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: 10799087
• 负责人: Mark Christopher
• 金额: $ 24.9万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-01 至 2026-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10799087
• 关键词: Deep; Learning; Approaches; Detect; Glaucoma

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 increased to over 3.3 million in 2020 and is expected to be >110 million worldwide by 2040. POAG is a progressive disease associated with characteristic functional and structural changes that clinicians use to diagnose and monitor the disease. Optical coherence tomography (OCT) and visual field (VF) testing are the clinical standard for measuring the structural (OCT) and functional (VF) changes associated with the development and progression of POAG. Combining data from these sources as well as information about patient demographics, medical history, and clinical measurements is critical to is critical in detecting glaucoma early, identifying signs of progression, and selecting appropriate treatment. Recent progress in AI and deep learning (DL) have provided tools to build predictive multimodal, models that incorporate multiple different types to make predictions. A central hypothesis of this updated research plan is that applying multimodal, longitudinal DL to clinical measurements, VF testing, and OCT imaging will improve the accuracy of predicting progressive structural and functional changes in glaucoma. This updated plan builds on the original research proposal by incorporating new methods and datasets. With this update, there is even greater potential to improve care and preserve vision by helping clinicians tailor glaucoma management to individual patients. This proposal also summarizes the research, training, and career development achievements made the K99 phase of the award. Working with my mentor, Dr. Linda Zangwill, I was able to conduct and publish impactful research during my K99 phase. I was also able to complete the training and career development objectives laid out in the original proposal. During the mentored phase, I helped developed infrastructure and secure access to real-world clinical datasets that will allow me to make immediate progress on the proposed research. This proposed R00 transition will put me in an ideal position to research, publish, mentor, secure funding, and advance my career as an independent investigator.

原发性开角型青光眼（POAG）是美国失明的主要原因， 国际吧据估计，超过220万美国人患有POAG，超过13万人患有POAG。 因为疾病而失明随着人口老龄化，美国POAG患者的数量 到2020年，世界上的国家增加到330多万，预计到2040年将超过1.1亿。POAG 是一种进行性疾病，与临床医生认为 用于诊断和监测疾病。光学相干断层扫描（OCT）和视野（VF） 测试是测量结构（OCT）和功能（VF）变化的临床标准 与POAG的发展和进展有关。结合这些来源的数据，以及 因为关于患者人口统计学、病史和临床测量的信息对于 这对于早期发现青光眼、识别进展迹象和选择适当的治疗至关重要。 人工智能和深度学习（DL）的最新进展为构建预测性多模态模型提供了工具 结合了多种不同的类型来进行预测。这项最新研究的一个核心假设是， 计划是将多模式纵向DL应用于临床测量、VF测试和OCT成像 将提高预测青光眼进行性结构和功能变化的准确性。这 更新后的计划以原来的研究建议为基础，采用了新的方法和数据集。与 这一更新，有更大的潜力，以改善护理和保护视力，帮助临床医生定制 青光眼的治疗方法 本建议还总结了研究，培训和职业发展所取得的成就， K99的奖励。与我的导师琳达赞格威尔博士合作，我能够进行并发表 在我的K99阶段进行了有影响力的研究。我也能够完成培训和职业发展 原提案中提出的目标。在指导阶段，我帮助开发了基础设施 并安全访问真实世界的临床数据集，这将使我能够立即取得进展， 提议的研究。这个建议的R00过渡将使我处于一个理想的位置，研究，出版， 指导，获得资金，并推进我作为独立调查员的职业生涯。