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: 10018038
• 负责人: Tobias Elze
• 金额: $ 47.34万
• 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/10018038
• 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; 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; outcome forecast; 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 青光眼是视力神经的一种疾病，是通过视觉场（VF）损失完成的。而准确的VF损失 随着时间的流逝，诊断及其进展的检测与临床从业者高度相关，因为它表明 为此目的，目的的客观措施尚无共识，并且 VF测量通常是不可靠的。该项目的主要目的是在临床上开发 适用的措施，通过两种方法来改善青光眼VF损失及其进展的诊断： 首先，通过大规模定制实现代表性损失模式及其进步的识别 生物信息程序应用于来自9个临床中心青光眼患者的数据，其次 包括眼睛和患者特定参数。该项目总共可以使用480,486个VF。 一个主要目的是基于计算上识别的演化模式来开发新颖的诊断指数 VF损失，特别是（1）指数表示青光眼视力丧失的可能性，以及（2）指数 将后续测量的可能性分配给VF，将处于特定缺陷类中。指数将 在单独的VF样本上进行统计评估，并将其与现有方法进行比较。通常可用 患者特定的参数是影响青光眼视力丧失的候选者，是患者种族，类型 青光眼，折射率的球形等效（SE）以及相对于固定的盲点的位置。 这些参数对视力损失模式的影响将是系统地研究的。他们包容的影响 在新的诊断指数及其对青光眼诊断的潜在改善将在A上进行量化 单独的数据集。另一个目的是计算指定的空间图指定为代表的VF 首选未来缺陷的VF位置及其可靠性，作为基于事件的进展诊断的帮助。 第二个主要目标是VF损失与各个参数相关的关系的投资 基于视网膜神经纤维层厚度（RNFLT）测量值周围的视网膜结构。 RNFLT代表性模式的相互关系及其随着时间的流逝而随着主要的轨迹而减少 视网膜动脉，SE和盲点位置是系统地研究的，对VF损失模式的影响 定量分析以改善现有VF损失的解释并预测未来的目标 青光眼视力丧失。该项目与临床实践有关的主要贡献是公开的 新诊断指标和地图的开源软件实现，通过单个功能增强 和结构参数，以及残留结构之间关系的详细和个性化模型 和青光眼视力丧失。