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.

项目总结