Public Funding for Seon Diagnostics Limited

The ADDRESS project will enable Sēon Diagnostics Limited (SDL) to build upon extensive research and development activity to date and create the next generation system to automatically detect retinal abnormalities that are diabetic retinopathy in people living with diabetes. Every one of the estimated 460 million people living with diabetes1 is at risk of developing complications that can lead to damage to the light-sensitive membrane at the back of the eye (the retina). Undetected and untreated, this damage can lead to sight impairment and even blindness. Early detection is achieved through routine photography of the retina and the examination of these photographs by highly skilled practitioners who can spot the very early signs of problems and refer the patient to an eye clinic. Early detection and treatment all but eliminates the risk of severe sight impairment. In a handful of countries, diabetic eye screening programmes (DESPs) have been established and these have proved very successful. DESPs are, however, costly to run and there is a world-wide shortage of skilled practitioners to staff them. Consequently, the vast majority of those living with diabetes do not have routine eye examinations and are at risk. Existing automated systems to detect diabetic retinopathy have a number of limitations that have hampered their uptake. The solution that SDL is developing will overcome these limitations in a unique and proprietary way, allowing the company to establish itself as market leader in automated eye screening. The key output of the ADDRESS project is the proof of concept for an automated system that will enable (1) existing DESPs to overcome the shortage of trained practitioners and substantially lower their running costs and (2) countries where no DESP exists to establish one at low cost and with limited human resources. At the close of the project, the technology (named SēonDx-Retina) will be ready for submission to regulatory authorities for clearance to be commercialised.

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"Diabetic Retinopathy (DR) is a common complication of diabetes mellitus, which affects around half of 430m diabetics worldwide (WHO). It is a major cause of blindness (\>7% UK blindness) but can be easily ameliorated if detected and treated early. Hence the importance of annual screening when images of the retina are taken and reviewed by qualified graders for symptomatic features. Diabetic Eye Screening Programmes (DESP) are run in the UK and other countries, however they are labour intensive, slow and expensive (annual NHS screening cost of \>£100m). Automated Retinal Image Analysis Systems (ARIASs) are emerging as a powerful tool for improving DR screening efficiencies and clinical outcomes. However, current systems only work with conventional narrow-field fundus cameras, limiting imaging to approximately 20% of the retina surface and leaving 80% out of view. This restricts their ability to detect some of the most harmful eye disease, such as diabetic retinopathy and ocular tumours, which occur frequently in the far periphery of the eye. Ophthalmic best practice is driving adoption of a new generation of Ultra-Wide-Field cameras, capable of imaging 85% of the retina. However, while UWF images are larger and more costly to grade manually (up to 5x), no ARIAS exists capable of reliably analysing UWF images for disease. This creates a significant, unmet and growing need which this project responds to. In this project, RetinaScan Ltd (RSL) partners with the leading NHS-DESP at Gloucester and Surrey University (CVSSP) to meet this challenge by developing the world's first AI competent to analyse UWF retinal images for DR. We will further innovate our successful A-CNN deep-learning architecture to achieve high detection performance and robustness to UWF distortions such as blurring and occlusions. The key outcomes of the R&D will be: (i) a fully capable prototype suite of trained UWF-Augmented-CNN technology; (ii) prototype validation via NHS user trials; and a detailed business plan for commercialisation as a cloud-based service for markets globally. Significant economic benefits will be realised by healthcare providers globally who deliver retinopathy screening services by UWF imaging with ARIAS."

"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."