Personalizing Glaucoma Diagnosis by Disease Specific Patterns and Individual Eye Anatomy

项目概要/摘要 青光眼是一种视神经疾病，伴有视野（VF）丧失。同时准确的 VF 损失 诊断和检测其随时间的进展与临床医生高度相关，因为它表明 眼科治疗的开始或改变，为此目的的客观措施尚未达成共识，并且 众所周知，VF 测量通常不可靠。该项目的主要目标是开发临床 通过两种方法改善青光眼性 VF 丧失及其进展的诊断的适用措施： 首先，通过大规模、定制的方法来识别代表性损失模式及其进展 生物信息程序适用于来自九个临床中心的青光眼患者的数据，其次， 包含眼睛和患者特定的个性化参数。总共有 480,486 个 VF 可用于该项目。 一个主要目标是基于计算识别的进化模式开发新的诊断指数 VF 丧失，特别是 (1) 表示青光眼视力丧失概率的指数，以及 (2) 表示青光眼视力丧失概率的指数 为 VF 分配后续测量属于特定缺陷类别的概率。该指数将 对单独的 VF 样本进行统计评估并与现有方法进行比较。常规可用 影响青光眼视力丧失的患者特定参数包括患者种族、视力类型 青光眼、屈光不正的球面当量 (SE) 以及相对于固视的盲点位置。的 将系统地研究这些参数对视力丧失模式的影响。他们的包容性的影响 新的诊断指标及其对青光眼诊断的潜在改善将在以下方面进行量化 单独的数据集。进一步的目标是计算特定于测量的 VF 的空间图，该图表示 未来缺陷的首选心室颤动位置及其作为基于事件的进展诊断的辅助手段的可靠性。 第二个主要目标是研究 VF 损失与相关个体参数的关系 根据视盘周围的视网膜神经纤维层厚度 (RNFLT) 测量来了解视网膜结构。 RNFLT 代表性模式及其随时间的减少与主要轨迹的相互关系 系统地研究了视网膜动脉、SE 和盲点位置，以及对 VF 损失模式的影响 进行定量分析，目的是改善对现有 VF 损失的解释并预测未来 青光眼性视力丧失。该项目与临床实践相关的主要贡献已公开 新诊断指数和地图的开源软件实施，通过单独的功能增强 和结构参数，以及视网膜结构之间关系的详细和个性化模型 和青光眼性视力丧失。

Project Summary/Abstract Glaucoma is a disease of the optic nerve which is accompanied by visual field (VF) loss. While accurate VF loss diagnosis and the detection of its progression over time is of high relevance to clinical practitioners as it indicates the initiation of or change in ocular therapy, there is no consensus on objective measures for this purpose, and VF measurements are known to be often unreliable. The main objective of this project is to develop clinically applicable measures to improve the diagnosis of glaucomatous VF loss and of its progression by two approaches: First, the identification of representative loss patterns and their progression, achieved by large-scale, customized bioinformatical procedures applied to data from glaucoma patients from nine clinical centers and second, the inclusion of eye and patient specific personalized parameters. In total, 480,486 VFs, are available for this project. One major aim is to develop novel diagnostic indices based on computationally identified evolution patterns of VF loss, particularly (1) an index that denotes the probability of glaucomatous vision loss and (2) an index that assigns probabilities to a VF that follow-up measurements will be in a certain defect class. The indices will be statistically evaluated on separate VF samples and compared to existing approaches. Routinely available patient specific parameters which are candidates to impact glaucomatous vision loss are patient ethnicity, type of glaucoma, spherical equivalent (SE) of refractive error and the location of the blind spot relative to fixation. The effect of these parameters on the vision loss patterns will be systematically studied. The impact of their inclusion in the novel diagnostic indices and their potential improvement on glaucoma diagnosis will be quantified on a separate data set. A further aim is the calculation of a spatial map specific to a measured VF that represents the preferred VF locations of future defects as well as their reliability as an aid to event-based progression diagnosis. A second major objective is the investigation of the relationship of VF loss and individual parameters related to retinal structure, based on retinal nerve fiber layer thickness (RNFLT) measurements around the optic disc. The inter-relationship of representative patterns of RNFLT and its decrease over time with trajectories of major retinal arteries, SE, and blind spot location is systematically studied, and the impact on patterns of VF loss is quantitatively analyzed with the goal to improve the interpretation of existing VF loss and to predict future glaucomatous vision loss. Main contributions of the project with relevance to clinical practice are publicly available open-source software implementations of new diagnostic indices and maps, enhanced by individual functional and structural parameters, and a detailed and personalized model for the relationship between retinal structure and glaucomatous vision loss.