Deep Learning Approaches to Detect Glaucoma and Predict Progression from Spectral Domain Optical Coherence Tomography

通过谱域光学相干断层扫描检测青光眼并预测进展的深度学习方法

• 批准号: 10055661
• 负责人: Mark Christopher
• 金额: $ 11.73万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2022-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10055661
• 关键词: 3-Dimensional; Affect; Age; American; Artificial Intelligence; Award; Biometry; Blindness; Caring; Characteristics; Clinic; Clinical; Computational Technique; Cornea; Data; Data Set; Decision Making; Detection; Development; Diagnosis; Disease; Disease Progression; Engineering; Ensure; Evaluation; Eye; Eye diseases; Frequencies; Glaucoma; Image; Imaging Techniques; Individual; Learning; Length; Measurement; Measures; Medicine; Mentors; Modeling; Monitor; Ophthalmology; Optic Disk; Optical Coherence Tomography; Optics; Participant; Patient Care; Patients; Performance; Phase; Population; Primary Open Angle Glaucoma; Probability; Progressive Disease; Race; Research; Research Personnel; Retina; Scanning; Severities; Severity of illness; South Korea; Standardization; Structure; Structure-Activity Relationship; Supervision; Techniques; Texture; Thick; Thinness; Three-Dimensional Image; Three-Dimensional Imaging; Training; Translating; United States; Universities; Vision; Visual Fields; Visualization; Width; Work; base; career development; clinical care; college; computer science; deep learning; experience; field study; imaging modality; improved; improved outcome; individual patient; large datasets; legally blind; macula; multidisciplinary; predictive modeling; preservation; research clinical testing; retinal nerve fiber layer; sex; skills; standard measure; three dimensional structure; tomography; tool

Project Abstract / Summary Primary open angle glaucoma (POAG) is a leading cause of blindness in the United States and worldwide. It is estimated that over 2.2 million Americans suffer from POAG and that over 130,000 are legally blind from the disease. As the population ages, the number of people with POAG in the United States will increase to over 3.3 million in 2020 and worldwide to an estimated 111.8 million by 2040. POAG is a progressive disease associated with characteristic functional and structural changes that clinicians use to diagnose and monitor the disease. Over the past several years, spectral domain optical coherent tomography (SDOCT) has become the standard tool for measuring structure in POAG. This 3D imaging modality provides a wealth of information about retinal structure and POAG-related retinal layers. This large amount of data is hard for clinicians to interpret and use effectively to help guide treatment decisions. Instead, summary metrics such as average layer thicknesses are used to reduce SDOCT images to a handful of values. While these metrics are useful, they can be difficult to interpret and they throwaway important information regarding voxel intensity and texture, relationships across retinal layers, and the overall 3D structure of the retina. Relying too heavily on these metrics limits our ability to gain a deeper understanding structural contributions to POAG, the relationship between structure and visual function, and how structural (and functional) changes progress in POAG. Recent advances in artificial intelligence and deep learning, however, offer new data-driven tools and techniques to interpret 3D SDOCT images and learn from the large SDOCT datasets being collected in clinics around the world. This proposal will apply state-of-the-art deep learning techniques to 3D SDOCT data in order to (1) develop more accurate POAG detection tools, (2) reveal structure-function relationships, and (3) predict structural and functional progression in POAG. This proposal also details a training plan to help the PI transition from a postdoctoral scholar to an independent researcher. The mentored phase of this award will be supervised by the primary mentor, Dr. Linda Zangwill, and a multidisciplinary mentoring team including Dr. Robert Weinreb (Ophthalmology), Dr. David Kriegman (Computer Science and Engineering), and Dr. Armin Schwartzman (Biostatistics). Performing the proposed research, formal coursework, and mentored career development will the provide the PI with highly sought- after skills and experience to help ensure a successful transition into independence.

项目摘要 /摘要 原发性开角青光眼（POAG）是美国和全球失明的主要原因。这是 据估计，超过220万美国人患有POAG，超过130,000人在法律上盲目 疾病。随着人口的年龄，美国POAG的人数将增加到3.3以上 2020年的百万美元，到2040年，全球估计达1.18亿。 临床医生用来诊断和监测该疾病的特征功能和结构变化。 在过去的几年中，光谱域光学相干断层扫描（SDOCT）已成为标准 测量POAG结构的工具。这种3D成像方式提供了有关视网膜的大量信息 结构和与POAG相关的视网膜层。临床医生很难解释和使用这些数据 有效地指导治疗决策。相反，摘要指标，例如平均层厚度 用于将SDOCT图像减少到少数值。尽管这些指标很有用，但它们可能很难 解释，他们抛出有关体素强度和质地的重要信息，跨越的关系 视网膜层和视网膜的总体3D结构。太严重地依靠这些指标限制了我们的能力 深入了解POAG的结构贡献，结构与视觉之间的关系 功能，以及结构性（和功能）如何改变POAG的进展。人工的最新进展 但是，情报和深度学习提供了新的数据驱动工具和技术来解释3D SDOCT 图像并从全球诊所收集的大型SDOCT数据集中学习。该提议将 将最先进的深度学习技术应用于3D SDOCT数据，以（1）开发更准确 POAG检测工具，（2）揭示结构功能关系，（3）预测结构和 POAG的功能进展。 该建议还详细介绍了一项培训计划，以帮助PI从博士后学者过渡到独立 研究员。该奖项的指导阶段将由主要导师Linda Zangwill博士和 一个多学科指导团队，包括Robert Weinreb博士（眼科），David Kriegman博士 （计算机科学与工程）和Armin Schwartzman博士（生物统计学）。执行提议 研究，正式课程和指导的职业发展将为PI提供备受追捧的 经过技能和经验，以帮助确保成功过渡到独立性。