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

基本信息

批准号：
10219269
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
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-08-01 至 2023-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10219269
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

项目摘要

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/总结原发性开角型青光眼 (POAG) 是美国和全世界失明的主要原因。这是据估计，超过 220 万美国人患有 POAG，超过 130,000 人因此而失明。疾病。随着人口老龄化，美国患有POAG的人数将增加到3.3以上到 2020 年，这一数字预计将达到 1.118 亿，到 2040 年，全球预计将达到 1.118 亿。POAG 是一种进行性疾病，与临床医生用来诊断和监测疾病的特征性功能和结构变化。在过去的几年里，谱域光学相干断层扫描 (SDOCT) 已成为标准 POAG 中测量结构的工具。这种 3D 成像方式提供了有关视网膜的丰富信息结构和 POAG 相关的视网膜层。如此大量的数据对于临床医生来说很难解释和使用有效地帮助指导治疗决策。相反，诸如平均层厚度之类的汇总指标是用于将 SDOCT 图像减少到少数值。虽然这些指标很有用，但它们可能很难解释并丢弃有关体素强度和纹理、之间关系的重要信息视网膜各层以及视网膜的整体 3D 结构。过度依赖这些指标限制了我们的能力更深入地了解 POAG 的结构贡献、结构与视觉之间的关系功能，以及结构（和功能）如何改变 POAG 的进展。人工技术的最新进展然而，智能和深度学习提供了新的数据驱动工具和技术来解释 3D SDOCT 图像并从世界各地诊所收集的大型 SDOCT 数据集中学习。该提案将将最先进的深度学习技术应用于 3D SDOCT 数据，以便 (1) 开发更准确的 POAG 检测工具，(2) 揭示结构-功能关系，(3) 预测结构和功能 POAG 的功能进展。该提案还详细介绍了帮助PI从博士后学者过渡到独立学者的培训计划。研究员。该奖项的指导阶段将由主要导师 Linda Zangwill 博士和多学科指导团队，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）。执行建议的研究、正式课程作业和指导的职业发展将为 PI 提供备受追捧的- 拥有技能和经验，以帮助确保成功过渡到独立。

项目成果

期刊论文数量（6）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Effects of Study Population, Labeling and Training on Glaucoma Detection Using Deep Learning Algorithms.

使用深度学习算法研究人群、标签和训练对青光眼检测的影响。

DOI：
发表时间：
2020-04
期刊：
影响因子：
3
作者：
Christopher, Mark;Nakahara, Kenichi;Bowd, Christopher;Proudfoot, James A;Belghith, Akram;Goldbaum, Michael H;Rezapour, Jasmin;Weinreb, Robert N;Fazio, Massimo A;Girkin, Christopher A;Liebmann, Jeffrey M;De Moraes, Gustavo;Murata, Hiroshi;Toku
通讯作者：
Toku

Usability and Clinician Acceptance of a Deep Learning-Based Clinical Decision Support Tool for Predicting Glaucomatous Visual Field Progression.

用于预测青光眼视野进展的基于深度学习的临床决策支持工具的可用性和临床医生的接受度。

DOI：
发表时间：
2023-03-01
期刊：
影响因子：
2
作者：
Chen, Jimmy S;Baxter, Sally L;van den Brandt, Astrid;Lieu, Alexander;Camp, Andrew S;Do, Jiun L;Welsbie, Derek S;Moghimi, Sasan;Christopher, Mark;Weinreb, Robert N;Zangwill, Linda M
通讯作者：
Zangwill, Linda M

Multimodal Deep Learning Classifier for Primary Open Angle Glaucoma Diagnosis Using Wide-Field Optic Nerve Head Cube Scans in Eyes With and Without High Myopia.

使用宽视场视神经头立方体扫描对患有和不患有高度近视的眼睛进行原发性开角型青光眼诊断的多模态深度学习分类器。

DOI：
发表时间：
2023-10-01
期刊：
影响因子：
2
作者：
Bowd, Christopher;Belghith, Akram;Rezapour, Jasmin;Christopher, Mark;Jonas, Jost B;Hyman, Leslie;Fazio, Massimo A;Weinreb, Robert N;Zangwill, Linda M
通讯作者：
Zangwill, Linda M

Deep Learning Estimation of 10-2 and 24-2 Visual Field Metrics Based on Thickness Maps from Macula OCT.

基于黄斑 OCT 厚度图的 10-2 和 24-2 视野指标的深度学习估计。

DOI：
发表时间：
2021-11
期刊：
影响因子：
13.7
作者：
Christopher, Mark;Bowd, Christopher;Proudfoot, James A;Belghith, Akram;Goldbaum, Michael H;Rezapour, Jasmin;Fazio, Massimo A;Girkin, Christopher A;De Moraes, Gustavo;Liebmann, Jeffrey M;Weinreb, Robert N;Zangwill, Linda M
通讯作者：
Zangwill, Linda M

Diagnostic Accuracy of Macular Thickness Map and Texture En Face Images for Detecting Glaucoma in Eyes With Axial High Myopia.

黄斑厚度图和纹理 En 面部图像用于检测轴性高度近视眼青光眼的诊断准确性。

DOI：
发表时间：
2022-10
期刊：
影响因子：
4.2
作者：
Bowd, Christopher;Belghith, Akram;Rezapour, Jasmin;Christopher, Mark;Hyman, Leslie;Jonas, Jost B;Weinreb, Robert N;Zangwill, Linda M
通讯作者：
Zangwill, Linda M