Relationship between Glaucoma and the Three-Dimensional Optic Nerve Head Related Structure

青光眼与三维视神经头相关结构的关系

• 批准号: 10316448
• 负责人: Mengyu Wang
• 金额: $ 24.72万
• 依托单位: SCHEPENS EYE RESEARCH INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-02-01 至 2024-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10316448
• 关键词: 3-Dimensional; Age; Anatomy; Biomechanics; Blindness; Bruch' s basal membrane structure; Cessation of life; Clinical; Clinical Data; Computer Simulation; Cross-Sectional Studies; Data; Data Science; Development; Diagnosis; Diagnostic; Ear; Elements; Eye; Fundus; Gaussian model; Geometry; Glaucoma; Hour; Image; Individual; Injury; Lead; Linear Models; Linear Regressions; Location; Machine Learning; Measurement; Mechanics; Medical Records; Methods; Modeling; Monitor; Morphologic artifacts; Multivariate Analysis; Nerve Fibers; Observational Study; Ophthalmology; Optic Disk; Optic Nerve; Optical Coherence Tomography; Participant; Pathogenesis; Patients; Pattern; Phase; Physiologic Intraocular Pressure; Play; Population Study; Process; Quality of life; Research; Research Training; Resolution; Retinal Ganglion Cells; Role; Scanning; Scheme; Scientist; Severities; Site; Solid; Source; Structure; Structure-Activity Relationship; Surface; Techniques; Testing; Thick; Translating; Variant; Vision; Visual Fields; Width; archetypal analysis; base; cell injury; clinical Diagnosis; clinical practice; clinically relevant; deep neural network; demographics; fundus imaging; image processing; improved; independent component analysis; insight; knowledge base; machine learning method; mathematical model; nerve damage; nonlinear regression; optic cup; retina blood vessel structure; retinal imaging; retinal nerve fiber layer; study population; training opportunity; unsupervised learning

Project Summary Glaucoma is the second leading cause of blindness globally, and is characterized by optic nerve damage that leads to the death of retinal ganglion cells with accompanying visual field (VF) loss. The optic nerve head (ONH) is the site of injury to the optic nerve fibers and plays a central role in glaucoma pathogenesis and diagnosis. Traditionally, glaucoma is diagnosed based on fundus inspection of the ONH, which provides information about the surface contour of the ONH. However, the optic nerve damage occurs in the deeper layers. With the development of optical coherence tomography (OCT) techniques for three-dimensional (3D) retinal imaging, parameters derived from the 3D ONH related structure (e.g., Bruch's membrane opening minimum rim width, peripapillary retinal nerve fiber layer thickness, disc tilt etc.) have been studied to better understand glaucoma pathogenesis, and are used to supplement clinical diagnosis. In addition, studies of the ONH biomechanics have also shown that the strain level at the ONH at any given intraocular pressure level depends on the 3D geometry of the ONH related structure. A high strain level is hypothesized to contribute to retinal ganglion cell injury. Previous research has suggested that the 3D ONH related structure is correlated to glaucoma pathogenesis and critically important to glaucoma diagnosis. However, to date, a systematic study using clinical data to determine the impact of the 3D ONH related structure on glaucoma has not been conducted. We propose to study the relationship between the 3D ONH related structure and glaucoma with a diverse set of combined techniques including image processing, computational mechanics and machine learning. The specific aims of this project are to: (1) Derive features from the 3D ONH related structure and study their implications on VF loss patterns (K99 Phase). (2) Investigate the impact of the strain field patterns at the ONH on glaucoma (K99 Phase). (3) Study the effect of the 3D ONH related features on OCT diagnostic parameters (R00 Phase). (4) Model central vision loss from the 3D ONH related structural features (R00 Phase). Collectively, these studies will provide new insights and perspectives into the structure-function relationships in glaucoma and establish ocular anatomy specific norms of retinal nerve fiber layer profiles, which will advance our current understanding of glaucoma pathogenesis and improve glaucoma diagnosis. Our research is of high clinical relevance and can be potentially translated into clinical practice for better glaucoma diagnosis, monitoring and treatment. Through the proposed research and training plans, the applicant will build a solid knowledge base in ophthalmology and further improve his expertise in mathematical modeling and data science. This project will provide critical training opportunities to further enhance the applicant's capabilities to become an independent computational vision scientist in ophthalmology.

项目概要 青光眼是全球第二大失明原因，其特点是视神经损伤， 导致视网膜神经节细胞死亡并伴有视野（VF）丧失。视神经乳头 (ONH) 是视神经纤维损伤的部位，在青光眼发病机制和诊断中起着核心作用。 传统上，青光眼是根据 ONH 的眼底检查来诊断的，该检查可提供以下信息： ONH 的表面轮廓。然而，视神经损伤发生在较深层。随着用于三维（3D）视网膜成像的光学相干断层扫描（OCT）技术的发展，参数 源自 3D ONH 相关结构（例如，Bruch 膜开口最小边缘宽度、peripapillary 视网膜神经纤维层厚度、椎间盘倾斜等）已被研究以更好地了解青光眼发病机制， 并用于补充临床诊断。此外，ONH生物力学的研究也表明 在任何给定眼压水平下 ONH 的应变水平取决于 ONH 的 3D 几何形状 相关结构。假设高应变水平会导致视网膜神经节细胞损伤。先前的研究 表明 3D ONH 相关结构与青光眼发病机制相关并且至关重要 来诊断青光眼。然而，迄今为止，一项使用临床数据来确定影响的系统研究 青光眼的3D ONH相关结构尚未进行。 我们建议通过多种方式研究 3D ONH 相关结构与青光眼之间的关系 包括图像处理、计算力学和机器学习在内的组合技术。该项目的具体目标是：（1）从3D ONH相关结构中导出特征并研究其含义 关于 VF 丢失模式（K99 阶段）。 (2) 研究ONH应变场模式对青光眼的影响 （K99 阶段）。 (3)研究3D ONH相关特征对OCT诊断参数（R00阶段）的影响。 (4) 3D ONH 相关结构特征（R00 阶段）的中央视力丧失模型。总的来说，这些研究将为青光眼的结构与功能关系提供新的见解和视角，并建立 视网膜神经纤维层轮廓的眼部解剖学特定规范，这将推进我们目前的理解 青光眼发病机制并提高青光眼诊断水平。我们的研究具有很高的临床相关性，可以 有可能转化为临床实践，以更好地诊断、监测和治疗青光眼。 通过拟议的研究和培训计划，申请人将建立坚实的眼科知识基础，并进一步提高其在数学建模和数据科学方面的专业知识。该项目将提供 关键的培训机会，以进一步提高申请人成为眼科独立计算视觉科学家的能力。