Systematic screening for diabetic retinopathy using digital retinal photography has been shown to reduce the incidence of blindness amongst people with diabetes. There are many challenges to the implementation of widespread screening and use of automation is one way to improve screening efficiency and ensure consistency and accuracy of grading review.

Current approaches to automated detection of retinopathy have now reached the limit of human intra-reader variability. iGradingM is the only commercially available automated diabetic retinopathy screening platform to have been validated within an organised, systematic screening programme.

Use of iGradingM allows:

Validation of image quality for diabetic retinopathy screening; Accurate and objective detection of the presence of microaneurysms; Management of diabetic retinopathy screening workload through filtration of retinal images and identification of those which have signs of disease Significant workload reduction

iGradingM has been tried, tested and validated against an unparalleled number of data sets and the algorithms deployed within it are the subject of many publications in peer-reviewed journals by the academic and clinical development team at the University of Aberdeen and NHS Grampian. The multidisciplinary team in Aberdeen have been involved in the optimisation of diabetic retinopathy screening practice for over 14 years. Key publications supporting the current algorithms are cited below:

Automated grading for diabetic retinopathy: a large‐scale audit using arbitration by clinical experts. Fleming AF, Goatman KA, Philips S, Prescott GL, Sharp PF & Olson JA. Br J Ophthalmol 2010; 94:1606‐1610 The efficacy of automated “disease/no disease” grading for diabetic retinopathy in a systematic screening programme. Philip S, Fleming AD, Goatman KA, et al. Br J Ophthalmol 2007; 91: 1512‐1517 Automated assessment of diabetic retinal image quality based on clarity and field definition. Fleming AD, Philip S, Goatman KA, Olson JA & Sharp PF. Invest Ophthalmol Vis Sci 2006;47:1120‐1125 Automated microaneurysm detection using local contrast normalisation and local vessel detection. Fleming AD, Philip S, Goatman KA, Olson JA & Sharp PF. IEEE Trans Med Imaging2006;25(9):1223‐1232

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