OCTage: monitoring the ageing brain via Optical Coherence Tomography of the eyes

大脑中与年龄相关的变化是决定我们的生活如何随着年龄变化的最重要的决定因素之一。大脑中唯一可以直接和非侵入性观察的部分是位于眼球后部的视网膜。视网膜实际上是脑组织的延伸，因此大脑因年龄和疾病而发生的变化往往反映在眼睛上。光学相干层析成像(OCT)是一种相对较新的技术，可以对活的视网膜组织的三维结构进行近微观细节的成像。OCT已经被广泛用于诊断、评估和监测青光眼等眼部疾病，以及监测糖尿病等全身疾病对眼睛的影响。然而，我们认为眼睛的OCT成像在监测大脑健康方面也有更大的潜力。我们已经知道，视网膜结构反映了年龄、性别和种族等人口统计因素，而特定的神经退行性疾病，如阿尔茨海默氏症和帕金森氏症，与视网膜内不同组织层厚度的变化有关。我们对使用OCT扫描来监测随着年龄增长的大脑健康的可能性感到兴奋。要做到这一点，需要检测和解释视网膜结构中非常细微的变化，比青光眼或老年性黄斑变性等眼病造成的巨大变化更难检测到。必须将关注的变化与不同人群中的正常可变性区分开来。而且已经缺乏训练有素的眼科医生，即使是根据OCT图像做出相对直接的诊断也是如此。我们建议通过使用人工智能(AI)技术来识别和描述随着年龄增长而发生的视网膜变化，无论是在健康老龄化还是在帕金森氏症等病理情况下都是如此。我们的愿景是有一天，可以为50岁以上的人提供OCT扫描作为健康检查的一部分。这些扫描便宜(约30 GB)，快速(不到两分钟)，无接触，完全无痛。然后，扫描可以通过自动化软件运行，该软件除了评估眼睛健康之外，还将评估患者是否走上了健康的老年道路，他们是否可能从早期干预(如更好的饮食或锻炼)中受益，或者他们是否应该被转介进行帕金森氏症等疾病的调查。这种筛查可以由商业街的验光师进行。实现这一愿景将需要克服后勤、伦理、技术和科学方面的挑战。人工智能要学习如何正确解释视网膜图像，它将需要对大量多样化、高质量、临床标记的数据进行培训，以确保不同人群的稳健性和可靠性。因此，该项目分为3个部分。首先，我们将利用当地NHS医院的档案和现有的国家科学基础设施，如英国生物库或英国健康数据研究中心，扩大我们现有的OCT扫描数据集。我们还与specsaver合作，在他们的验光商店收集扫描图像。其次，我们将设计人工智能方法来学习这个数据集，使用适合不同质量和数量的可用数据的技术。例如，即使在几乎没有临床信息的情况下，大型数据集也可以向人工智能传授种群内的变异性；相反，具有详细临床信息的较小数据集可以建议如何解释这种变异性。我们将生产两个关键的软件。其中一个是OCTageNet，它会通过一个人的眼睛来估计他的年龄。如果这比他们的实际年龄大，这可能表明他们没有健康地变老，干预可能会有所帮助。另一个是OCTagePath，旨在检测特定疾病的早期迹象，如帕金森氏症。第三，我们将与公众、患者和利益相关者接触，以更好地了解围绕大脑健康筛查的担忧和障碍

Age-related changes in the brain are one of the most important determinants of how our lives change with age. The only part of the brain that can be viewed directly and non-invasively is the retina at the back of the eye. The retina is actually an outgrowth of brain tissue, and thus changes in the brain due to age and disease are often reflected in the eyes.Optical Coherence Tomography (OCT) is a relatively new technique which can image the 3D structure of living retinal tissue in near microscopic detail. OCT is already widely used to diagnose, assess and monitor eye conditions such as glaucoma, as well as monitor how systemic conditions such as diabetes are affecting the eyes. However, we believe that OCT imaging of the eyes has much greater potential to monitor brain health as well. We already know that retinal structure reflects demographic factors such as age, sex and ethnicity, and that particular neurodegenerative conditions, such as Alzheimer's and Parkinson's disease, are associated with changes in the thickness of the different tissue layers within the retina. We are excited by the possibility of using OCT scans to monitor brain health as people age. Achieving this will require detecting and interpreting very subtle changes in retinal structure, far harder to detect than the gross changes caused by eye diseases like glaucoma or age-related macular degeneration. Changes of concern will have to be discriminated from normal variability within diverse populations. And there is already a shortage of ophthalmologists trained to make even relatively straightforward diagnoses from OCT images. We propose to address this by using artificial intelligence (AI) techniques to recognise and characterise the changes that occur in the retina as people age, both in healthy ageing and in the presence of pathologies such as Parkinson's. Our vision is that one day, people could be offered an OCT scan as part of over-50s health check. These scans are cheap (around £30), quick (under two minutes), contact-free and completely painless. The scan could then be run through automated software which, as well assessing eye health, would assess whether the person was on course for a healthy old age, whether they might benefit from early interventions (e.g. better diet or exercise) or whether they should be referred for investigations for conditions such as Parkinson's. This screening could be delivered by high-street optometrists.Achieving this vision will require overcoming logistical, ethical, technical, and scientific challenges. For the AI to learn how to interpret retinal images correctly, it will need training on very large amounts of diverse, high-quality, clinically-labelled data in order to ensure robustness and reliability across diverse populations. The project therefore has 3 parts. First, we will expand our existing dataset of OCT scans, drawing on archives from local NHS hospitals and existing national scientific infrastructure such as the UK Biobank or Health Data Research UK. We have also partnered with Specsavers to collect scans in their optometry stores. Second, we will design AI methods to learn from this dataset, using techniques geared to the different quality and quantity of data available. For example, large datasets can teach the AI about variability within the population, even with little clinical information; conversely, smaller datasets with detailed clinical information can then suggest how to interpret this variability. We will produce two key pieces of software. One, OCTageNet, will estimate a person's age from their eyes. Where this is older than their actual age, it may suggest that they are not ageing healthily and that intervention could be helpful. The other, OCTagePath, will aim to detect early signs of particular conditions such as Parkinson's.Third, we will engage with public, patients and stakeholders to better understand concerns and barriers around screening for brain health