A. Sim, Pearse A. Keane, Adnan Tufail, Lloyd Paul Aiello and Paolo S. Silva declare that they have no conflict of interest.

Catherine A. Egan reports grants and personal fees from Novartis. Her husband is a retinal specialist and receives honoraria, grants and speakers fees from academic and pharmaceutical industry relevant to this work.

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. Department of Ophthalmology, Harvard Medical School and Beetham Eye Institute, Joslin Diabetes Center, One Joslin Place, Boston, MA, 02215, USA

    Dawn A. Sim, Lloyd Paul Aiello & Paolo S. Silva

  2. NIHR Biomedical Research Centre for Ophthalmology, Moorfields Eye Hospital NHS Foundation Trust, London, UK

    Dawn A. Sim, Pearse A. Keane, Adnan Tufail & Catherine A. Egan

  3. Institute of Ophthalmology, University College London, London, UK

    Dawn A. Sim, Pearse A. Keane & Adnan Tufail

  4. Teleophthalmology and Image Reading Center, Philippine Eye Research Institute, National Institutes of Health, University of the Philippines, Manila, Philippines

    Paolo S. Silva

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  1. Dawn A. Sim
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Correspondence to Paolo S. Silva.

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

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Sim, D.A., Keane, P.A., Tufail, A. et al. Automated Retinal Image Analysis for Diabetic Retinopathy in Telemedicine. Curr Diab Rep 15, 14 (2015). https://doi.org/10.1007/s11892-015-0577-6

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