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The efficacy of automated "disease/no disease" grading for diabetic retinopathy in a systematic screening programme
  1. Sam Philip (sam.philip{at}arh.grampian.scot.nhs.uk),
  2. Alan D Fleming (a.fleming{at}abdn.ac.uk),
  3. Keith A Goatman (k.goatman{at}biomed.abdn.ac.uk),
  4. Sofia Fonseca,
  5. Paul Mcnamee (p.mcnamee{at}abdn.ac.uk),
  6. Graham S Scotland (g.scotland{at}abdn.ac.uk),
  7. Gordon J Prescott (gordon.prescott{at}abdn.ac.uk),
  8. Peter F Sharp (p.sharp{at}biomed.abdn.ac.uk),
  9. John Alexander Olson (john.olson{at}nhs.net)
  1. NHS Grampian Retinal Screening Programme, United Kingdom
  2. Biomedical Physics, University of Aberdeen, United Kingdom
  3. Biomedical Physics, University of Aberdeen, United Kingdom
  4. Department of Public Health, University of Aberdeen, United Kingdom
  5. Health Economics Research Unit, University of Aberdeen, United Kingdom
  6. Health Economics Research Unit, University of Aberdeen, United Kingdom
  7. Medical Statistics, Department of Public Health, University of Aberdeen, United Kingdom
  8. Biomedical Physics, University of Aberdeen, United Kingdom
  9. NHS Grampian Retinal Screening Programme, United Kingdom

    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 14406 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 ungradable images (technical failures) and 62.5% as having no retinopathy. Detection of technical failures and 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.

    • Automated grading
    • Diabetic retinopathy
    • Image analysis

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