Digital Quantification of Goldmann Visual Fields (GVFs) as a Means for Genotype–Phenotype Comparisons and Detection of Progression in Retinal Degenerations

Abstract

Purpose To develop a reliable and efficient digital method to quantify planimetric Goldmann visual field (GVF) data to monitor disease course and treatment responses in retinal degenerative diseases. Methods A novel method to digitally quantify GVF using Adobe Photoshop CS3 was developed for comparison to traditional digital planimetry (Placom 45C digital planimeter; EngineerSupply, Lynchburg, Virginia, USA). GVFs from 20 eyes from 10 patients with Stargardt disease were quantified to assess the difference between the two methods (a total of 230 measurements per method). This quantification approach was also applied to 13 patients with X-linked retinitis pigmentosa (XLRP) with mutations in RPGR. Results Overall, measurements using Adobe Photoshop were more rapidly performed than those using conventional planimetry. Photoshop measurements also exhibited less inter- and intra-observer variability. GVF areas for the I4e isopter in patients with the same mutation in RPGR who were nearby in age had similar qualitative and quantitative areas. Conclusions Quantification of GVF using Adobe Photoshop is quicker, more reliable, and less-user dependent than conventional digital planimetry. It will be a useful tool for both retrospective and prospective studies of disease course as well as for monitoring treatment response in clinical trials for retinal degenerative diseases.

Peripheral Visual Fields in ABCA4 Stargardt Disease and Correlation With Disease Extent on Ultra-widefield Fundus Autofluorescence

“…14,16,18,21,41 Using 30-degree FAF, Fujinami et al found that patients with type I were the second most prevalent, while Klufas et al and Zahid et al found it to be the least prevalent. 16,18,42 Although UWF-FAF and 30-degree FAF imaging are not necessarily comparable for types II and III, it is reasonable to assume that type I is the same in both classifications. Therefore, rather than being due to imaging type, this discrepancy may be due to the progressive nature of this condition.…”

Variability and Errors of Manually Digitized Goldmann Visual Fields

“…Non-seeing isopters were assigned negative area values in this study. It should be noted that in some previous publications, isopter groups have been referred to as isopters; 14,20,23,27,30–32,37 the two terms should not be confused.…”

“…Calculation of areas from manual Goldmann perimetry data has been addressed 16–23 and used 5,7,8,10,14,18,21,24–37 many times in the past several decades. Popular methods include the use of software that can analyze scanned images of GVFs (e.g.…”

“…Adobe Photoshop, 23,34,37 Engauge Digitizer, 32 GIMP, 27 JHU’s FieldDigitize, 7,10,24 MathWorks MATLAB, 14 NIH’s ImageJ, 18,31 Scion Image, 34,36 UCL’s Retinal Area Analysis Tool, 28 UTHSCSA ImageTool 29,33 ), or handheld devices to convey position information to computer software. 5,20,21,23,26 To our knowledge, however, none of these previous studies have explored the variability of calculated retinal areas or the error rates of digitizers. Here, the term “error” refers to an omission or erroneous classification of one isopter: mislabeling an isopter target size, mislabeling an isopter as seeing or non-seeing, or missing an isopter during digitization.…”

“…Here, the term “error” refers to an omission or erroneous classification of one isopter: mislabeling an isopter target size, mislabeling an isopter as seeing or non-seeing, or missing an isopter during digitization. Zahid et al 23 did recently examine inter- and intra-digitizer variabilities for two digitizing techniques, but neither of these techniques tracked errors in processing isopters or translated planimetric map areas to retinal surface areas, which requires calibration for each GVF.…”