Development of New Low Cost Point of Care Diagnostic Technologies for Diabetic Retinopathy in China

我们将开发新型、低成本的诊断技术，用于检测中国的致盲疾病糖尿病视网膜病变（DR）。这些技术将使非专业卫生保健工作者能够在中国对患者进行护理的时间和地点进行具有成本效益的大规模视力威胁疾病检测。中国有超过1.1亿人患有糖尿病，预计到2040年这一数字将增加到1.5亿。糖尿病通过损害眼睛后部视网膜的血管而导致视力丧失。治疗是有效的（在中国也越来越负担得起），但前提是及早发现这种疾病。提高DR的早期诊断和治疗水平是中国政府《国家眼健康五年规划（2016-2020）》的主要目标之一。目前检测DR的方法依赖于昂贵的成像设备和许多熟练的人员来拍摄和解释视网膜图像。中国具备这些技能的卫生工作者很少。在西欧具有成本效益的病例发现战略不可能以中国所需的规模复制。基于利物浦大学、北京大学和中华医学会（CMA）之间的现有合作，我们来自利物浦和中国的工程师、统计学家、教育专家、眼科医生和卫生经济学家组成的联合研究团队有能力开发一种适合当地需求的新型诊断成像解决方案。我们这个项目的目标是：1。开发一种新型、低成本、强大的DR2检测成像设备。开发新的自动计算机算法，对视网膜图像进行评估，以发现威胁视力的疾病。开发一种新的比较判断方法，从自动计算机算法中细化DR严重程度分级4。为了在英国验证新技术，并在中国进行测试，以确保最大的成本效益，我们将开发一种专门针对中国需求的新型低成本相机。该设备将产生高质量的彩色图像和视网膜的光学相干断层扫描（OCT）图像。OCT广泛可用，但目前的商用系统非常昂贵。我们的新设备将基于我们正在申请专利的新技术，并将首先在人类捐献组织上进行测试。我们将开发和评估新的自动化图像分析技术，使计算机能够学习分析视网膜的彩色和OCT图像。我们的目标是生产能够在患者护理时间和地点区分患有和不患有DR的患者以及患有轻度/中度和严重疾病的患者的系统。为了达到高水平的诊断准确性（超过自动图像分析），我们将开发和评估一个新的人类学习系统。该系统将利用中国卫生工作者的集体判断，根据DR的严重程度对图像进行排序。我们将建立一个自我维持的活动网络，让新手和专家在做出有效的临床判断时相互支持。我们将开发新的统计方法，以支持该系统，并评估诊断的准确性和卫生工作者的表现。我们的成像和诊断系统将在英国的241名糖尿病患者中进行验证。然后，我们在中国的项目团队将对461名患者进行试点研究，并进行成本计算。研究了该系统的诊断准确性及其成本效益。我们将通过在CMA中担任领导角色的中国团队与政策制定者进行接触。发现和治疗威胁视力的DR将预防残疾，有利于中国社会和中国经济。我们的系统将提高卫生保健工作者的技能，加强现有的卫生系统，并在中国建立研究能力。通过开源软件传播我们的技术将使其他低收入和中等收入国家的利益最大化。

We will develop novel, low-cost, diagnostic technologies for the detection of a blinding condition called diabetic retinopathy (DR) in China. These technologies will enable cost-effective, large-scale detection of sight-threatening disease to be performed by non-expert health care workers at the time and place of patient care in China.Over 110 million people in China live with diabetes and this number is expected to increase to 150m by 2040. Diabetes causes visual loss through damage to blood vessels of the retina at the back of the eye. Treatments are effective (and increasingly affordable in China) but only if the disease is detected early enough. Improving early diagnosis and treatment of DR is one of the principal objectives of the Chinese Government's 5-Year National Plan of Eye Health (2016-2020). Current methods of detecting DR rely on costly imaging equipment and many skilled personnel to take and interpret retinal images. China has very few health workers with these skills. Case detection strategies which are cost-effective in Western Europe cannot possibly be replicated at the scale necessary for China.Building on an existing collaboration between the University of Liverpool, Peking University and the Chinese Medical Association (CMA), our joint research team from Liverpool and China of engineers, statisticians, education specialists, eye doctors and health economists are well placed to develop a new diagnostic imaging solution tailored to local needs. Our objectives for this project are:1. To develop a novel, low-cost, robust imaging device for detection of DR2. To develop new automated computer algorithms to rate images of the retina for sight-threatening disease3. To develop a novel comparative judgement method to refine the DR severity grading from automated computer algorithms4. To validate the new technologies in the UK and test them in China to ensure maximum cost-effectivenessWe will develop a new low-cost, camera designed specifically for the needs of China. This device will produce both high-quality colour images and optical coherence tomography (OCT) images of the retina. OCT is widely available but current commercial systems are very expensive. Our new device will be based on our novel patent-pending technology and will be first tested on human donor tissue. We will develop and evaluate new, automated image analysis techniques allowing computers to learn to analyse both colour and OCT images of the retina. We aim to produce systems capable of differentiating between patients with and without DR and between those with mild/moderate and severe disease at the time and place of patient care.In order to achieve a high level of diagnostic accuracy (over and above automated image analysis), we will develop and evaluate a new human learning system. This system will harness the collective judgements of Chinese health workers to rank images in terms of DR severity. We will create a self-sustaining network of activity where novices and experts support each other in making effective clinical judgements. We will develop new statistical methods to underpin the system and to evaluate both diagnostic accuracy and performance of the health workers. Our imaging and diagnostic system will be validated in 241 patients with diabetes in the UK. Our project team in China will then undertake a pilot study of 461 patients and a costing exercise. The diagnostic accuracy of the system and its cost-effectiveness will be investigated.We will engage policy makers through our Chinese team who occupy leading roles in the CMA. Detection and treatment of sight threatening DR will prevent disability with benefit to Chinese society and China's economy. Our systems will upskill health care workers, strengthen existing health systems and build research capacity in China. Dissemination of our techniques through open-source software will maximise benefits for other low and middle-income countries.