A-EYE: Integrating In-silico Modelling and Deep Learning to Optimize Diagnosis and Treatment of Wet Age-related Macular Degeneration

老年性黄斑变性(AMD)是导致失明的主要原因之一。在“湿”状态下，不正常的新血管在视网膜中生长，导致出血、结疤和视网膜结构的物理变形。黄斑是眼睛中负责高分辨率中央视觉的部分，这种视网膜结构的破坏对视力尤其有害。目前，湿性AMD的治疗包括玻璃体内注射一种抑制新生血管生长的分子。无论这种疗法对保护视力有多大的革命性作用，它都不能解决湿性AMD的根本原因，因此必须经常重复注射，这给患者带来了巨大的负担，也给医疗保健系统带来了成本。此外，患者对治疗的反应也非常不同。光学相干断层扫描血管成像(OCTA)是一种新的、快速、非侵入性和高分辨率的视网膜血管成像工具。OCTA允许对健康和患病眼睛的视网膜微血管系统进行新的洞察。它正越来越多地被用作湿性AMD的诊断工具。OCTA的应用之一是将新生血管分为对当前治疗有不同反应的亚型。此外，OCTA提供了视网膜、其血管系统和其中的血液流动的三维表示。三维信息已被证明比以前的二维信息(如视网膜中央厚度)提供了更有效的生物标志物(如异常液体体积)[1]。尽管它在医学成像中得到了广泛应用，但深度学习方法，特别是卷积神经网络(CNN)尚未被应用于从OCTA扫描中对新生血管进行分类，这也是本博士项目的目标之一。另外的新颖性将是使用微血管的三维分割作为算法的输入。此外，学习算法将通过我们的硅胶模型中计算的血管系统参数来提供信息，以潜在地提高其准确性和可解释性，这是人工智能固有的两个主要挑战。目前，大多数研究都集中在从数据中学习，对生理学的计算机模拟(即计算机内模型)的工作很少。这样的模型依赖于生理上知情的假设和数学方程来提供对系统内工作的、可能是非线性的关系的理解[2]。此外，它们还允许进行快速、廉价的“硅内”实验，这是试验新治疗方案的有效工具。健康眼和患病眼的视网膜血流功能将在电子显微镜下进行研究。随后，血流和代谢反应将在患病眼睛的模型中结合起来，以调查患者对治疗的反应，并探索新的治疗方案和新的治疗方式的可能性。作为这项工作的一部分，将开发一条管道(见图1)，从OCTA图像到分段血管系统，再到个性化的血流硅胶模型和药物治疗[3]。临床数据将从皇家利物浦大学医院的圣保罗眼科工作中收集。与眼科团队的密切合作对于开发准确的模型以及保持一定程度的临床可译性至关重要。

Age-related macular degeneration (AMD) is one of the leading causes of blindness. In its 'wet' form, abnormal new vessels grow in the retina, causing bleeding, scarring and physical deformation of the retinal structure. The macula being the part of the eye responsible for high resolution, central vision, this disruption of the retinal structure is particularly detrimental to vision. Currently, treatment for wet AMD consists of intravitreal injections of a molecule inhibiting growth of the neovasculature. Regardless of how much of a revolution this kind of therapy has been for conserving sight, it does not address the underlying cause of wet AMD and therefore injections must be repeated often, representing a substantial burden for the patient and cost for the health care system. Furthermore, patients respond very differently to the treatment.Optical Coherence Tomography Angiography (OCTA) is a novel, quick, non-invasive, and high-resolution tool for imaging the retinal vasculature. OCTA allows novel insight into the retinal microvasculature in healthy and diseased eyes. It is becoming increasingly used as a diagnostic tool for wet AMD. One of the applications of OCTA is to classify neovasculatures into subtypes which have different response to the current treatment. Additionally, OCTA provides a three-dimensional representation of the retina, its vasculature, and the blood flow therein. The three-dimensional information has been shown to provide more efficient biomarkers (e.g., abnormal fluid volumes) than the previous two-dimensional information (e.g., central retinal thickness) [1].Despite its extensive use in medical imaging, deep learning methods and in particular convolutional neuron networks (CNN) have yet to be applied to classifying neovasculatures from OCTA scans and is one of the goals of this PhD project. Additional novelty will be in the use of three-dimensional segmentations of the microvasculature as an input for the algorithm. Furthermore, the learning algorithm will be informed by computed parameters of the vasculature from our in-silico models to potentially improve its accuracy and explainability, two of the major challenges inherent to artificial intelligence. Currently, most research has been focussing on learning from data, with little work on computer simulations (I.e., in-silico models) of the physiology. Such models rely on physiologically informed assumptions and mathematical equations to provide understanding of the, possibly nonlinear, relationships at work within a system [2]. Furthermore, they allow for quick, inexpensive experiments 'in-silico' which are an efficient tool for trials of new treatment protocols. The function of retinal blood flow in the retina in both healthy and diseased eyes will be investigated in-silico. Later, blood flow and metabolic response will be combined in models of the diseased eye to investigate patient's responsiveness to treatment and explore the possibilities for new treatment regime and new therapy modalities. As part of this, a pipeline (see Fig. 1) will be developed to go from OCTA images, to segmented vasculature, to personalised in-silico models of blood flow and drug treatment [3].Clinical data will be collected from the St Paul's Eye Unit work at the Royal Liverpool University Hospital. The close collaboration with the ophthalmic team is essential for the development of accurate models as well as maintaining a degree of translatability into the clinic.