Automated feature-based grading and progression analysis of diabetic retinopathy

Lutfiah Al-Turk*, James Wawrzynski, Su Wang, Paul Krause, George M. Saleh, Hend Alsawadi, Abdulrahman Zaid Alshamrani, Tunde Peto, Andrew Bastawrous, Jingren Li, Hongying Lilian Tang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Background: In diabetic retinopathy (DR) screening programmes feature-based grading guidelines are used by human graders. However, recent deep learning approaches have focused on end to end learning, based on labelled data at the whole image level. Most predictions from such software offer a direct grading output without information about the retinal features responsible for the grade. In this work, we demonstrate a feature based retinal image analysis system, which aims to support flexible grading and monitor progression. 

Methods: The system was evaluated against images that had been graded according to two different grading systems; The International Clinical Diabetic Retinopathy and Diabetic Macular Oedema Severity Scale and the UK’s National Screening Committee guidelines. 

Results: External evaluation on large datasets collected from three nations (Kenya, Saudi Arabia and China) was carried out. On a DR referable level, sensitivity did not vary significantly between different DR grading schemes (91.2–94.2.0%) and there were excellent specificity values above 93% in all image sets. More importantly, no cases of severe non-proliferative DR, proliferative DR or DMO were missed. 

Conclusions: We demonstrate the potential of an AI feature-based DR grading system that is not constrained to any specific grading scheme.

Original languageEnglish
JournalEye (Basingstoke)
Early online date17 Mar 2021
Publication statusEarly online date - 17 Mar 2021

Bibliographical note

Funding Information:
Acknowledgements This project was funded by the National Plan for Science, Technology and Innovation (MAARIFAH)—King Abdulaziz

Funding Information:
City for Science and Technology—the Kingdom of Saudi Arabia— award number (10-INF1262–03). The authors also, acknowledge with thanks Science and Technology Unit, King Abdulaziz University for technical support. The authors thank the participants and teams from the Saudi Arabia, China and Kenya studies. The authors would like to thank the Engineering and Physical Sciences Research Council (EPSRC) in the UK for supporting the foundation of this work. GM Saleh’s contribution was part-funded and funded supported by the National Institute for Health Research (NIHR), Biomedical Research Centre based at Moorfields Eye Hospital, NHS Foundation Trust and UCL Institute of Ophthalmology. The views expressed here are those of the authors and not necessarily those of the Department of Health.

Publisher Copyright:
© 2021, The Author(s).

Copyright 2021 Elsevier B.V., All rights reserved.


  • eye
  • diabetic retinopathy
  • artificial intelligence

ASJC Scopus subject areas

  • Ophthalmology
  • Sensory Systems


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