Using Population Contrast Sensitivity Function Data to Develop Tunable Test Procedures

使用群体对比敏感度函数数据开发可调测试程序

基本信息

批准号：
10375287
负责人：
DENNIS L BARBOUR
金额：
$ 26.12万
依托单位：
WASHINGTON UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-03-01 至 2024-02-28
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10375287
关键词：
Active Learning Address Algorithms Behavioral Belief Brain Clinical Computer software Contrast Sensitivity Data Data Collection Data Set Development Diagnosis Diagnostic Eye Frequencies Funding Hearing Tests Human Individual Letters Machine Learning Measurement Measures Methods Modeling Nature Participant Performance Perimetry Play Population Positioning Attribute Procedures Property Psychophysics Research Role Sampling Statistical Models Structure System Tablets Techniques Test Result Testing Time Training Vision Disorders Visual Visual Acuity Visual Fields Visual Psychophysics Visual system structure Work behavioral response computerized data processing data acquisition design experience flexibility improved individual response machine learning algorithm machine learning framework patient population portability predictive test screening success visual dysfunction visual processing visual stimulus visual threshold

项目摘要

ABSTRACT Visual contrast sensitivity represents a core processing ability of the visual system useful for diagnosing a variety of visual disorders. The simplest, easiest, cheapest and most portable way to quantify this ability is by querying directly—delivering appropriate visual stimuli and recording behavioral responses. As with all psychophysical tests, however, estimating contrast sensitivity functions (CSFs) requires serial data acquisition, leading to impractically long acquisition times. While full CSFs can therefore have significant clinical value, quick psychophysical screenings that lack quantitative precision are often used instead for practical reasons. The objective of this proposal is to combine machine learning algorithms and high-quality retrospective CSF data to design tunable diagnostic estimators that can be either quick (for screening) or thorough (for diagnostics), as desired. Our approach will be to train a multidimensional Bayesian active machine learning estimator that has been validated previously for visual field perimetry and audiometric testing—tests that share many properties with contrast sensitivity testing. In aim 1 we will implement and validate a machine learning CSF estimator (mlCSF). This type of estimator accommodates flexible assumptions and allows optimization of data collection for maximizing information gain. In aim 2 we will improve mlCSF efficiency with population CSF data. The Bayesian nature of mlCSF allows for previous empirical findings from a population to refine prior beliefs for new test subjects. Population summaries derived from previous CSF testing procedures will be used to establish informative prior beliefs for the mlCSF estimator. In aim 3 we will extend mlCSF models to include related individual measures. Other visual tests result in measurements that correlate with an individual’s CSF. Relationships among these extra predictors in previously collected visual processing data from the same individuals will be used to refine the prior beliefs of the mlCSF estimator. When complete, this study will have produced a cutting-edge active machine learning framework to estimate probabilistic contrast sensitivity functions using relatively few measurements. The flexibility of this estimator will allow experimenters and clinicians to combine theoretical assumptions and empirical prior beliefs to address a variety of clinical questions ranging from screening to diagnosis with the same procedure.

抽象的视觉对比敏感度代表视觉系统的核心处理能力，可用于诊断量化这种能力的最简单、最容易、最便宜和最便携的方法是通过直接询问——提供适当的视觉刺激并记录行为反应。然而，心理物理测试估计对比敏感度函数 (CSF) 需要串行数据采集，因此，虽然完整的 CSF 具有显着的临床价值，出于实际原因，经常使用缺乏定量精度的快速心理物理学筛查。该提案的目标是将机器学习算法和高质量的回顾性 CSF 结合起来数据来设计可调诊断估计器，该估计器可以快速（用于筛选）或彻底（用于诊断），根据需要，我们的方法将是训练多维贝叶斯主动机器学习。先前已针对视野视野测量和听力测试进行验证的估计器——共享的测试在目标 1 中，我们将实现并验证机器学习。 CSF 估计器 (mlCSF) 这种类型的估计器适应灵活的假设并允许优化收集数据以最大化信息增益在目标 2 中，我们将通过群体 CSF 提高 mlCSF 效率。 mlCSF 的贝叶斯性质允许利用先前从人群中获得的经验结果来完善先前的数据。将使用从以前的 CSF 测试程序得出的群体摘要。为 mlCSF 估计器建立信息丰富的先验信念在目标 3 中，我们将扩展 mlCSF 模型以包括。其他视觉测试的结果与个人的脑脊液相关。先前从相同的视觉处理数据中收集的这些额外预测变量之间的关系完成后，本研究将使用个体来完善 mlCSF 估计器的先验信念。制作了一个尖端的主动机器学习框架来估计概率对比敏感度使用相对较少的测量的函数，该估计器的灵活性将允许实验者和勇士们将理论假设和经验先验信念结合起来，解决各种临床问题使用相同的程序来解决从筛查到诊断的各种问题。