March 2012
Volume 53, Issue 14
Free
ARVO Annual Meeting Abstract  |   March 2012
Using Structural Image Data to Seed a Perimetric Test Strategy for Glaucoma
Author Affiliations & Notes
  • Jonathan Denniss
    Optometry & Vision Sciences,
    Computing & Information Systems,
    The University of Melbourne, Melbourne, Australia
  • Allison M. McKendrick
    Optometry & Vision Sciences,
    The University of Melbourne, Melbourne, Australia
  • Andrew Turpin
    Optometry & Vision Sciences,
    Computing & Information Systems,
    The University of Melbourne, Melbourne, Australia
  • Footnotes
    Commercial Relationships  Jonathan Denniss, Heidelberg Engineering GmbH (F); Allison M. McKendrick, Heidelberg Engineering GmbH (F); Andrew Turpin, Heidelberg Engineering GmbH (F)
  • Footnotes
    Support  ARC Linkage Project LP100100250; ARC Future Fellowship FT0990930 (AMM); ARC Future Fellowship FT0991326 (AT)
Investigative Ophthalmology & Visual Science March 2012, Vol.53, 699. doi:
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      Jonathan Denniss, Allison M. McKendrick, Andrew Turpin; Using Structural Image Data to Seed a Perimetric Test Strategy for Glaucoma. Invest. Ophthalmol. Vis. Sci. 2012;53(14):699.

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

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Abstract

Purpose: : To establish bounds on the accuracy of visual sensitivity predictions from structural imaging that would reduce measurement error and test duration of perimetric procedures, and to establish if current structure-function relations fall within these bounds.

Methods: : Computer simulation was used to determine the error distribution and presentation count for Structure-ZEST (SZEST), a modified Zippy Estimation by Sequential Testing (ZEST) algorithm whose prior probability density function (PDF) depended upon the threshold prediction from a structural measure (SM). First the SM was assumed to perfectly predict true threshold, and various algorithm parameters were trialled to find the best trade-off between test accuracy and duration. Next the algorithm with best prior PDFs for SM predictions of 0, 10, 20, 30dB was applied to the full range of true thresholds. For these predictions, the range of true thresholds across which SZEST produced less error than ZEST was measured. A hypothetical structure-function relationship was then simulated using ZEST measurement error, with SM simulated to predict true threshold with just adequate precision for SZEST to outperform ZEST. Four response conditions were modelled [false positive rate, false negative rate]: [0%, 0%], [15%, 3%], [3%, 15%], [20%, 20%].

Results: : A Gaussian prior PDF centred on the SM prediction with standard deviation (SD) 5dB and termination criterion of PDF SD 1.5dB performed best for SZEST. When response errors were made, SZEST generally produced less error than ZEST when the SM prediction was within +/-9dB of true threshold (median 0.9dB reduction [IQR 0.3-1.4dB], p<0.001). With no response errors, ZEST and SZEST accuracy was similar. Across all response conditions, SZEST was faster than ZEST, making median 49% (IQR 45-54%, p<0.001) fewer presentations per damaged location. The simulated structure-function relationship that predicted true threshold to within +/-9dB had R-squared 0.31 once typical measurement error from the standard ZEST algorithm was incorporated.

Conclusions: : A perimetric procedure incorporating sensitivity predictions from structural measures (SZEST) typically makes more accurate and faster threshold estimations than ZEST when the SM predicts threshold within +/-9dB. This level of precision may be achievable with current clinical imaging tools.

Keywords: perimetry • visual fields 
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