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

在美国，原发性开角型青光眼(POAG)是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，超过13万人 从法律上讲，他因这种疾病而失明。随着人口老龄化，美国患有POAG的人数 到2020年，美国的数量增加到330多万个，预计到2040年，全球将达到1.1亿英镑。POAG 是一种进行性疾病，与临床医生特有的功能和结构变化有关 用于诊断和监控疾病。光学相干层析成像(OCT)和视野(VF) 测试是测量结构(OCT)和功能(VF)变化的临床标准 与开角型青光眼的发展和进展有关。也将这些来源的数据组合在一起 由于有关患者人口统计、病史和临床测量的信息对IS至关重要 对于及早发现青光眼、识别进展迹象和选择适当的治疗方法至关重要。 人工智能和深度学习领域的最新进展为建立预测性多模式模型提供了工具 它结合了多种不同的类型来进行预测。这项最新研究的一个中心假设 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 更新的计划通过纳入新的方法和数据集，建立在原始研究提案的基础上。使用 这次更新，通过帮助临床医生量身定做，甚至有更大的潜力来改善护理和保护视力 对个别患者的青光眼管理。 该建议还总结了在研究、培训和职业发展方面取得的成就 K99阶段的奖项。与我的导师琳达·赞威尔博士合作，我能够指挥和出版 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 原建议中所列的目标。在指导阶段，我帮助开发了基础设施 以及安全地访问真实世界的临床数据集，这将使我能够立即在 拟开展的研究。建议的R00过渡将使我处于一个理想的位置来研究、出版 指导，获得资金，并推动我作为一名独立调查员的职业生涯。

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.