A hybrid artificial intelligence framework for glaucoma monitoring

用于青光眼监测的混合人工智能框架

基本信息

批准号：
9892013
负责人：
Tobias Elze
金额：
$ 22.08万
依托单位：
UNIVERSITY OF TENNESSEE HEALTH SCI CTR
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-04-01 至 2022-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9892013
关键词：
Academy Address American Artificial Intelligence Axon Bayesian Modeling Big Data Big Data to Knowledge Blindness Caring Clinical Clinical Trials Communities Complex Computer Models Computer software Data Data Analyses Data Analytics Data Set Detection Devices Disease Disease Progression Evolution Eye Gaussian model Glaucoma Goals Hybrids Institutes Intelligence Joints Judgment Knowledge Machine Learning Measurement Measures Methods Modeling Modernization Monitor Neurodegenerative Disorders Ophthalmology Optic Nerve Optical Coherence Tomography Outcome Patients Pattern Population Prevention Public Health Registries Research Retina Severity of illness Source Structural Models Structure Surrogate Endpoint Testing Thick Thinness Time Training Vision Visual Fields analytical method archetypal analysis clinical Diagnosis data space design diagnostic accuracy early onset evidence base expectation field study health record high dimensionality improved intelligent algorithm machine learning algorithm multidimensional data novel open source optical imaging outcome forecast programs retinal ganglion cell degeneration retinal nerve fiber layer tool

项目摘要

Glaucoma is a complex neurodegenerative disease that results in degeneration of retinal ganglion cells and their axons. With older people making up the fastest growing part of the US population, glaucoma will become even more prevalent in the US in the coming decades. Due to the complex interaction of multiple factors in glaucoma, better structural and functional predictors are needed for its progression. The main impediments are massive health record data and sophisticated computational models. Our overall goal is to leverage the power of big data and rapidly evolving machine learning approaches. The NEI's “Big Data to Knowledge (BD2K)” initiative and the American Academy of Ophthalmology Intelligent Research in Sight (IRIS) registry are all efforts to exploit the power of data and to better understand diseases and to provide improved prevention and treatment. In this multi-PI proposal, we offer to assemble over 1 million optical coherence tomography (OCT) and visual fields (VFs) from the glaucoma research network (GRN). We propose to develop a hybrid artificial intelligence (AI) algorithm that synthesizes Gaussian mixture model expectation maximization (GEM) and archetypal machine learning approach to identify glaucoma progression and its monitoring using VFs and retinal nerve fiber layer (RNFL) thickness measurements. We will make these tools openly available to the vision and ophthalmology research communities. Our proposed studies could offer substantial improvements in the prognosis of glaucoma as well as potentially providing OCT and joint VF/OCT surrogate endpoints to be used in glaucoma clinical trials.

青光眼是一种复杂的神经退行性疾病，导致残留神经节细胞和他们的轴突。随着老年人组成美国人口中增长最快的部分，青光眼将成为在未来几十年中，在美国更普遍。由于多个因素的复杂相互作用需要青光眼，更好的结构和功能预测因子才能进行其进展。主要障碍是大量的健康记录数据和复杂的计算模型。我们的总体目标是利用大数据的力量和快速发展的机器学习方法。 NEI的“大数据知识（BD2K）”倡议和美国眼科智能研究（IRIS）注册表是否努力探索数据的力量并更好地了解疾病并提供改进的疾病预防和治疗。在此多PI提案中，我们提出要组装超过100万个光学相干断层扫描（OCT）和来自青光眼研究网络（GRN）的视野（VF）。我们建议开发一种混合艺术综合高斯混合模型期望最大化（GEM）和原型机器学习方法，以识别使用VFS的青光眼进展及其监测的方法视网膜神经纤维层（RNFL）厚度测量。我们将使这些工具公开为愿景和眼科研究社区。我们提出的研究可以在青光眼的预后以及有可能在青光眼临床试验中使用OCT和联合VF/OCT替代端点。