Predicting the future development of advanced age-related macular degeneration (AMD) using multi-modal imaging and genetics

使用多模态成像和遗传学预测晚期年龄相关性黄斑变性 (AMD) 的未来发展

• 批准号: 2410776
• 金额: --
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2410776
• 关键词: Predicting; future; development; advanced; age

1) Description and potential impact of the research Age-related macular degeneration (AMD) is the most common cause of blindness in the developed world. In the UK, more than 200 people develop the advanced blinding neovascular ("wet") form of the disease daily. Wet AMD typically affects one eye first, leaving patients reliant upon the unaffected "good" eye to allow the activities of daily living. Unfortunately, in many - but not all - patients, the good eye subsequently becomes affected, and the patient becomes severely sight impaired. As a result, a number of studies have begun to explore preventative therapies for the development of AMD progression, both in its wet and dry forms. In many cases, these treatments are invasive, with the potential for adverse effects. Robust methods for predicting future progression of AMD would thus allow better targeting of these therapies - such risk stratification could allow identification of those patients at risk of imminent conversion (i.e., development of advanced AMD within a six-month period) as well as those patients that could be reassured (i.e., unlikely to develop advanced AMD within the next two years). 2) Objectives- Develop machine learning systems for prediction of imminent AMD progression, defined as progression to choroidal neovascularization (CNV) or geographic atrophy (GA) within a 6-month period, using demographic and clinical metadata plus multi-modality imaging (colour fundus photography (CFP) / fundus autofluorescence (FAF), and high-resolution 3D optical coherence tomography (OCT)) from: 1) single time-point, and 2) longitudinal data. - Develop AMD "patient reassurance" models, defined as NO progression to choroidal neovascularization (CNV) or geographic atrophy (GA) within a 2-year period, using the same data. - Incorporate information on genetic variants and/or other diagnostic tests into AMD prediction models and evaluate its incremental effects on model performance in each clinical scenario.- Benchmark AMD prediction models against performance of human experts (ophthalmologists with subspecialty expertise in retinal disease at Moorfields Eye Hospital).- Explore preliminary clinical translation by validating model performance in prospective, non-interventional clinical studies.3) Novelty of Research MethodologyThe use of machine learning has shown great potential for retinal disease classification using imaging modalities. A number of studies have demonstrated the potential of deep learning to predict future AMD progression using retinal CFP and/or OCT scans. However, these studies have typically employed a single modality at a single time-point, producing good - but not spectacular - results. We will develop AMD progression models that incorporate longitudinal, multi-modal imaging data, and genetic data, and then demonstrate their potential clinical applicability. This project will involve the application of established technologies such as convolutional neural networks, as well as newer approaches such as graph neural networks. It will also involve more advanced modelling techniques such as neural ordinary differential equations. Lastly, it will involve both supervised and semi-supervised learning.4) Alignment to EPSRC's strategiesStrongly related to EPSRC's "Medical Imaging" research area and EPSRC's Healthcare Technology challenge to "Optimise Treatment and Care through effective diagnosis, patient-specific prediction and evidence-based intervention."5) CollaborationsThis project will use imaging data from Moorfields Eye Hospital. Affiliated with the UCL Institute of Ophthalmology, Moorfields has the world's largest single-centre ophthalmic imaging database (including >200,000 paired CFP and OCT scans from nearly 10,000 patients with advanced AMD). Since 2019, they have begun collecting gene they have begun collecting genetic data on these patients also.

年龄相关性黄斑变性（AMD）是发达国家最常见的致盲原因。在英国，每天有超过200人发展为晚期致盲的新血管（“湿”）形式的这种疾病。湿性AMD通常首先影响一只眼睛，使患者依赖于未受影响的“好”眼睛来进行日常生活活动。不幸的是，在许多（但不是所有）患者中，良好的眼睛随后受到影响，患者视力严重受损。因此，许多研究已经开始探索AMD进展的预防性治疗，包括湿性和干性。在许多情况下，这些治疗是侵入性的，有潜在的不良反应。因此，预测AMD未来进展的可靠方法将允许更好地靶向这些治疗-这种风险分层可以识别那些有即将转变风险的患者（即在六个月内发展为晚期AMD）以及那些可以放心的患者（即在未来两年内不太可能发展为晚期AMD）。2)目标-开发机器学习系统，用于预测即将发生的AMD进展，定义为在6个月内进展为脉膜新生血管（CNV）或地理萎缩（GA），使用人口统计学和临床元数据以及多模态成像（彩色眼底摄影（CFP) /眼底自体荧光（FAF）和高分辨率3D光学相干断层扫描（OCT））： 1）单一时间点，2)纵向数据。-开发AMD“患者安心”模型，定义为2年内NO进展为脉络膜新生血管（CNV）或地理萎缩（GA），使用相同的数据。-将遗传变异和/或其他诊断测试信息纳入AMD预测模型，并评估其在每种临床情况下对模型性能的增量影响。-对比人类专家（Moorfields眼科医院在视网膜疾病方面具有亚专业知识的眼科医生）表现的基准AMD预测模型。-通过验证模型在前瞻性、非介入性临床研究中的表现，探索初步的临床转化。3)研究方法的新颖性机器学习的使用在利用成像方式进行视网膜疾病分类方面显示出巨大的潜力。许多研究表明，利用视网膜CFP和/或OCT扫描，深度学习可以预测未来AMD的进展。然而，这些研究通常在单一时间点采用单一模式，产生良好但不引人注目的结果。我们将开发纳入纵向、多模态成像数据和遗传数据的AMD进展模型，然后展示其潜在的临床适用性。该项目将涉及卷积神经网络等现有技术的应用，以及图神经网络等较新的方法。它还将涉及更先进的建模技术，如神经常微分方程。最后，它将涉及监督和半监督学习。4)与EPSRC的战略保持一致与EPSRC的“医学成像”研究领域和EPSRC的医疗技术挑战密切相关，即“通过有效的诊断、针对患者的预测和循证干预来优化治疗和护理”。5)合作：本项目将使用Moorfields眼科医院的成像数据。Moorfields隶属于伦敦大学学院眼科研究所，拥有世界上最大的单中心眼科成像数据库（包括来自近10,000名晚期AMD患者的200,000对CFP和OCT扫描）。自2019年以来，他们开始收集基因，他们也开始收集这些患者的基因数据。