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The role of haemorrhage and exudate detection in automated grading of diabetic retinopathy
  1. Alan D Fleming1,
  2. Keith A Goatman1,
  3. Sam Philip2,
  4. Graeme J Williams3,
  5. Gordon J Prescott4,
  6. Graham S Scotland5,
  7. Paul McNamee4,
  8. Graham P Leese6,
  9. William N Wykes7,
  10. Peter F Sharp1,
  11. John A Olson2
  12. on behalf of the Scottish Diabetic Retinopathy Clinical Research Network
  1. 1Biomedical Physics, University of Aberdeen, Foresterhill, Aberdeen, UK
  2. 2Diabetes Retinal Screening Service, David Anderson Building, Foresterhill, Aberdeen, UK
  3. 3Department of Ophthalmology, MacQuaker Building, Grange Road, Glasgow, Lanarkshire, UK
  4. 4Section of Population Health, University of Aberdeen, Foresterhill, Aberdeen, UK
  5. 5Health Economics Research Unit, University of Aberdeen, Foresterhill, Aberdeen, UK
  6. 6Department of Diabetes, Ninewells Hospital, Dundee, UK
  7. 7Department of Ophthalmology, Southern General Hospital, Glasgow, UK
  1. Correspondence to Mr Alan D Fleming, Department of Bio-Medical Physics and Bio-Engineering, Aberdeen University and Grampian University Hospitals, Foresterhill, Aberdeen AB25 2ZD, UK; a.fleming{at}


Background/aims Automated grading has the potential to improve the efficiency of diabetic retinopathy screening services. While disease/no disease grading can be performed using only microaneurysm detection and image-quality assessment, automated recognition of other types of lesions may be advantageous. This study investigated whether inclusion of automated recognition of exudates and haemorrhages improves the detection of observable/referable diabetic retinopathy.

Methods Images from 1253 patients with observable/referable retinopathy and 6333 patients with non-referable retinopathy were obtained from three grading centres. All images were reference-graded, and automated disease/no disease assessments were made based on microaneurysm detection and combined microaneurysm, exudate and haemorrhage detection.

Results Introduction of algorithms for exudates and haemorrhages resulted in a statistically significant increase in the sensitivity for detection of observable/referable retinopathy from 94.9% (95% CI 93.5 to 96.0) to 96.6% (95.4 to 97.4) without affecting manual grading workload.

Conclusion Automated detection of exudates and haemorrhages improved the detection of observable/referable retinopathy.

  • Pattern recognition
  • automated
  • vision screening
  • diabetic retinopathy
  • retina
  • photography
  • imaging

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  • Funding This project was funded by the Chief Scientist Office, Scottish Government Health Department (grant number CZH/4/316).

  • 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 Government.

  • Provenance and peer review Not commissioned; externally peer reviewed.

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