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: 10580023
• 负责人: DENNIS L BARBOUR
• 金额: $ 19.5万
• 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 2025-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10580023
• 关键词: Active Learning; Address; Algorithms; Behavioral; Belief; Brain; Clinical; Computer software; Contrast Sensitivity; Data; Data Collection; Data Set; Development; Diagnosis; Diagnostic; Dimensions; Disparate; 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; Training; Vision Disorders; Visual; Visual Acuity; Visual Fields; Visual Psychophysics; Visual System; Work; behavioral response; computerized data processing; data acquisition; design; experience; flexibility; improved; individual response; machine learning algorithm; machine learning framework; patient population; portability; 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.

抽象的 视觉对比度灵敏度代表了可视系统的核心处理能力，可用于诊断A 各种视觉障碍。量化此功能的最简单，最简单，最便宜，最便携的方法是 直接查询 - 保留适当的视觉刺激和记录行为响应。和所有人一样 但是，心理物理测试估计对比敏感性功能（CSF）需要序列数据获取， 导致不切实际的收购时间。因此，完整的CSF可以具有显着的临床价值，但 由于实际原因，通常使用缺乏定量精度的快速心理物理筛查。 该建议的目的是将机器学习算法和高质量回顾性CSF结合起来 设计可调诊断估计器的数据，可以快速（用于筛选）或彻底（用于 诊断），根据需要。我们的方法是培训多维贝叶斯主动机器学习 估计器先前已验证了视野周期性和听力测试的估算器 - 共享的检验 许多具有对比度灵敏度测试的属性。在AIM 1中，我们将实施并验证机器学习 CSF估计器（MLCSF）。这种类型的估计器接受灵活的假设，并允许优化 数据收集以最大化信息增益。在AIM 2中，我们将提高人口CSF的MLCSF效率 数据。 MLCSF的贝叶斯性质允许以前的经验发现，可以先进 相信新测试对象。将使用以前CSF测试程序得出的人口摘要 为MLCSF估计器建立信息丰富的先验信念。在AIM 3中，我们将扩展MLCSF模型以包括 相关的个人测量。其他视觉测试导致与个人CSF相关的测量。 这些额外的预测因子之间的关系在先前收集的视觉处理数据中 个人将用于完善MLCSF估计量的先前信念。完成后，这项研究将有 产生了一个尖端的主动机器学习框架，以估计概率对比度灵敏度 使用相对较少的测量功能。该估计器的灵活性将允许专家和 临床医生结合理论假设和经验的先验信念，以解决各种临床 从筛查到具有相同过程的诊断的问题。