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Br J Ophthalmol. 2007 Nov;91(11):1512-7. Epub 2007 May 15.

The efficacy of automated "disease/no disease" grading for diabetic retinopathy in a systematic screening programme.

Author information

1
Biomedical Physics and Grampian Retinal Screening Programme, University of Aberdeen, Foresterhill, Aberdeen.

Abstract

AIM:

To assess the efficacy of automated "disease/no disease" grading for diabetic retinopathy within a systematic screening programme.

METHODS:

Anonymised images were obtained from consecutive patients attending a regional primary care based diabetic retinopathy screening programme. A training set of 1067 images was used to develop automated grading algorithms. The final software was tested using a separate set of 14 406 images from 6722 patients. The sensitivity and specificity of manual and automated systems operating as "disease/no disease" graders (detecting poor quality images and any diabetic retinopathy) were determined relative to a clinical reference standard.

RESULTS:

The reference standard classified 8.2% of the patients as having ungradeable images (technical failures) and 62.5% as having no retinopathy. Detection of technical failures or any retinopathy was achieved by manual grading with 86.5% sensitivity (95% confidence interval 85.1 to 87.8) and 95.3% specificity (94.6 to 95.9) and by automated grading with 90.5% sensitivity (89.3 to 91.6) and 67.4% specificity (66.0 to 68.8). Manual and automated grading detected 99.1% and 97.9%, respectively, of patients with referable or observable retinopathy/maculopathy. Manual and automated grading detected 95.7% and 99.8%, respectively, of technical failures.

CONCLUSION:

Automated "disease/no disease" grading of diabetic retinopathy could safely reduce the burden of grading in diabetic retinopathy screening programmes.

PMID:
17504851
PMCID:
PMC2095421
DOI:
10.1136/bjo.2007.119453
[Indexed for MEDLINE]
Free PMC Article

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