EYE-SCREEN-4-DPN: Development of an innovative Intelligent EYE imaging solution for SCREENing of Diabetic Peripheral Neuropathy

糖尿病是一个主要的全球性问题，在英国影响超过490万人。糖尿病每年花费> 100亿英镑（约占NHS预算的10%），其中80%的费用是由于糖尿病并发症，其中糖尿病周围神经病变（DPN）是最常见的。DPN是由糖尿病引起的神经损伤，可导致麻木，感觉丧失，脚，腿和手的神经相关疼痛。DPN还导致50-75%的非创伤性截肢，这是由于足部溃疡和最终感染的终末期后遗症。DPN筛查将通过早期干预改善护理，DPN更容易逆转。目前，由于缺乏敏感的、可扩展的基于人群的测试，没有有效的DPN筛查计划。首先，没有可靠、易于使用和准确的诊断工具适合DPN筛查（从而检测早期DPN）。目前的常规诊断测试是主观的或侵入性的，或无法评估小神经纤维（最早受到影响的神经纤维）。我们的团队率先使用非侵入性眼科测试，即角膜共焦显微镜（CCM）来成像角膜神经（眼睛前部的神经）。CCM是评估早期DPN的一个很好的测试。然而，由于需要直接接触角膜、患者不适、视野非常小、检查时间延长以及需要高水平的操作技能，CCM用于DPN筛查受到阻碍。另一个挑战是缺乏自动化、低成本、可靠和准确的方法来检测DPN并从角膜神经图像预测DPN的发生。人工评估成本高，而且由于主观性而容易出错。我们汇集了一批在各自领域具有丰富经验的世界级工程师、科学家、临床医生，开发出第一种针对DPN筛查需求量身定制的集成智能成像解决方案。具体目标是1。目的：研制一种阶跃式高分辨率光学相干断层扫描（OCT）装置，以取代并克服CCM对角膜神经进行非接触成像的局限性。OCT是一种快速、无创、非接触的成像技术，广泛应用于眼科诊所，包括社区验光师。然而，目前的临床OCT设备缺乏对角膜神经成像的分辨率。基于我们的专利OCT技术，我们将开发一种新的光学配置，以达到大视野下角膜神经成像所需的分辨率和速度，并实现全自动图像采集。2.开发新的智能算法（软件），以在床旁检测和预测DPN。分析随时间收集的大量不同类型的临床数据（纵向数据）（包括图像）的能力仍然是一个挑战。通过利用人工智能的最新进展，我们将开发能够区分DPN患者和非DPN患者的工具，这些患者将发展为DPN，并且在那些患者中会恶化，从而实现个性化护理和临床管理。 3.生产集成OCT设备和AI检测和预测（诊断/预后）的原型DPN筛查解决方案。这种创新的智能成像解决方案将是可部署的和临床医生友好的。4.为了证实在健康志愿者和糖尿病患者（有和没有DPN）中开发的创新技术的性能，在Aintree大学医院，DPN和CCM研究的临床卓越中心。总之，我们这个雄心勃勃的项目的近期目标是一个创新的DPN筛查解决方案，而长期目标是一个完全临床应用的技术，可以商业化。通过我们的创新，DPN的早期发现和及时治疗将预防残疾并挽救生命，为英国的社会和经济带来实质性利益。

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 are1. 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.