# Using Population Contrast Sensitivity Function Data to Develop Tunable Test Procedures

> **NIH NIH R21** · WASHINGTON UNIVERSITY · 2022 · $261,156

## Abstract

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.

## Key facts

- **NIH application ID:** 10375287
- **Project number:** 1R21EY033553-01
- **Recipient organization:** WASHINGTON UNIVERSITY
- **Principal Investigator:** DENNIS L BARBOUR
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $261,156
- **Award type:** 1
- **Project period:** 2022-03-01 → 2024-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10375287

## Citation

> US National Institutes of Health, RePORTER application 10375287, Using Population Contrast Sensitivity Function Data to Develop Tunable Test Procedures (1R21EY033553-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10375287. Licensed CC0.

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