Sampling the Visual Field Based on Individual Retinal Nerve Fiber Layer Thickness Profile.

Area of Science:

• Ophthalmology
• Medical Imaging
• Glaucoma Research

Background:

• Current visual field (VF) perimetry relies on fixed grid patterns.
• These fixed patterns may not optimally detect localized visual field defects, particularly in glaucoma.

Purpose of the Study:

• To evaluate if a visual field grid tailored to an individual's retinal nerve fiber layer (RNFL) thickness profile can detect more visual field defects compared to standard methods.
• To introduce and test a novel defect-based sampling strategy for visual field testing.

Main Methods:

• A defect-based method was developed, selecting 52 test locations based on high predictive value for glaucoma and RNFL thickness.
• Individualized maps relating visual field locations to peripapillary RNFL thickness were used.
• This method was compared against uniform, 24-2, polar, and reduced polar grid patterns using VF data from 23 glaucoma participants.

Main Results:

• The defect-based method identified significantly more abnormal visual field locations in 9 out of 15 eyes compared to chance.
• It outperformed standard sampling schemes, including the 24-2 pattern, polar, and uniform grids, in detecting defects.

Conclusions:

• Individualized visual field testing grids based on structural RNFL information are superior to existing fixed patterns.
• This approach enhances the detection of visual field abnormalities in glaucoma patients.