Quantifying the Functional Relationship Between Visual Acuity and Contrast Sensitivity Function.


Journal

Investigative ophthalmology & visual science
ISSN: 1552-5783
Titre abrégé: Invest Ophthalmol Vis Sci
Pays: United States
ID NLM: 7703701

Informations de publication

Date de publication:
01 Oct 2024
Historique:
medline: 22 10 2024
pubmed: 22 10 2024
entrez: 22 10 2024
Statut: ppublish

Résumé

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. 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. 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. 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.

Identifiants

pubmed: 39436371
pii: 2802157
doi: 10.1167/iovs.65.12.33
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

33

Auteurs

Zhong-Lin Lu (ZL)

Division of Arts and Sciences, NYU Shanghai, Shanghai, China.
Center for Neural Science, New York University, New York, New York, United States.
Department of Psychology, New York University, New York, New York, United States.
NYU-ECNU Institute of Brain and Cognitive Neuroscience, Shanghai, China.

Yukai Zhao (Y)

Center for Neural Science, New York University, New York, New York, United States.

Luis Andres Lesmes (LA)

Adaptive Sensory Technology Inc., San Diego, California, United States.

Michael Dorr (M)

Adaptive Sensory Technology Inc., San Diego, California, United States.

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Classifications MeSH