June 2022
Volume 63, Issue 7
Open Access
ARVO Annual Meeting Abstract  |   June 2022
Estimating visual acuity without a visual acuity chart
Author Affiliations & Notes
  • Yueh-Hsun Wu
    Department of Psychology, University of Minnesota Twin Cities, Minneapolis, Minnesota, United States
  • Deyue Yu
    College of Optometry, The Ohio State University, Columbus, Ohio, United States
  • Judith E Goldstein
    Johns Hopkins Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States
  • MiYoung Kwon
    Department of Psychology, Northeastern University, Boston, Massachusetts, United States
  • Emily Catherine Watson
    College of Optometry, The Ohio State University, Columbus, Ohio, United States
  • Luc Waked
    College of Optometry, The Ohio State University, Columbus, Ohio, United States
  • Micaela R Gobeille
    Johns Hopkins Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States
  • Dawn K DeCarlo
    Department of Ophthalmology and Visual Sciences, The University of Alabama at Birmingham School of Medicine, Birmingham, Alabama, United States
  • Rachel Gage
    Department of Psychology, University of Minnesota Twin Cities, Minneapolis, Minnesota, United States
  • Chun Wang
    Measurement and Statistics, University of Washington College of Education, Seattle, Washington, United States
  • Gordon E Legge
    Department of Psychology, University of Minnesota Twin Cities, Minneapolis, Minnesota, United States
  • Footnotes
    Commercial Relationships   Yueh-Hsun Wu None; Deyue Yu None; Judith Goldstein None; MiYoung Kwon None; Emily Watson None; Luc Waked None; Micaela Gobeille None; Dawn DeCarlo None; Rachel Gage None; Chun Wang None; Gordon Legge None
  • Footnotes
    Support  NIH grant EY025658
Investigative Ophthalmology & Visual Science June 2022, Vol.63, 4265. doi:
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      Yueh-Hsun Wu, Deyue Yu, Judith E Goldstein, MiYoung Kwon, Emily Catherine Watson, Luc Waked, Micaela R Gobeille, Dawn K DeCarlo, Rachel Gage, Chun Wang, Gordon E Legge; Estimating visual acuity without a visual acuity chart. Invest. Ophthalmol. Vis. Sci. 2022;63(7):4265.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : Visual acuity (VA) measurement obtained from a letter acuity chart is assumed to predict the ability to resolve the visual details of objects in real life. Conversely, a person's ability to see object details in daily life could be used to estimate the person's visual acuity. The goal of our project is to determine if a person's Yes/No answers to a series of questions about their vision in daily life can be used to estimate their visual acuity.

Methods : 333 participants from four different testing sites responded to a set of 100 yes/no questions designed to assess people’s acuity for recognizing familiar objects at typical viewing distances. The questions were selected to evaluate acuities ranging from normal to ultra-low vision. A sample question is: "Are you able to count the individual tines on a fork that is sitting on the table in front of you?". Measured VA values were available from all participants and converted to logMAR units. The yes/no responses to the 100 questions were analyzed by a two-parameter (2PL) model based on Item Response Theory. The 2PL model estimated each participant's visual ability (θ) to answer the set of questions and the difficulty and discriminability of each question.

Results : The mean age and VA of the sample were 56.66 years (18 to 93, SD = 20.72) and 0.58 logMAR (-0.3 to 2.0 logMAR, SD = 0.45). The three largest diagnostic categories were glaucoma (17%), macular degeneration (13%), and cataract (9%). The percentage of yes responses answered by each participant was significantly correlated with their VAs (r = -0.71, p < .001). A strong relationship was also found between each participant's visual ability estimate (θ) from the 2PL model and their VA (r = -0.72, p < .001). Each participant's VA was predicted by their estimated theta based on the linear relationship between the two variables. The average prediction error, calculated by the absolute difference between the predicted VA and actual VA, was 0.23 logMAR (SD = 0.18). The same linear function was used to infer the acuity limit required for each question. For example, the VA limit of the question above about forks was 0.83 logMAR.

Conclusions : Our results show that a questionnaire can be used for estimating visual acuity worse than 20/40. The item responses provide insights into the real-world visual capabilities of people across a range of acuities and may be a potentially useful tool in telehealth.

This abstract was presented at the 2022 ARVO Annual Meeting, held in Denver, CO, May 1-4, 2022, and virtually.

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