June 2013
Volume 54, Issue 15
Free
ARVO Annual Meeting Abstract  |   June 2013
Comparing Visual Thresholds Measured with Manual Perimetry and Eye-Movement Perimetry
Author Affiliations & Notes
  • David Warren
    Department of Neurology, University of Iowa, Iowa City, IA
  • Alice Xu
    Department of Ophthalmology, University of Iowa, Iowa City, IA
  • Andrew Papendieck
    Department of Neurology, University of Iowa, Iowa City, IA
    Department of Ophthalmology, University of Iowa, Iowa City, IA
  • Matthew Thurtell
    Department of Neurology, University of Iowa, Iowa City, IA
    Department of Ophthalmology, University of Iowa, Iowa City, IA
  • Michael Wall
    Department of Neurology, University of Iowa, Iowa City, IA
    Department of Ophthalmology, University of Iowa, Iowa City, IA
  • Footnotes
    Commercial Relationships David Warren, None; Alice Xu, None; Andrew Papendieck, None; Matthew Thurtell, None; Michael Wall, None
  • Footnotes
    Support None
Investigative Ophthalmology & Visual Science June 2013, Vol.54, 189. doi:
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    • Get Citation

      David Warren, Alice Xu, Andrew Papendieck, Matthew Thurtell, Michael Wall; Comparing Visual Thresholds Measured with Manual Perimetry and Eye-Movement Perimetry. Invest. Ophthalmol. Vis. Sci. 2013;54(15):189.

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

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Abstract
 
Purpose
 

We aimed to test the hypothesis that automated eye-movement perimetry can yield visual threshold estimates that are similar to threshold estimates obtained using automated manual perimetry.

 
Methods
 

We tested 27 normal subjects with two perimetry conditions: manual, in which responses were made with a computer mouse; and eye-movement, in which responses were made by moving the eyes to the stimulus. Eye movements (monocular, right eye) were recorded with an EyeLink1000 infrared camera. System accuracy measuring the horizontal visual field was <0.5°, with a temporal resolution of 1000 Hz. Our analysis included responses made to 3 stimulus positions spread across the visual field (3°,3°; 3°,-9°; and -21°,-3°), and we used correlation measures to compare visual threshold data from the manual and eye-movement tasks for each stimulus position within participants. We manipulated target salience by varying stimulus size across a continuous range.

 
Results
 

The accompanying figure shows estimates of visual threshold obtained from manual perimetry (ordinate) against estimates obtained from eye-movement perimetry (abscissa) for each test location (one per panel). A strong and statistically significant positive correlation between manual and eye-movement thresholds was observed at each test location (3°,3°: r = 0.94; 3°,-9°: r = 0.70; and -21°,-3°: r = 0.76; each p < 0.001). On average, threshold estimates based on manual perimetry were numerically smaller than estimates based on eye-movement perimetry (mean differences [SD] at 3°,3°: 0.16 dB [0.41]; 3°,-9°: 0.21 dB [0.64]; and -21°,-3°: 0.03 dB [0.46]), but this difference was only reliable at one target location (3°,-9°: T(21) = 4.45, p < 0.001). Furthermore, all differences were within the expected range of test-retest reliability for perimetry testing (i.e., 1 dB).

 
Conclusions
 

Manual and eye-movement perimetry produced similar estimates of visual threshold in normal subjects, supporting our hypothesis that automated eye-movement perimetry can provide a valid measure of visual function. Further refinement of this technique will provide a tool permitting automated, whole-field perimetry testing, along with novel, eye-movement based measures (eye movement latency and accuracy) that reflect the health of the visual system.

  
Keywords: 525 eye movements: saccades and pursuits • 758 visual fields • 642 perimetry  
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