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Diagnostic accuracy of an iPad application for detection of visual field defects

Eye volume 37, pages 1690–1695 (2023)Cite this article

Subjects

Abstract

Background/objectives

Tablet-based perimetry could be used to test for glaucomatous visual field defects in settings without easy access to perimeters, although few studies have assessed diagnostic accuracy of tablet-based tests. The goal of this study was to determine the diagnostic accuracy of iPad perimetry using the visualFields Easy application.

Subjects/methods

This was a prospective, cross-sectional study of patients undergoing their first Humphrey Field Analyser (HFA) visual field test at a glaucoma clinic in India. Participants underwent 24-2 SITA Standard HFA testing and iPad-based perimetry with the visualFields Easy application. Reference standards for both visual field loss and suspected glaucoma were determined by ophthalmologist review of HFA results and optic disc photographs. Receiver operating characteristic curves were constructed to assess diagnostic accuracy at various test thresholds.

Results

203 eyes from 115 participants were included, with 82 eyes classified as moderate or worse glaucoma. iPad perimetry had an area under the receiver operating characteristic (AUROC) curve of 0.64 (95% CI 0.57 to 0.71) for detection of any visual field defect relative to HFA and an AUROC of 0.68 (0.59 to 0.76) for detection of moderate or worse glaucoma relative to ophthalmologist examination. At a set specificity of 90%, the sensitivity of iPad perimetry for detection of moderate or worse glaucoma was 35% (22–48%).

Conclusions

iPad perimetry using the visualFields Easy application had inadequate diagnostic accuracy to be used as a screening tool for glaucoma in this South Indian population.

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Fig. 1: Study flow.
Fig. 2: Correlation between visualFields Easy and Humphrey Field Analyser (HFA).
Fig. 3: Receiver operating characteristic (ROC) curves for detection of visual field defects.
Fig. 4: Receiver operating characteristic (ROC) curves for detection of glaucoma.

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Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The authors thank Phani Kishore for logistical and technological support during the study.

Funding

This work was supported by the Fortisure Foundation, the National Institutes of Health (grant UG1EY028097), Research to Prevent Blindness (Medical Student Eye Research Fellowship), and That Man May See.

Author information

Author notes

  1. These authors contributed equally: Quintin R Richardson, Rajesh S Kumar.

Authors and Affiliations

  1. Francis I. Proctor Foundation, University of California, San Francisco, CA, USA

    Quintin R. Richardson, Caitlin A. Moe, Dionna M. Wittberg, Kieran S. O’Brien & Jeremy D. Keenan

  2. Narayana Nethralaya Eye Hospital, Bangalore, India

    Rajesh S. Kumar, B. Ramgopal, Mahalakshmi V. Rackenchath, Sathi Devi A. V., Suria S. Mannil & Sriharsha Nagaraj

  3. Cleveland Clinic Abu Dhabi, Abu Dhabi, UAE

    Rajesh S. Kumar

  4. Byers Eye Institute, Stanford University School of Medicine, Palo Alto, CA, USA

    Suria S. Mannil

  5. Department of Ophthalmology, University of California, San Francisco, CA, USA

    Julius T. Oatts, Robert L. Stamper & Jeremy D. Keenan

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  1. Quintin R. Richardson
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Contributions

QRR performed statistical analysis, created figures and tables, and wrote the first draft of the paper. RSK designed the study, collected data, was responsible for implementation of the study and interpreted the results. BR, MVR, SDA, SSM, and SN collected data. CAM, DMW, and KSO helped implement the study. JTO interpreted the results. RLS procured funding, conceived of the study, designed the study, collected data, and interpreted the results. JDK designed and helped implement the study, collected data, and interpreted the results. All co-authors interpreted and critically edited the paper.

Corresponding author

Correspondence to Jeremy D. Keenan.

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Competing interests

The authors declare no competing interests.

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Supplementary information

41433_2022_2223_MOESM1_ESM.docx

Supplemental Figure 1. Correlation between visualFields Easy and Humphrey Field Analyser (HFA) among eyes with reliable visualFields Easy results.

41433_2022_2223_MOESM2_ESM.docx

Supplemental Figure 2. Receiver operating characteristic (ROC) curves for detection of visual field defects among eyes with reliable visualFields Easy results.

41433_2022_2223_MOESM3_ESM.docx

Supplemental Figure 3. Receiver operating characteristic (ROC) curves for detection of eye-level glaucoma among eyes with reliable visualFields Easy results.

Supplemental Figure 4. Receiver operating characteristic (ROC) curves for detection of isolated glaucoma severities.

Supplemental Figure 5. Visual field printouts from Humphrey Field Analyser (HFA) and the visualFields Easy application.

Supplemental Table 1. Demographics of study participants and summary of visual field defects and glaucoma

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Richardson, Q.R., Kumar, R.S., Ramgopal, B. et al. Diagnostic accuracy of an iPad application for detection of visual field defects. Eye 37, 1690–1695 (2023). https://doi.org/10.1038/s41433-022-02223-y

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