Artificial Intelligence for Assessment of Stargardt Macular Atrophy

项目摘要 Stargardt病是最常见的遗传性青少年黄斑变性。眼底 自体荧光(FAF)是一种广泛使用的成像技术，可能有助于Stargardt的诊断 疾病，通常用于监测其进展情况。FAF成像提供了视网膜的活体分析 但这只是一种间接措施。相比之下，光谱域光学相干层析成像(SD-OCT)， 提供视网膜微结构的三维可视化，从而允许对其进行评估 Stargardt病患者的直接和个别诊断。在视网膜疾病终点与食物的会议上 美国药品监督管理局(FDA)于2016年11月，可靠地衡量了解剖状态的完整性 在视网膜中的椭球区(EZ)，被认为是一个潜在的合适的调节终点 治疗干预临床试验。手动分割/识别EZ波段，特别是在3-D中 事实证明，OCT图像非常繁琐、耗时和昂贵。自动目标器 分割技术，例如使用深度学习-人工智能(AI)构造的方法， 会有很大的价值。此外，Stargardt病可能会导致儿童和幼儿严重视力丧失。 成年人。早期预测Stargardt病的进展可能会促进新的治疗试验。因此，这一点 Proposal开发了一种基于人工智能的自动Stargardt萎缩分割和预测方法 Faf和OCT图像中的萎缩进展。更具体地说，我们首先记录纵向Faf和OCT 分别对图像进行拼接，并将横截面Faf图像配准为OCT图像。然后我们开发一种2-D 一种基于AI和3D的Faf图像Stargardt萎缩分割方法 使用基于3-D图形的方法从OCT图像中分割EZ频段。最后，基于人工智能的方法是 用于预测新的Stargardt萎缩的后续发展或现有萎缩的进展 本次就诊患者的OCT EZ波段厚度和强度特征。

Project Abstract Stargardt disease is the most frequent form of inherited juvenile macular degeneration. Fundus autofluorescence (FAF) is a widely available imaging technique which may aid in the diagnosis of Stargardt disease and is commonly used to monitor its progression. FAF imaging provides an in vivo assay of the retinal layers, but is only an indirect measure. Spectral-domain optical coherence tomography (SD-OCT), in contrast, provides three-dimensional visualization of the retinal microstructure, thereby allowing it to be assessed directly and individually in eyes with Stargardt disease. At a retinal disease endpoints meeting with the Food and Drug Administration (FDA) in November of 2016, a reliable measure of the anatomic status of the integrity of the ellipsoid zone (EZ) in the retina, was proposed to be a potential suitable regulatory endpoint for therapeutic intervention clinical trials. Manual segmentation/identification of the EZ band, particularly in 3-D OCT images, has proven to be extremely tedious, time-consuming, and expensive. Automated objective segmentation techniques, such as an approach using a deep learning - artificial intelligence (AI) construct, would be of significant value. Moreover, Stargardt disease may cause severe visual loss in children and young adults. Early prediction of Stargardt disease progression may facilitate new therapeutic trials. Thus, this proposal develops an AI-based approach for automated Stargardt atrophy segmentation and the prediction of atrophy progression in FAF and OCT images. More specifically, we first register the longitudinal FAF and OCT enface images respectively, and register the cross-sectional FAF to OCT image. We then develop a 2-D approach for Stargardt atrophy segmentation from FAF images using an AI approach and a 3-D approach for EZ band segmentation from OCT images using a 3-D graph-based approach. Finally, an AI-based approach is developed to predict subsequent development of new Stargardt atrophy or progression of existing atrophy from the OCT EZ band thickness and intensity features of the current patient visit.