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.

项目总结/摘要 青光眼是一种视神经疾病，伴有视野（VF）丧失。虽然准确的VF损失 诊断和检测其随时间的进展对临床从业者具有高度相关性， 眼部治疗的开始或改变，对此目的的客观措施尚未达成共识，以及 已知VF测量通常是不可靠的。该项目的主要目标是在临床上开发 通过以下两种方法改善对昏迷性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 生物信息学程序应用于来自9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的个性化参数。总共有480，486个自愿捐款可用于该项目。 一个主要目标是开发新的诊断指数的基础上计算识别的艾德进化模式， VF丧失，特别是（1）表示昏迷性视力丧失概率的指数和（2） 为VF分配后续测量将在特定缺陷类别中的概率。指数将 在单独的VF样本上进行统计评估，并与现有方法进行比较。可以定期获取 影响昏迷性视力丧失的候选患者特异性参数包括患者种族、 青光眼、屈光不正的等效球镜（SE）和盲点相对于角膜的位置。的 将系统地研究这些参数对视力丧失模式的影响。将其纳入的影响 新的诊断指标及其对青光眼诊断的潜在改善将在一个 单独的数据集。另一个目的是计算特定于测量的VF的空间图，其表示 未来缺陷的首选VF位置及其可靠性，以辅助基于事件的进展诊断。 第二个主要目的是研究VF损失与相关个体参数的关系。 视网膜结构，基于视盘周围的视网膜神经纤维层厚度（RNFLT）测量。 RNFLT的代表性模式及其随时间的减少与主要轨迹的相互关系 系统研究了视网膜动脉、SE和盲点位置，以及对VF丢失模式的影响。 定量分析的目的是改善现有VF损失的解释，并预测未来 昏迷性视力丧失。该项目与临床实践相关的主要贡献是公开的 新的诊断指数和图谱的开源软件实现，通过个人功能增强 和结构参数，并为视网膜结构之间的关系建立了详细和个性化的模型 和昏迷性视力丧失。