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

A-EYE：整合计算机建模和深度学习来优化湿性年龄相关性黄斑变性的诊断和治疗

• 批准号: 2599504
• 金额: --
• 依托单位: University of Liverpool
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2599504
• 关键词: EYE; Integrating; silico; Modelling; Deep

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.

黄斑变性（AMD）是导致失明的主要原因之一。在其“湿”形式中，异常的新血管在视网膜中生长，导致视网膜结构的出血、瘢痕和物理变形。黄斑是眼睛的一部分，负责高分辨率，中心视力，视网膜结构的这种破坏对视力特别有害。目前，湿性AMD的治疗包括玻璃体内注射抑制新血管生长的分子。无论这种疗法对于保护视力有多大的革命性，它都没有解决湿性AMD的根本原因，因此必须经常重复注射，这对患者来说是一个巨大的负担，对医疗保健系统来说是一个巨大的成本。光学相干断层血管成像（Optical Coherence Tomography Angiography，OCTA）是一种新型、快速、无创、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了解。它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类为对当前治疗具有不同反应的亚型。另外，OCTA提供视网膜、其脉管系统和其中的血流的三维表示。三维信息已经显示出提供更有效的生物标志物（例如，异常流体体积）比先前的二维信息（例如，尽管深度学习方法在医学成像中得到了广泛的应用，但深度学习方法，特别是卷积神经元网络（CNN）尚未应用于对OCTA扫描的新生血管进行分类，这也是该博士项目的目标之一。额外的新奇将是在使用三维分割的微血管作为输入的算法。此外，学习算法将由我们的计算机模型计算的血管参数提供信息，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，很少有计算机模拟方面的工作（即，计算机模拟模型）。这种模型依赖于生理学上的假设和数学方程，以提供对系统内工作的可能非线性关系的理解[2]。此外，它们允许快速，廉价的实验“计算机”，这是一个有效的工具，用于试验新的治疗方案。将通过计算机模拟研究健康和患病眼睛中视网膜中视网膜血流的功能。之后，将在患病眼睛的模型中结合血流和代谢反应，以研究患者对治疗的反应，并探索新治疗方案和新治疗方式的可能性。作为该项目的一部分，将开发一个管道（见图1），从OCTA图像到分割的血管系统，再到血流和药物治疗的个性化计算机模拟模型[3]。临床数据将从皇家利物浦大学医院的圣保罗眼科中心收集。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可翻译性到临床至关重要。