Personalizing Glaucoma Diagnosis by Disease Specific Patterns and Individual Eye Anatomy

根据疾病特定模式和个体眼睛解剖结构进行个性化青光眼诊断

• 批准号: 10245094
• 负责人: Tobias Elze
• 金额: $ 44.83万
• 依托单位: SCHEPENS EYE RESEARCH INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-30 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10245094
• 关键词: Anatomy; Atrophic; Axon; Bioinformatics; Blindness; Clinical; Cluster Analysis; Computer software; Consensus; Custom; Data; Data Set; Defect; Detection; Development; Diagnosis; Diagnostic; Disease; Ethnic Origin; Event; Evolution; Eye; Future; Glaucoma; Goals; Hemorrhage; Impairment; Individual; Investigation; Length; Location; Maps; Measurement; Measures; Modeling; Morphologic artifacts; Nerve Fibers; Optic Disk; Optical Coherence Tomography; Patients; Pattern; Probability; Procedures; Prognosis; Public Health; Refractive Errors; Retina; Retinal Defect; Retinal Ganglion Cells; Retinal blind spot; Sampling; Structure; Structure-Activity Relationship; System; Thick; Time; Variant; Vision; Visual Fields; Work; base; central retinal artery; clinical application; clinical center; clinical practice; disease diagnosis; follow-up; fovea centralis; improved; indexing; multidisciplinary; neglect; novel diagnostics; open source; optic nerve disorder; retinal nerve fiber layer; sample fixation; sex; statistical learning

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.

项目摘要/摘要 青光眼是一种伴有视觉诱发电位(VF，VF)丧失的视神经疾病。同时准确的VF损失 正如它所表明的那样，诊断和检测其随时间的进展对临床医生具有高度的相关性 关于眼科治疗的启动或改变，对于为此目的采取的客观措施没有达成共识，以及 众所周知，VF测量通常是不可靠的。该项目的主要目标是开发临床应用 通过两种方法改进青光眼室颤丢失及其进展的诊断的适用措施： 首先，Identifi的代表性损失模式及其进展，实现了大规模、定制化 生物信息学程序应用于来自九个临床中心的青光眼患者的数据，第二， 包括眼睛和患者特定fic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机识别的进化模式开发新的诊断指数 VF损失，特别是(1)表示青光眼视力损失概率的指数和(2) 为VF分配后续测量将处于特定缺陷类别的概率。这些指数将 对不同的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候选参数包括患者种族、fi类型 青光眼、屈光不正的球面当量(SE)和盲点相对于fi的位置。这个 这些参数对视力损失模式的影响将被系统地研究。他们被包括在内的影响 在新的诊断指标及其对青光眼诊断的潜在改进中，将基于fi 单独的数据集。另一个目的是计算空间映射fic到表示 未来缺陷的首选VF位置以及它们的可靠性，以帮助基于事件的进展诊断。 第二个主要目标是研究室性心动过速损失与相关个体参数的关系 视网膜结构，根据视盘周围的视网膜神经fi层厚度(Rnflt)测量。 RNFLT的典型模式及其随时间的减少与主要运动轨迹的相互关系 系统地研究了视网膜动脉、SE和盲点定位，以及对VF丢失模式的影响 定量分析的目的是改进对现有VF损失的解释并预测未来 青光眼视力丧失。该项目与临床实践相关的主要贡献已公开提供 开放源码软件实施新的诊断指数和地图，通过单独的功能增强 和结构参数，以及详细和个性化的视网膜结构之间的关系模型 以及青光眼视力丧失。