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Multicenter Study
. 2024 Sep 3;13(9):20.
doi: 10.1167/tvst.13.9.20.

Estimating Visual Acuity Without a Visual Acuity Chart

Yueh-Hsun Wu  1   2 , Deyue Yu  1 , Judith E Goldstein  3 , MiYoung Kwon  4 , Micaela Gobeille  5 , Emily Watson  1 , Luc Waked  1 , Rachel Gage  2 , Chun Wang  6 , Gordon E Legge  2
Affiliations
Multicenter Study

Estimating Visual Acuity Without a Visual Acuity Chart

Yueh-Hsun Wu et al. Transl Vis Sci Technol. .

Abstract

Purpose: This study explored whether visual acuity (VA) can be inferred from self-reported ability to recognize everyday objects using a set of yes/no questions.

Methods: Participants answered 100 yes/no questions designed to assess their ability to recognize familiar objects at typical viewing distances, such as distinguishing between a full moon and a half moon on a clear night. The questions demanded VA ranging from normal to severe vision impairment. Responses were analyzed using item response theory, and the results were compared with participants' VA values.

Results: We recruited 385 participants from 4 sites in the United States. Participants had a mean age of 56.7 years with VA ranging from -0.3 to 2.0 logarithm of the minimum angle of resolution (logMAR) (mean = 0.58). A strong relationship was observed between participants' estimated vision ability and their VA (r = -0.72). The linear relationship can be used to predict each participant's VA based on their estimated vision ability. The average signed and unsigned prediction errors were 0 and 0.24 logMAR, respectively, with a coefficient of repeatability of 0.59 logMAR between the estimated VA and measured VA. The same linear function was used to determine the VA limit required for each question. For instance, the VA limit for the moon question was 1.0 logMAR.

Conclusions: Yes/no questions about everyday visual activities have the potential to estimate an individual's VA. Future refinements may enhance reliability.

Translational relevance: The survey provides insights into the real-world visual capabilities of people with low vision, making it potentially useful for telehealth applications.

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Conflict of interest statement

Disclosure: Y.-H. Wu, None; D. Yu, None; J.E. Goldstein, None; M. Kwom, None; M. Gobeille, None; E. Watson, None; L. Waked, None; R. Gage, None; C. Wang, None; G.E. Legge, None

Figures

Figure 1.
Figure 1.
Examples of different item characteristic curves (ICCs). (A) Three ICCs represent different item difficulty levels when sharing the same item discriminability. (B) Three ICCs show different levels of item discriminability when sharing the same level of item difficulty.
Figure 2.
Figure 2.
(A) Distributions of estimated thetas and item difficulty. (B) The scatterplot shows the relationship between the estimated item difficulty and item discriminability.
Figure 3.
Figure 3.
The relationship between vision abilities and measured VA (logMAR).
Figure. 4
Figure. 4
The relationship between estimated VA and measured VA. Estimated VA values were transformed from vision ability estimates.
Figure 5.
Figure 5.
(A) The plot represents the agreement between the estimated and measured VA. The signed prediction errors are calculated as the differences between the estimated VA and the measured VA. The solid black line represents the mean of sign prediction errors. The two black dashed lines indicate the range within ± 1.96 SD of the signed prediction errors. The two red dash-dot lines indicate the range of average unsigned prediction errors. (B) The scatterplot shows the relationship between measured VA and unsigned prediction errors.
See this image and copyright information in PMC

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