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Details of Grant 

EPSRC Reference: EP/X01441X/1
Title: EYE-SCREEN-4-DPN: Development of an innovative Intelligent EYE imaging solution for SCREENing of Diabetic Peripheral Neuropathy
Principal Investigator: Zheng, Professor Y
Other Investigators:
Shen, Professor Y Alam, Dr U
Researcher Co-Investigators:
Project Partners:
University of Manchester, The Weill Cornell Medical College in Qatar
Department: Eye and Vision Sciences
Organisation: University of Liverpool
Scheme: Standard Research
Starts: 01 August 2023 Ends: 31 July 2027 Value (£): 1,019,988
EPSRC Research Topic Classifications:
Med.Instrument.Device& Equip. Medical Imaging
EPSRC Industrial Sector Classifications:
Related Grants:
Panel History:
Panel DatePanel NameOutcome
27 Sep 2022 Healthcare Technologies Investigator Led Panel Sept 2022 Announced
Summary on Grant Application Form
Diabetes is a major global problem, affecting over 4.9 million people in the UK. Diabetes costs >£10 billion per annum (~10% of the NHS budget) and 80% of these costs are due to diabetes complications, of which diabetic peripheral neuropathy (DPN) is the commonest. DPN is is nerve damage caused by diabetes and can lead to numbness, loss of sensation, nerve-related pain in the feet, legs, and hands. DPN is also responsible for 50-75% of non-traumatic limb amputations due end stage sequelae of foot ulceration and eventual infection.

Screening of DPN will improve care by enabling early intervention where DPN is more readily reversible. At present, there is no effective screening programme for DPN due to a lack of sensitive, scalable population-based tests.

First, there are no reliable, easy-to-use and accurate diagnostic tools fit for DPN screening (thus detecting early DPN). Current routine diagnostic tests are subjective or invasive or inability to assess small nerve fibres (the earliest nerve fibres to be affected). Our group has pioneered the use of a non-invasive eye test, namely corneal confocal microscopy (CCM) to image the corneal nerves (nerves at the front of the eye). CCM is an excellent test for the assessment of early DPN. However, the use of CCM for DPN screening has been hindered due to the need for direct contact with the cornea, patient discomfort, very small field of view, prolonged examination time, and requiring a high level of operational skills. The other challenge is the lack of automated, low-cost, reliable and accurate ways detect DPN and predict the occurrence of DPN from corneal nerve images. Manual assessment is expensive and prone to errors due to subjectiveness.

We have brought together a group of world-class engineers, scientists, clinicians with extensive experience in their respective fields to develop the first kind of integrated intelligent imaging solution tailored to the needs of DPN screening.

The specific objectives are

1. To develop a step-change ultrahigh resolution optical coherence tomography (OCT) device to replace and overcome the limitations of CCM for non-contact imaging the corneal nerves. OCT is a fast, non-invasive, non-contact imaging technique that widely used in eye clinics including community optometrists. However, current clinical OCT devices lack the resolution to image the corneal nerves. Based on our patented OCT technology, we will develop a new optical configuration to achieve the desired resolving power and speed for imaging the corneal nerves with a large field of view, and achieve fully automatic image acquisition.

2. To develop new intelligent algorithms (software) to detect and predict DPN at the point-of-care. The ability to analyse a large amount of differing types of clinical data collected over time (longitudinal data) including images remains a challenge. By leveraging the recent advances in artificial intelligence, we will produce tools capable of distinguishing between patients with and without DPN, people who will progress to DPN, and in those which it will worsen thus enabling personalised care and clinical management.

3. To produce a prototype DPN screening solution integrating the OCT device and the AI detection and prediction (diagnostic/prognostic). This innovative intelligent imaging solution will be deployable and clinician-friendly.

4. To confirm the performance of the developed innovative technologies in healthy volunteers and people with diabetes (with and without DPN) at the Aintree University Hospital, a centre of clinical excellence in DPN and CCM research.

In summary, our immediate goal of this ambitious project is an innovative DPN screening solution, whilst the long-term goal is a fully clinically utilised technology which can be commercialised. Early detection and timely treatment of DPN by our innovations will prevent disability and save lives with substantial benefit to the UK's society and economy.

Key Findings
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Organisation Website: http://www.liv.ac.uk