Automated Retinal Image Analysis for Diabetic Retinopathy in Telemedicine

Overview

Journal Curr Diab Rep

Publisher Current Science

Specialty Endocrinology

Date 2015 Feb 21

PMID 25697773

Citations 25

Authors

Dawn A Sim

Pearse A Keane

Adnan Tufail

Catherine A Egan

Lloyd Paul Aiello

Paolo S Silva

Affiliations

Soon will be listed here.

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.

Citing Articles

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A Detailed Systematic Review on Retinal Image Segmentation Methods.

Panda N, Sahoo A J Digit Imaging. 2022; 35(5):1250-1270.

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Comparison of early diabetic retinopathy staging in asymptomatic patients between autonomous AI-based screening and human-graded ultra-widefield colour fundus images.

Sedova A, Hajdu D, Datlinger F, Steiner I, Neschi M, Aschauer J Eye (Lond). 2022; 36(3):510-516.

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