RetinaScan: AI-enabled automated image assessment system for diabetic retinopathy screening
Lead Participant:
RETINASCAN LIMITED
Abstract
"Diabetes mellitus (DM) is a global healthcare problem. In 2014 there were 422m diabetics, forecast to rise to 642m by 2040 \[WHO\]. Diabetic Retinopathy (DR) is a common complication (\>50% of sufferers) caused by physiological changes in the retina. It is a major cause of blindness (\>7% UK blindness) but easily ameliorated through laser or drug treatment.
Annual routine screening enables DR to be captured and treated early. Images of the retina are taken using readily available cameras for qualified people to review for symptomatic features. Few countries have managed to run a diabetic eye screening programme (DESP), the UK being one. Whilst highly effective, current DESPs are:
* labour intensive, requiring manual grading of images by up to 3 specialists
* slow, with a targeted 6-week turnaround, impacting on patient retention
* expensive, generating an annual NHS screening cost of \>£100m
Automated retinal image analysis systems (ARIAS) utilise image analysis algorithms to detect disease features. ARIAS have shown potential to transform DESP delivery with speed, non-scaling cost of operation and significant cost savings. However, achieving accuracy at the level required to provide an effective replacement of level 1 human grading has not yet been realised.
RetinaScan meets this challenge through an innovative ARIAS solution: an advanced algorithm methodology design by experts in diabetology associated with the University of Oxford; with novel AI based imaging analysis systems (convolution neural networks - CNNs) developed at the University of Surrey. Led by Retinopathy Answer Ltd (RAL), a proof of concept prototype has been devised and validated. RALs Augmented-CNN design brings together deep understanding of imaging data and methods to automate consistently qualified human levels of performance.
Advancing on this prototype, RetinaScan will: i) further develop the system architecture for end-user scenarios; ii) advance retina scan datasets for system training and testing; iii) develop regional and eye level image processing engines for accurate disease grading; and iv) develop and demonstrate a complete web-based system prototype through user trials.
The key outcomes of the research will be: a fully capable prototype suite of trained Augmented-CNN technology; ii) prototype validation via user trials; and a detailed business plan for commercialisation as a cloud-based service for markets globally.
The potential addressable global market for ARIAS is estimated at \>£2.98 billion. The partnership targets ~£14.98 million business growth within a 5-year period (~£27.89m cumulative sales), creating \>35 new jobs and generating a \>30-fold ROI."
Annual routine screening enables DR to be captured and treated early. Images of the retina are taken using readily available cameras for qualified people to review for symptomatic features. Few countries have managed to run a diabetic eye screening programme (DESP), the UK being one. Whilst highly effective, current DESPs are:
* labour intensive, requiring manual grading of images by up to 3 specialists
* slow, with a targeted 6-week turnaround, impacting on patient retention
* expensive, generating an annual NHS screening cost of \>£100m
Automated retinal image analysis systems (ARIAS) utilise image analysis algorithms to detect disease features. ARIAS have shown potential to transform DESP delivery with speed, non-scaling cost of operation and significant cost savings. However, achieving accuracy at the level required to provide an effective replacement of level 1 human grading has not yet been realised.
RetinaScan meets this challenge through an innovative ARIAS solution: an advanced algorithm methodology design by experts in diabetology associated with the University of Oxford; with novel AI based imaging analysis systems (convolution neural networks - CNNs) developed at the University of Surrey. Led by Retinopathy Answer Ltd (RAL), a proof of concept prototype has been devised and validated. RALs Augmented-CNN design brings together deep understanding of imaging data and methods to automate consistently qualified human levels of performance.
Advancing on this prototype, RetinaScan will: i) further develop the system architecture for end-user scenarios; ii) advance retina scan datasets for system training and testing; iii) develop regional and eye level image processing engines for accurate disease grading; and iv) develop and demonstrate a complete web-based system prototype through user trials.
The key outcomes of the research will be: a fully capable prototype suite of trained Augmented-CNN technology; ii) prototype validation via user trials; and a detailed business plan for commercialisation as a cloud-based service for markets globally.
The potential addressable global market for ARIAS is estimated at \>£2.98 billion. The partnership targets ~£14.98 million business growth within a 5-year period (~£27.89m cumulative sales), creating \>35 new jobs and generating a \>30-fold ROI."
Lead Participant | Project Cost | Grant Offer |
|---|---|---|
| RETINASCAN LIMITED | £643,856 | £ 450,699 |
Participant |
||
| RETINASCAN LTD | ||
| UNIVERSITY OF SURREY | ||
| THE UNIVERSITY OF SURREY | £340,860 | £ 340,860 |
People |
ORCID iD |
| Iain Rae (Project Manager) |