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

• 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
• 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

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.

摘要 视觉对比敏感度代表了视觉系统的核心处理能力，可用于诊断视觉系统的异常。 各种视觉障碍。量化这种能力的最简单、最容易、最便宜和最便携的方法是 直接询问-传递适当的视觉刺激并记录行为反应。如同所有 然而，心理物理测试，估计对比敏感度函数（CSF）需要连续的数据采集， 导致不切实际的长采集时间。虽然完整的CSF因此可以具有显著的临床价值， 出于实际原因，经常使用缺乏定量精确度的快速心理物理筛查。 该提案的目标是将联合收割机机器学习算法和高质量的回顾性CSF相结合 数据来设计可调的诊断估计器，可以是快速的（用于筛选）或彻底的（用于 诊断）。我们的方法将是训练一个多维贝叶斯主动机器学习 先前已验证用于视野视野检查和听力测试的估计器-共享 许多属性与对比敏感度测试。在aim 1中，我们将实现并验证一个机器学习 CSF估计量（mlCSF）。这种类型的估计器适应灵活的假设，并允许优化 数据收集，以最大限度地提高信息增益。在目标2中，我们将用群体CSF提高mlCSF效率 数据mlCSF的贝叶斯性质允许来自群体的先前经验发现来细化先前的经验发现。 新测试对象的信念。将使用来自既往CSF检测程序的人群总结 为mlCSF估计量建立信息丰富的先验信念。在目标3中，我们将扩展mlCSF模型， 相关的个别措施。其他视觉测试结果与个人的CSF相关的测量。 这些额外的预测因素之间的关系，在以前收集的视觉处理数据，从相同的 个体将被用于细化mlCSF估计量的先验信念。完成后，本研究将 产生了一个先进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功能。这个估计器的灵活性将允许实验者和 临床医生将联合收割机理论假设和经验先验信念结合起来， 从筛查到诊断的问题都是用同样的程序。