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      Incorporating Spatial Models in Visual Field Test Procedures

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          Abstract

          Purpose

          To introduce a perimetric algorithm (Spatially Weighted Likelihoods in Zippy Estimation by Sequential Testing [ZEST] [SWeLZ]) that uses spatial information on every presentation to alter visual field (VF) estimates, to reduce test times without affecting output precision and accuracy.

          Methods

          SWeLZ is a maximum likelihood Bayesian procedure, which updates probability mass functions at VF locations using a spatial model. Spatial models were created from empirical data, computational models, nearest neighbor, random relationships, and interconnecting all locations. SWeLZ was compared to an implementation of the ZEST algorithm for perimetry using computer simulations on 163 glaucomatous and 233 normal VFs (Humphrey Field Analyzer 24-2). Output measures included number of presentations and visual sensitivity estimates.

          Results

          There was no significant difference in accuracy or precision of SWeLZ for the different spatial models relative to ZEST, either when collated across whole fields or when split by input sensitivity. Inspection of VF maps showed that SWeLZ was able to detect localized VF loss. SWeLZ was faster than ZEST for normal VFs: median number of presentations reduced by 20% to 38%. The number of presentations was equivalent for SWeLZ and ZEST when simulated on glaucomatous VFs.

          Conclusions

          SWeLZ has the potential to reduce VF test times in people with normal VFs, without detriment to output precision and accuracy in glaucomatous VFs.

          Translational Relevance

          SWeLZ is a novel perimetric algorithm. Simulations show that SWeLZ can reduce the number of test presentations for people with normal VFs. Since many patients have normal fields, this has the potential for significant time savings in clinical settings.

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          Most cited references 44

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          Mapping the visual field to the optic disc in normal tension glaucoma eyes.

          To establish the anatomical relationship between visual field test points in the Humphrey 24-2 test pattern and regions of the optic nerve head (ONH) DESIGN: Cross-sectional study. Glaucoma patients and suspects from the Normal Tension Glaucoma Clinic at Moorfields Eye Hospital. Sixty-nine retinal nerve fiber layer (RNFL) photographs with well-defined RNFL defects and/or prominent bundles were digitized. An appropriately scaled Humphrey 24-2 visual field grid and an ONH reference circle, divided into 30 degrees sectors, were generated digitally. These were superimposed onto the RNFL images. The relationship of visual field test points to the circumference of the ONH was estimated by noting the proximity of test points to RNFL defects and/or prominent bundles. The position of the ONH in relation to the fovea was also noted. The sector at the ONH corresponding to each visual field test point, the position of the ONH in relation to the fovea, and the effect of the latter on the former. A median 22 (range, 4-58), of a possible 69, ONH positions were assigned to each visual field test point. The standard deviation of estimations was 7.2 degrees. The position of the ONH was 15.5 degrees (standard deviation 0.9 degrees ) nasal and 1.9 degrees (standard deviation 1.0 degrees ) above the fovea. The location of the ONH had a significant effect on the corresponding position at the ONH for 28 of 52 visual field test points. A clinically useful map that relates visual field test points to regions of the ONH has been produced. The map will aid clinical evaluation of glaucoma patients and suspects, as well as form the basis for investigations of the relationship between retinal light sensitivity and ONH structure.
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            Efficient and unbiased modifications of the QUEST threshold method: theory, simulations, experimental evaluation and practical implementation.

            QUEST [Watson and Pelli, Perception and Psychophysics, 13, 113-120 (1983)] is an efficient method of measuring thresholds which is based on three steps: (1) Specification of prior knowledge and assumptions, including an initial probability density function (p.d.f.) of threshold (i.e. relative probability of different thresholds in the population). (2) A method for choosing the stimulus intensity of any trial. (3) A method for choosing the final threshold estimate. QUEST introduced a Bayesian framework for combining prior knowledge with the results of previous trials to calculate a current p.d.f.; this is then used to implement Steps 2 and 3. While maintaining this Bayesian approach, this paper evaluates whether modifications of the QUEST method (particularly Step 2, but also Steps 1 and 3) can lead to greater precision and reduced bias. Four variations of the QUEST method (differing in Step 2) were evaluated by computer simulations. In addition to the standard method of setting the stimulus intensity to the mode of the current p.d.f. of threshold, the alternatives of using the mean and the median were evaluated. In the fourth variation--the Minimum Variance Method--the next stimulus intensity is chosen to minimize the expected variance at the end of the next trial. An exact enumeration technique with up to 20 trials was used for both yes-no and two-alternative forced-choice (2AFC) experiments. In all cases, using the mean (here called ZEST) provided better precision than using the median which in turn was better than using the mode. The Minimum Variance Method provided slightly better precision than ZEST. The usual threshold criterion--based on the "ideal sweat factor"--may not provide optimum precision; efficiency can generally be improved by optimizing the threshold criterion. We therefore recommend either using ZEST with the optimum threshold criterion or the more complex Minimum Variance Method. A distinction is made between "measurement bias", which is derived from the mean of repeated threshold estimates for a single real threshold, and "interpretation bias", which is derived from the mean of real thresholds yielding a single threshold estimate. If their assumptions are correct, the current methods have no interpretation bias, but they do have measurement bias. Interpretation bias caused by errors in the assumptions used by ZEST is evaluated. The precisions and merits of yes-no and 2AFC techniques are compared.(ABSTRACT TRUNCATED AT 400 WORDS)
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              A new generation of algorithms for computerized threshold perimetry, SITA.

              The purpose of this work was to develop a new family of test algorithms for computerized static threshold perimetry which significantly reduces test time without any reduction of data quality. A comprehensive visual field model constructed from available knowledge of normal and glaucomatous visual fields is continuously updated during testing. The model produces threshold estimates and also estimates of the certainty to which the threshold is known at each point. Testing is interrupted at each test location at predetermined levels of threshold certainty. New time-saving methods are employed for estimation of false answers, and test pacing is optimized. After completion of the test, all threshold estimates are re-computed, taking into account the complete body of patient responses. Computer simulations were used to optimize the different parameters of the new algorithms, to evaluate the relative importance of those parameters, and to evaluate the performance of the algorithm as a whole in comparison with a standard algorithm. Simulated test results obtained with this algorithm were slightly more accurate than those of the Humphrey Full Threshold test algorithm. The number of simulated stimuli presented was reduced by an average of 29% in normal fields and 26% in glaucomatous fields. Actual clinical test time should be further reduced, since the influence of the improved timing algorithm was not included in the simulations. We applied new methods which take available knowledge of visual field physiology and pathophysiology into account, and employ modern computer-intensive mathematical methods for real time estimates of threshold values and threshold error estimates. In this way it was possible to design a family of testing algorithms which significantly reduced perimetric test time without any loss of quality in results.
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                Author and article information

                Journal
                Transl Vis Sci Technol
                Transl Vis Sci Technol
                tvst
                tvst
                tvst
                Translational Vision Science & Technology
                The Association for Research in Vision and Ophthalmology
                2164-2591
                11 March 2016
                March 2016
                : 5
                : 2
                Affiliations
                [1 ]Department of Optometry and Vision Sciences The University of Melbourne, Melbourne, Australia
                [2 ]Computing and Information Systems, The University of Melbourne, Melbourne, Australia
                Author notes
                Correspondence: Andrew Turpin, Computing and Information Systems, The University of Melbourne, Parkville, 3010, Victoria, Australia. e-mail: aturpin@ 123456unimelb.edu.au
                Article
                tvst-05-02-09 MS#: TVST-15-0254
                10.1167/tvst.5.2.7
                4790418
                26981329

                This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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                perimetry, visual field, glaucoma

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