A Comprehensive Strategy to Detect Glaucoma Worsening Earlier and With Fewer Tests

通过更少的测试及早发现青光眼恶化的综合策略

• 批准号: 10589048
• 负责人: Jithin Yohannan
• 金额: $ 19.12万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-03-01 至 2026-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10589048
• 关键词: Accounting; Affect; Area; Bayesian Analysis; Blindness; Calendar; Clinical; Clinical Data; Coupled; Data; Data Science; Data Set; Detection; Diagnostic tests; Disease; Educational workshop; Effectiveness; Ethics; Eye; Future; Glaucoma; Goals; Healthcare; Healthcare Systems; Image; Knowledge; Learning; Machine Learning; Measures; Mentored Patient-Oriented Research Career Development Award; Mentors; Methods; Modeling; Monitor; Optical Coherence Tomography; Outcome; Papillary; Patients; Pattern; Performance; Physiologic Intraocular Pressure; Population; Public Health; Pythons; Research; Research Activity; Research Personnel; Research Proposals; Resource Allocation; Resources; Risk; Signal Transduction; Stream; Structure; Techniques; Testing; Thick; Time; Training; Visit; Visual Acuity; Visual Fields; Work; clinical decision-making; compare effectiveness; convolutional neural network; deep learning; deep learning model; demographics; field study; generative adversarial network; high risk; improved; longitudinal database; machine learning method; machine learning model; meetings; model design; multilevel analysis; neural network architecture; predictive modeling; preservation; prevent; programs; responsible research conduct; retinal nerve fiber layer; risk prediction; skills; tool; visual optics; wasting

ABSTRACT This is an application for a K23 Mentored Patient-Oriented Research Career Development Award. The goal of this proposal is to provide the candidate with the advanced skills needed to establish an independent research program in the area of glaucoma diagnostic testing with special expertise in test error correction and predictive modeling of future glaucoma outcomes. To facilitate this long-term goal, in the current proposal, the candidate’s main research goal is to reduce the time and number of tests necessary to detect glaucoma worsening by (1) correcting for errors in previously obtained visual field (VF) and peripapillary optical coherence tomography (OCT) tests by using multilevel models with Bayesian analysis (MLB) and generative adversarial networks (GAN) (2) stratifying eyes at high and low risk for rapid glaucoma worsening at the baseline clinical visit using deep convolutional neural networks (DCNN). These aims are based on high quality preliminary data which show that: (1) the effect of VF reliability metrics and OCT signal strength on test error can be quantified and thus corrected for and (2) machine learning methods can predict risk of future VF progression with fair accuracy with baseline visit VF data alone and therefore adding structural (OCT) and clinical information from the baseline visit is likely to improve model accuracy. The main hypotheses of the proposed research aims are (1) correcting for test errors with MLB and GAN will reduce the time needed to detect worsening by 10 and 20% respectively (2) combining baseline visit structural (OCT), functional (VF) and clinical data as inputs into DCNNs will allow us to achieve an area under the receiver operating curve of at least 0.8 at predicting the risk of future rapid glaucoma worsening. The candidate proposes a comprehensive training plan, combining formal coursework, meetings, seminars and workshops overseen by his diverse group of mentors. Specific training goals include: (1) Receiving training in multi-level regression modeling and Bayesian analysis techniques. (2) Becoming adept at data science with a special emphasis on learning Python for data extraction, manipulation and analysis. (3) Furthering knowledge of machine learning techniques with a specific emphasis on deep learning including DCNNs and GANs. (4) Continuing training in the ethical and responsible conduct of research. The training plan will be executed in coordination with the set of research activities mentioned above. Results from this research proposal will be used to develop a subsequent R01 research proposal that will facilitate the candidate’s transition to an independent researcher.

摘要 这是一个K23指导以患者为导向的研究职业发展奖的应用程序。的 本提案的目标是为候选人提供建立独立的 青光眼诊断测试领域的研究项目，具有测试错误纠正和 未来青光眼结果的预测模型。为了促进这一长期目标，在目前的提案中， 候选人的主要研究目标是减少青光眼检测所需的时间和数量 通过（1）校正先前获得的视野（VF）和视乳头周围光学 相干断层扫描（OCT）测试，使用贝叶斯分析（MLB）和生成的多层次模型 对抗性网络（GAN）（2）对快速青光眼恶化的高风险和低风险眼睛进行分层， 使用深度卷积神经网络（DCNN）进行基线临床访问。这些目标是基于高质量 初步数据表明：（1）VF可靠性指标和OCT信号强度对测试误差的影响 可以量化，从而纠正（2）机器学习方法可以预测未来VF的风险 仅基线访视VF数据的进展具有相当的准确性，因此增加了结构性（OCT）和 来自基线访视的临床信息可能会提高模型的准确性。的主要假设 建议的研究目标是：（1）用MLB和GAN校正测试错误将减少所需的时间， 分别检测到恶化10%和20%（2）结合基线访视结构（OCT）、功能（VF）和 作为DCNN输入的临床数据将使我们能够在接受者工作曲线下面积达到 在预测未来青光眼快速恶化的风险时至少为0.8。候选人提出了一个全面的 培训计划，结合正式的课程，会议，研讨会和讲习班，由他的多元化小组监督 的导师。具体培训目标包括：（1）接受多水平回归建模培训， 贝叶斯分析技术。(2)熟练掌握数据科学，特别强调学习 Python用于数据提取、操作和分析。(3)进一步了解机器学习 特别强调深度学习的技术，包括DCNN和GAN。(4)继续培训 道德和负责任的研究行为。培训计划将与成套设备协调执行。 上述研究活动。这项研究计划的结果将用于制定一项 随后的R01研究提案，这将有助于候选人的过渡到一个独立的研究人员。