AUTOMATED RETINAL IMAGE DIFFERENCING - Isis Project No 2158
Isis Innovation, the technology transfer arm of the University of Oxford, announces ARID - a new method for the assessment of diabetic retinopathy.
Marketing Opportunity
Diabetic retinopathy is a serious sight threatening condition. It is still the major cause of visual loss in the UK and the major cause of working age adult blindness. The risk of retinopathy may be reduced by attention to glycaemic control and the treatment of hypertension. If retinopathy is detected early, then laser treatment is effective in preventing the progression of the disease. The UK National Service Framework working party has recommended the use of camera technology for fundal photography using either film or digitisation with the emphasis being now on digital photography. However the scale of the management task for handling photographs is huge. Taking the conservative estimate that 2% of the UK population has diabetes implies over 1,000,000 ophthalmic screening visits every year leading to at least 2,000,000 photographs in the UK alone annually, for the USA population this equates to approximately 20,000,000 photographs.
The Oxford Invention
ARID is a computer system that highlights change between retinal images over time. ARID analyses similarities in images, matches them up, and identifies the changes between them. This demonstrates what exactly has changed to the user. It does not replace the retinal image screener, but reduces the workload of images that require grading.
ARID can be a care based system where clinicians will be able to detect change in the retina instantly as soon as a new image is taken. This is a real time procedure that will demonstrate to the patient why tight glucose control is important.
Project Status
Isis would like to talk to companies interested in developing the commercial opportunity that this represents. Please contact the Isis Project Manager to discuss this further.
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Request Further Information: Project Number 2158 - Automated Retinal Image Differencing