ArticleInvestigative ophthalmology & visual science2024
Quantifying the Functional Relationship Between Visual Acuity and Contrast Sensitivity Function.
Article in Investigative ophthalmology & visual science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Contrast Sensitivity Impairment in Diabetic Retinopathy: Clinical and Structural Correlates.Diagnostics (Basel, Switzerland) · 2026Review
- Analyzing display photometric features through time-dependent dynamics of accommodative responses and ocular imaging quality.Scientific reports · 2025Article
- Exploring the impact of visual function degradation on manual prehension movements in normal-sighted individuals.PloS one · 2025Article
- Quantifying the Functional Relationship Between Visual Acuity and Contrast Sensitivity Function.Investigative ophthalmology & visual science · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Purpose: Studies have reported that individuals with certain ocular disorders may have significant decreases in contrast sensitivity function (CSF) despite having normal or near normal visual acuity (VA). This study seeks to elucidate this phenomenon by investigating the relationship between VA and CSF. Methods: We analyzed data from 14 eyes tested with Electronic Early Treatment Diabetic Retinopathy and quantitative CSF under four Bangerter foil conditions (n = 56). From the CSF data, we estimated peak gain, peak frequency, and contrast sensitivity acuity (CSA). We explored the correlations between VA and various CSF parameters and evaluated five predictive models of VA using CSA alone and in combination with additional CSF parameters through ridge regression. Results: We found that similar VA scores can correspond with markedly different CSFs and observed significant correlations among all CSF parameters and between VA and each CSF parameter (all P < 0.001). The most effective predictive model, incorporating CSA and peak gain, explained 90.97% of the variance with a root mean squared error of 0.0676 logMAR, which is comparable with the average standard deviation of the VA scores (0.0627 logMAR) and accounted for 38.6% of the residual variance not explained by the CSA-alone model. Conclusions: This study offers the first empirical inference of the quantitative relationship between VA and CSF, suggesting that various CSF parameter combinations can yield identical VA. This might help to explain why some clinical populations with normal or near-normal VA exhibit significant CSF deficits and calls for further research in different clinical settings.
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Registered trials
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