Early detection and diagnosis of age-related macular degeneration AMD using machine learning

使用机器学习早期检测和诊断年龄相关性黄斑变性 AMD

• 批准号: 2271375
• 金额: --
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2271375
• 关键词: Early; detection; diagnosis; age; related

AMD is the commonest cause of blindness in the elderly. By 2020, 200 million people are expected to be affected with AMD, increasing to nearly 300 million by 2040 [1]. It is a complex, heritable and heterogeneous disease that affects the macula, the central retina that is responsible for detailed central vision. The pathogenesis of AMD remains unclear. Drusen are an early clinical feature of the disease and are visualised as yellow deposits predominantly located throughout the macula. Currently, severity of AMD is classified as early, intermediate and late AMD based on the size and morphology of drusen and pigmentary changes at the macula. However, the inter-individual progression rates of AMD based on these characteristics are extremely variable.Recent advances in imaging technologies have enabled identification of other imaging biomarkers of AMD such as reticular pseudodrusen (subretinal deposits) that develop and progress independent of drusen [2]. Optical coherence tomography (OCT) also indicates that there are 38 different types of drusen that may co-exist in a patient and their significance in AMD progression is unknown [3]. Furthermore, not all high-risk features in the retina progress to late AMD within an individual highlighting the need for computational analysis to better understand the trajectories of these markers. Similarly, late AMD may present as geographic atrophy (GA) with atrophy of photoreceptors and retinal pigment epithelium (RPE) in the macula or choroidal neovascularization (CNV) where choroidal blood vessels migrate into the retina and it remains unknown why, when and which marker/event precede or predict each form of late AMD. Therefore, there is an unmet need to better understand the pathophysiology of AMD, accurately classify AMD and develop personalised risk prediction models to progress into prevention trials for this condition.The PhD project will be carried in collaboration with the PINNACLE consortium funded by the Wellcome Trust.References:[1] Wong, W.L., X. Su, X. Li, C.M. Cheung, R. Klein, C.Y. Cheng, and T.Y. Wong, Global prevalence of age-related macular degeneration and disease burden projection for 2020 and 2040: a systematic review and meta-analysis. The Lancet Global Health, 2014. 2(2): p. e106-e116.[2] Sivaprasad, S., A. Bird, R. Nitiahpapand, L. Nicholson, P. Hykin, I. Chatziralli, et al., Perspectives on reticular pseudodrusen in age-related macular degeneration. Surv Ophthalmol, 2016. 61(5): p. 521-37.[3] Khanifar, A.A., A.F. Koreishi, J.A. Izatt, and C.A. Toth, Drusen ultrastructure imaging with spectral domain optical coherence tomography in age-related macular degeneration. Ophthalmology, 2008. 115(11): p. 1883-90.

AMD是老年人失明的最常见原因。到2020年，预计将有2亿人受到AMD的影响，到2040年将增加到近3亿人[1]。它是一种复杂的、遗传性的和异质性的疾病，影响黄斑，即负责详细的中央视觉的中央视网膜。AMD的发病机制尚不清楚。玻璃疣是该疾病的早期临床特征，并且可视化为主要位于整个黄斑的黄色沉积物。目前，AMD的严重性基于黄斑处玻璃疣和色素改变的大小和形态被分类为早期、中期和晚期AMD。然而，基于这些特征的AMD的个体间进展率是极其可变的。成像技术的最新进展使得能够鉴定AMD的其他成像生物标志物，如网状假性玻璃疣（视网膜下沉积物），其发展和进展独立于玻璃疣[2]。光学相干断层扫描（OCT）还表明，患者中可能共存38种不同类型的玻璃疣，其在AMD进展中的意义尚不清楚[3]。此外，并非视网膜中的所有高风险特征在个体内进展为晚期AMD，这突出了对计算分析的需要，以更好地理解这些标记物的轨迹。类似地，晚期AMD可表现为地图状萎缩（GA），其中黄斑或脉络膜新生血管形成（CNV）中的光感受器和视网膜色素上皮（RPE）萎缩，其中脉络膜血管迁移到视网膜中，并且仍然不知道为什么、何时以及哪种标志物/事件先于或预测每种形式的晚期AMD。因此，有一个未满足的需求，以更好地了解AMD的病理生理学，准确地分类AMD和开发个性化的风险预测模型，以进展到这种情况的预防试验。该博士项目将进行合作与PINNACLE财团资助的威康信托基金。参考文献：[1]黄，W.L.，X. Su，X.李正民张河Klein，C.Y. Cheng和T.Y. Wong，年龄相关性黄斑变性的全球患病率和2020年和2040年的疾病负担预测：系统回顾和荟萃分析。《柳叶刀全球健康》，2014年。2（2）：第e106-e116页。[2]Sivaprasad，S.，A.伯德河尼蒂亚帕潘湖Nicholson，P. Hykin，I. Chatterdalli等人，年龄相关性黄斑变性中网状假性玻璃疣的观点。Surv Ophthalmol，2016年。61（5）：p.521 -37. [3]Khanifar，A.A.，A.F. Koreishi，J.A. Izatt和C.A. Toth，年龄相关性黄斑变性中玻璃疣超微结构的光谱域光学相干断层扫描成像。眼科，2008年。115（11）：p. 1883-90.