Static Perimetry: Accuracy and Fluctuations

Bebie, Hans; Fankhauser, Franz; Spahr, Jürg

doi:10.1111/j.1755-3768.1976.tb01263.x

Cited by 116 publications

(18 citation statements)

References 7 publications

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“…When introducing probabilities deduced from a frequency-of-seeing curve for untrained subjects (Greve 1973) in computer simulations, the resulting dispersion was found to be close to the mean dispersion obtained in the normal visual fields in this study. The dispersions of this study were of the same magnitude as those found by Bebie et al (1976) and those measured earlier by us (Heijl & Krakau 1975a). The dispersion represents a real variation in the results of the threshold estimation and are not due to a change of the mean threshold -a general constriction or widening of the visual field -between the test sessions.…”

Section: Discussionsupporting

confidence: 78%

Computer Test Logics for Automatic Perimetry

Heijl

1977

Acta Ophthalmologica

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Using an automatic computerized perimeter developed by Heijl & Krakau (1975b) three different perimetric test logics, one simple (I) and two more complicated and time-consuming (II & III) were investigated in practical experiments on healthy normal test subjects and patients and in computer simulated tests. The patients either had a verified diagnosis of glaucoma or glaucoma was suspected. The best consistency in measured thresholds was obtained with test logic II, in which an averaging procedure is used. The variation of the results was larger in pathological than in normal visual fields. All test logics investigated readily detected the pathological field defects, but blind spots could easier pass unrecognized with the simplest logic than with the other two logics. The conclusion is drawn that a simple test logic can be used for perimetry in glaucoma suspects if no visual field defect has previously been documented. For the follow-up of pathological fields a fairly complicated test logic, e.g. using averaging, is preferable.

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Section: Discussionsupporting

confidence: 78%

Computer Test Logics for Automatic Perimetry

Heijl

1977

Acta Ophthalmologica

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“…These define short-term fluctuation (SF) and long-term fluctuation (LF) as the components of variability in automated static perimetry. 29 …”

Section: Discussionmentioning

confidence: 99%

Comparing the Nidek MP-1 and Humphrey Field Analyzer in Normal Subjects

Acton¹,

Bartlett²,

Greenstein³

2011

Optometry and Vision Science

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Purpose To compare visual fields on the Nidek MP-1 to those obtained on the Humphrey field analyzer (HFA) in healthy volunteers and assess the effects of differences in stimulus parameters and testing strategies that may influence the interpretation of results in patients. A secondary aim was to establish MP-1 normative data to calculate the total deviation analyses and global indices analogous to those used by the HFA. Methods 50 healthy volunteers (age 43.5 ± 13.9 yrs, range 18–68 yrs) underwent repeat MP-1 and HFA visual field testing, using the 10-2 pattern. MP-1 data were converted to HFA equivalent dB units. Between instrument comparisons of HFA and MP-1 sensitivities, regression of sensitivity with age, and examination duration were assessed. Test-retest variability was examined between visits. Results MP-1 (mean = 32.82 dB, SD = 1.92 dB) and HFA sensitivities (mean = 32.84 dB, SD = 1.83 dB) were not significantly different (p = 0.759). SD values for the HFA (range 1.11–3.30 dB) were similar to the MP-1 (range 0.14–2.75 dB). However asymmetry comparisons between instruments showed significantly decreased superior rather than inferior retinal values for the MP-1. There was a small but significant difference (p = 0.004) in mean test duration between the MP-1 (mean = 6:11 min, SD = 1:49 min) and the HFA (mean = 5:14 min, SD = 0:42 min). There was also a difference in the decline of mean sensitivity with age, a decline of 0.1 and 0.4dB per decade was noted in MP-1 and HFA sensitivity respectively. Test-retest variability was similar between instruments. A small but non-significant increase in mean sensitivity at the second visit for both the MP-1 (p = 0.060) and HFA (p = 0.570) was found. Conclusions Both instruments showed similar variability and test-retest variability when results were compared using equivalent units. However there are important differences in sensitivity values, stimulus parameters and testing strategies that have to be taken into account when comparisons are made.

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“…Left eye values were inverted to right eye values and the results from both eyes were combined for this report. The components of fluctuation were calculated from a statistical random three-fold nested model according to the number of repetitions nested in locations, nested in sessions, nested in individuals in this study (Bebie et al 1976;Graybill 1961). This model defines four components of fluctuation: The short-term component describes the fluctuation which occurs within a single visual field exam.…”

Section: Methodsmentioning

confidence: 99%

Threshold variability using different Goldmann stimulus sizes

Gilpin

Stewart

Hunt

et al. 1990

Acta Ophthalmologica

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We tested 10 healthy subjects on the Humphrey Field Analyzer using Goldmann stimulus sizes I-V to determine the effect of varying the area of the stimulus upon threshold fluctuation. Our results show similar components of fluctuation for the size III stimulus as in past studies. However, an increased total fluctuation was observed for Goldmann stimulus sizes I (3.69 decibels) and II (3.17 decibels) and a similar fluctuation for stimulus sizes IV (2.64 decibels) and V (2.51 decibels) as compared to stimulus size III (2.52 decibels). The study suggests no advantage results in reduced threshold fluctuation by changing the Goldmann stimulus from a size III when testing normal individuals on the Humphrey Field Analyzer.

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Static Perimetry: Accuracy and Fluctuations

Cited by 116 publications

References 7 publications

Computer Test Logics for Automatic Perimetry

Computer Test Logics for Automatic Perimetry

Comparing the Nidek MP-1 and Humphrey Field Analyzer in Normal Subjects

Threshold variability using different Goldmann stimulus sizes

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