Realising the potential of retinal image analysis via AI methods for early detection of brain disease in the community

通过人工智能方法实现视网膜图像分析在社区早期检测脑部疾病方面的潜力

• 批准号: 2887450
• 金额: --
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2887450
• 关键词: Realising; potential; retinal; image; analysis

Changes in the brain leading to brain disease are thought to start decades before cognitive symptoms emerge. If biomarkers for these early stages could be identified, it would contribute to a more accurate estimation of an individual's risk of developing disease and enable the monitoring of high-risk (presymptomatic) people. This would also provide the means for assessing the efficacy of new interventions. The retina is an extension of the brain sharing embryological origins as well as a blood supply and nerve tissue. It, therefore, has huge potential as a site for biomarker investigation through easy, non-invasive imaging. Computational image analysis can then be used to reveal valuable information about microvascular health, deposition, and neurodegenerative damage. [1] The Scottish Collaborative Optometry-Ophthalmology Network e-research (SCONe, https://www.ed.ac.uk/ophthalmology/scone ) is a pioneering project established in 2020 which aims to build a world-leading nationwide retinal image resource for innovation in eye research and healthcare. In Scotland, community-based optometrists routinely collect millions of retinal images every year and many have been doing so for more than a decade. This represents a unique opportunity to create a large-scale longitudinal image resource that is representative of the primary care population. Within its initial two-year pilot phase, SCONe demonstrated the feasibility of bringing community-acquired retinal images for people aged 60+ together with other routinely collected healthcare data within the NHS National Safe Haven and has successfully acquired and linked over 200,000 images. Community-based early identification of individuals at risk of cognitive decline and dementia is currently a major unmet clinical need. Our solution is to exploit the potential embedded within retinal images to predict neurovascular health in the aging brain using routinely captured retinal images, cross-linked hospital datasets with information on symptoms/diagnosis, and the latest advances in Artificial Intelligence [2]. Based on this, we will devise a retinal neurovascular biomarker toolkit for cognitive decline, stroke, and dementia. Predictive modelling based on routinely collected retinal images poses a broad range of novel image analysis problems related to image quality control and enhancement, longitudinal image registration, and feature engineering [3]. AimsThe main aim of the study is to develop Artificial Intelligence approaches to the early identification of individuals at risk of cognitive decline and dementia, which is a major unmet clinical need. These solutions can then be deployed in the community (GP practices, optometrists, consumer devices). Progress towards this aim will be delivered based on the following objectives: Taking advantage of the data linkage capabilities of Public Health Scotland, link the SCONe repository of retinal image with hospital inpatient/outpatient electronic health records (e.g. SMR datasets) to identify a population of individuals over 60 years old with retinal images spanning over 10 years or more and incident diagnosis of cognitive decline, stroke or dementia. Develop novel approaches to quality scoring and retinal image enhancement using generative adversarial networks (GANs) to make this community acquired dataset suitable for latest advances in deep learning for image classification. Develop novel approaches to longitudinal retinal image registration and identification of key retinal phenotypes changing over time. Extend existing epidemiological models predicting the risk of brain health deterioration and incident disease with the SCONe linked retinal images, either in raw format at multiple points in time or after having characterised their temporal evolution in point 3.

人们认为，导致脑部疾病的大脑变化在认知症状出现之前几十年就开始了。如果这些早期阶段的生物标志物能够被识别出来，它将有助于更准确地估计个体发展疾病的风险，并能够监测高风险（症状前）人群。这也将为评估新干预措施的效力提供手段。视网膜是大脑的延伸，共享胚胎起源，以及血液供应和神经组织。因此，它具有巨大的潜力，可以通过简单、无创的成像来研究生物标志物。计算图像分析可以用来揭示有关微血管健康、沉积和神经退行性损伤的有价值的信息。b[1]苏格兰眼科合作网络电子研究（SCONe, https://www.ed.ac.uk/ophthalmology/scone）是一个成立于2020年的开创性项目，旨在为眼科研究和医疗创新建立一个世界领先的全国性视网膜图像资源。在苏格兰，以社区为基础的验光师每年都会定期收集数百万张视网膜图像，其中许多人已经这样做了十多年。这代表了一个独特的机会，可以创建一个大规模的纵向图像资源，代表初级保健人群。在最初的两年试点阶段，SCONe展示了将60岁以上人群的社区获取视网膜图像与NHS国家安全港内其他常规收集的医疗保健数据结合起来的可行性，并成功获取并链接了20多万张图像。以社区为基础的早期识别有认知能力下降和痴呆风险的个体是目前尚未满足的主要临床需求。我们的解决方案是利用视网膜图像中嵌入的潜力，通过常规捕获的视网膜图像、带有症状/诊断信息的交联医院数据集以及人工智能bbb的最新进展，来预测衰老大脑中的神经血管健康状况。在此基础上，我们将设计一个视网膜神经血管生物标志物工具包，用于认知能力下降、中风和痴呆。基于常规采集的视网膜图像的预测建模提出了一系列新的图像分析问题，涉及图像质量控制和增强、纵向图像配准和特征工程[10]。该研究的主要目的是开发人工智能方法来早期识别有认知能力下降和痴呆风险的个体，这是一个主要的未满足的临床需求。然后，这些解决方案可以在社区（全科医生、验光师、消费设备）中部署。实现这一目标的进展将基于以下目标：利用苏格兰公共卫生部的数据链接能力，将SCONe视网膜图像存储库与医院住院/门诊电子健康记录（例如SMR数据集）链接起来，以确定60岁以上的人群，他们的视网膜图像跨越10年或更长时间，并对认知能力下降、中风或痴呆症进行偶然诊断。使用生成对抗网络（gan）开发质量评分和视网膜图像增强的新方法，使该社区获得的数据集适合图像分类深度学习的最新进展。发展纵向视网膜图像配准的新方法和识别随时间变化的关键视网膜表型。扩展现有的流行病学模型，预测大脑健康恶化和突发疾病的风险，使用与SCONe相关的视网膜图像，无论是在多个时间点的原始格式，还是在第3点描述了它们的时间演变之后。