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M. V. Boland, H. A. Quigley, H. Danesh-Meyer; Development and Evaluation of a Neurological Hemifield Test. Invest. Ophthalmol. Vis. Sci. 2008;49(13):1071.
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Algorithms for assessing neurological visual field defects similar to the glaucoma hemifield test are not available and might detect field loss associated with neurological disease. For this reason, we developed and tested a neurological hemifield test (NHT).
Using a scoring system similar to the glaucoma hemifield test, we converted each point in the 24-2 pattern of the Humphrey Field Analyzer 2 to a number that was inversely proportional to the pattern deviation probability: p > 0.1 = 0, 0.1 > p >= 0.05 = 2, 0.05 > p >= 0.01 = 5, and 0.01 > p = 10. Field points were grouped into two regions on either side of the vertical meridian (e.g., 1 and 2 in the Figure). We tested a number of point groupings. Some had both superior and inferior regions (two above and below the horizontal meridian) and some had only one region on either side of the vertical. All patterns avoided points commonly affected by the physiologic blind spot (gray points in the Figure). The NHT score was the difference between the sum of the point scores in the right and left subfields. We did not attempt to combine data from both eyes in this phase of the project. NHT scores were calculated for visual fields from 59 subjects (115 eyes) with neurological lesions collected at a neuro-ophthalmology practice. Lesions included stroke (10 eyes), pituitary masses (59 eyes) and other compressive lesions (46 eyes). Visual fields from 96 subjects (156 eyes) without neurological disease seen in a cataract practice were used as controls. The ability of the NHT to distinguish neurological fields from controls was assessed using receiver operating characteristic (ROC) curve analysis.
The area under the ROC curve varied between 0.62 and 0.87 depending on the pattern of points used to calculate the NHT score. Superior subfields were better at discriminating than inferior subfields when using patterns that were split both along the horizontal and vertical meridians. The pattern with the best ROC performance is shown in the Figure.
Automated analysis of visual fields can distinguish patients with neurological disease from controls.
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