May 2008
Volume 49, Issue 13
Free
ARVO Annual Meeting Abstract  |   May 2008
Smooth Estimation of Visual Field Loss for Predicting Function
Author Affiliations & Notes
  • L. L. Renninger
    Smith-Kettlewell Eye Research Institute, San Francisco, California
  • C. Psomadakis
    Smith-Kettlewell Eye Research Institute, San Francisco, California
  • L. Dang
    Smith-Kettlewell Eye Research Institute, San Francisco, California
  • D. Fletcher
    Smith-Kettlewell Eye Research Institute, San Francisco, California
  • Footnotes
    Commercial Relationships  L.L. Renninger, None; C. Psomadakis, None; L. Dang, None; D. Fletcher, None.
  • Footnotes
    Support  NIH R01 EY018004; Pacific Vision Foundation
Investigative Ophthalmology & Visual Science May 2008, Vol.49, 1508. doi:https://doi.org/
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    • Get Citation

      L. L. Renninger, C. Psomadakis, L. Dang, D. Fletcher; Smooth Estimation of Visual Field Loss for Predicting Function. Invest. Ophthalmol. Vis. Sci. 2008;49(13):1508. doi: https://doi.org/.

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

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Abstract
 
Purpose:
 

It is not well understood how the profile of central field loss due to macular degeneration leads to differential difficulties in activities of daily living. The coarse nature of clinical field assessment may contribute variability to the data, obscuring potential links between field loss and functional losses. In this work, we develop an algorithm to generate field maps from which scotoma metrics can be computed.

 
Methods:
 

Microperimetry was performed on low vision patients using a scanning laser ophthalmoscope (SLO). Continuous scotoma maps (SM) were estimated from this discrete data using a weighted k-nearest neighbor classification algorithm (figure). The weights are varied as a function of eccentricity from the fovea, whose location is defined relative to the optic disk. The preferred retinal locus (PRL) was taken as the location of the fixation cross during microperimetry. Scotoma metrics were estimated including: distance from fovea to PRL, total area and location of scotoma relative to PRL, and shape/symmetry of scotoma. The metrics were validated against a clinical assessment. Correlations were computed between scotoma metrics and functional tests such as acuity, fixation stability and reading rates.

 
Results:
 

The distance from the foveal location to the PRL was correlated with acuity (0.57), however not with scotoma area, suggesting that scotoma are not typically symmetric about the fovea. Accordingly, no significant correlation was found between scotoma area and acuity. Previous results (Fletcher & Schuchard, 1999) found that acuity was a weak predictor of reading rates (-0.47 in this data). Scotoma area was a slightly stronger predictor (r=-0.54).

 
Conclusions:
 

The correlation of acuity and scotoma area with reading rates is expected, however the lack of correlation between these two measures suggests they may affect reading behavior through different mechanisms. In related work (Renninger, et. al., VSS 2008), the efficiency of eye movements in a shape discrimination task were drastically affected as scotoma area increased. While poor acuity may hinder letter and word recognition, large scotomas likely hinder saccadic targeting of the text.  

 
Keywords: imaging/image analysis: clinical • low vision • quality of life 
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