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) 是美国导致失明的主要原因 全世界。据估计，超过 220 万美国人患有 POAG，其中超过 13 万美国人患有 POAG 在法律上因该疾病而失明。随着人口老龄化，美国患有 POAG 的人数 到 2020 年，各国数量将增加到超过 330 万，预计到 2040 年全球将超过 1.1 亿。 POAG 是一种与特征性功能和结构变化相关的进行性疾病，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 的发生和进展有关。也结合这些来源的数据 因为有关患者人口统计、病史和临床测量的信息对于 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) 的最新进展提供了构建预测性多模态模型的工具 结合多种不同类型来进行预测。这项最新研究的中心假设 计划是将多模态、纵向深度学习应用于临床测量、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 更新后的计划以最初的研究提案为基础，纳入了新的方法和数据集。和 此次更新表明，通过帮助临床医生定制，改善护理和保留视力的潜力更大 对个别患者进行青光眼管理。 该提案还总结了该项目所取得的研究、培训和职业发展成就 K99相奖。与我的导师 Linda Zangwill 博士合作，我能够进行并发表 在我的 K99 阶段进行的有影响力的研究。我也能够完成培训和职业发展 原提案中列出的目标。在指导阶段，我帮助开发了基础设施 并安全地访问真实世界的临床数据集，这将使我能够立即取得进展 提出的研究。这个拟议的 R00 过渡将使我处于一个理想的位置来研究、出版、 导师，确保资金，并推进我作为独立调查员的职业生涯。