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The efficacy of automated “disease/no disease” grading for diabetic retinopathy in a systematic screening programme
  1. S Philip1,
  2. A D Fleming2,
  3. K A Goatman2,
  4. S Fonseca3,
  5. P Mcnamee4,
  6. G S Scotland4,
  7. G J Prescott3,
  8. P F Sharp2,
  9. J A Olson5
  1. 1
    Biomedical Physics and Grampian Retinal Screening Programme, University of Aberdeen, Foresterhill, Aberdeen
  2. 2
    Biomedical Physics, University of Aberdeen, Foresterhill, Aberdeen
  3. 3
    Department of Public Health, University of Aberdeen, Foresterhill, Aberdeen
  4. 4
    Health Economics Research Unit, University of Aberdeen, Foresterhill, Aberdeen
  5. 5
    Retinal Screening, David Anderson Building, Foresterhill Road, Aberdeen
  1. Dr John A Olson, Clinical Director, Diabetes Retinal Screening Service, David Anderson Building, Foresterhill Road, Aberdeen AB25 2ZP; John.olson{at}nhs.net

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.

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Footnotes

  • Competing interests: Implementation in Scotland is being considered. If this occurs it is likely that there will be some remuneration for the University of Aberdeen, NHS Grampian and the Scottish Executive.

  • Funding: This project was funded by the Chief Scientist Office, Scottish Executive Health Department (grant number CZH/4/76).

  • Ethics approval: Ethics approval was obtained from the Grampian Medical Research Ethics Committee for the use of the anonymised images and grading data.

  • Abbreviation:
    DH/MA

    dot haemorrhage/microaneurysm

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