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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

基本信息

批准号：
10454416
负责人：
Tobias Elze
金额：
$ 44.56万
依托单位：
SCHEPENS EYE RESEARCH INSTITUTE
依托单位国家：
美国
项目类别：
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/10454416
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

项目摘要

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/文摘