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A clinical study of perimetric probability maps.

A Heijl¹, P Asman

¹Department of Ophthalmology, University of Lund, Malmö General Hospital, Sweden.

Archives of Ophthalmology (Chicago, Ill. : 1960)

|February 1, 1989

Summary

Choosing the right normal visual field model is crucial for accurate perimetric probability maps. An empirically derived model significantly improves the detection of glaucoma compared to a standard Gaussian model.

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Area of Science:

Ophthalmology
Visual field testing

Background:

Perimetric probability maps are essential for interpreting visual field test results.
These maps compare patient findings to a normal population's expected visual field data.

Purpose of the Study:

To evaluate the clinical impact of different normal visual field models on perimetric probability maps.
To determine which model provides better discrimination between normal and glaucomatous visual fields.

Main Methods:

Two models of the normal visual field were used: a Gaussian model with constant variability and an empirically determined, non-Gaussian, location-dependent model.
Probability maps were generated for 41 normal subjects and 46 glaucoma patients.

Main Results:

The empirically derived model demonstrated superior separation between normal and glaucomatous eyes.
This model also resulted in a number of significant points in normal subjects that more closely matched theoretical expectations.
The Gaussian model produced an unacceptably high rate of false positives in normal subjects, especially in the mid-peripheral visual field.

Conclusions:

The clinical utility of perimetric probability maps is highly dependent on the selection of an appropriate normal visual field model.
Empirically derived models offer improved diagnostic accuracy in glaucoma detection.

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